# **MAGNETIC RESONANCE IMAGING OF HEALTHY AND DISEASED BRAIN NETWORKS**

**Topic Editors Yong He and Alan Evans**

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**ISSN** 1664-8714 **ISBN** 978-2-88919-435-3 **DOI** 10.3389/978-2-88919-435-3

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## **MAGNETIC RESONANCE IMAGING OF HEALTHY AND DISEASED BRAIN NETWORKS**

Topic Editors:

**Yong He, Ph.D.,** Beijing Normal University, China **Alan Evans, Ph.D.,** McGill University, Canada

Cover Legend: The brain network includes nodes representing brain regions and edges representing connections linking regions. This figure was visualized by using the BrainNet Viewer software (www.nitrc.org/projects/bnv).

An important aspect of neuroscience is to characterize the underlying connectivity patterns of the human brain (i.e., human connectomics). Over the past few years, researchers have demonstrated that by combining a variety of different neuroimaging technologies (e.g., structural MRI, diffusion MRI and functional MRI) with sophisticated analytic strategies such as graph theory, it is possible to noninvasively map the patterns of structural and functional connectivity of human whole-brain networks. With these novel approaches, many studies have shown that human brain networks have nonrandom properties such as modularity, small-worldness and highly connected hubs. Importantly, these quantifiable

network properties change with age, learning and disease. Moreover, there is growing evidence for behavioral and genetic correlates. Network analysis of neuroimaging data is opening up a new avenue of research into the understanding of the organizational principles of the brain that will be of interest for all basic scientists and clinical researchers. Such approaches are powerful but there are a number of challenging issues when extracting reliable brain networks from various imaging modalities and analyzing the topological properties, e.g., definitions of network nodes and edges and reproducibility of network analysis. We assembled contributions related to the state-of-the-art methodologies of brain connectivity and the applications involving development, aging and neuropsychiatric disorders such as Alzheimer's disease, schizophrenia, attention deficit hyperactivity disorder and mood and anxiety disorders. It is anticipated that the articles in this Research Topic will provide a greater range and depth of provision for the field of imaging connectomics.

# Table of Contents


Wanqing Li, Xiaoqin Mai and Chao Liu


Angelica De La Fuente, Shugao Xia, Craig Branch and Xiaobo Li


Diana M. E. Torta, Tommaso Costa, Sergio Duca, Peter T. Fox and Franco Cauda


Chao-Gan Yan, R. Cameron Craddock, Yong He and Michael P. Milham


*150 Task vs. Rest—Different Network Configurations Between the Coactivation and the Resting-State Brain Networks*

Xin Di, Suril Gohel, Eun H. Kim and Bharat B. Biswal


Qing Gao, Qiang Xu, Xujun Duan, Wei Liao, Jurong Ding, Zhiqiang Zhang, Yuan Li, Guangming Lu and Huafu Chen

*187 Erratum: Extraversion and Neuroticism Relate to Topological Properties of Resting-State Brain Networks*

Qing Gao


Zhang Chen, Min Liu, Donald W. Gross and Christian Beaulieu


Riikka Rytty, Juha Nikkinen, Liisa Paavola, Ahmed Abou Elseoud, Virpi Moilanen, Annina Visuri, Osmo Tervonen, Alan E. Renton, Bryan J. Traynor, Vesa Kiviniemi and Anne M. Remes


Anselm Doll, Christian Sorg, Andrei Manoliu, Andreas Wöller, Chun Meng, Hans Förstl, Claus Zimmer, Afra M. Wohlschläger and Valentin Riedl

## *310 Insular Dysfunction within the Salience Network is Associated with Severity of Symptoms and Aberrant Inter-Network Connectivity on Major Depressive Disorder*

Andrei Manoliu, Chun Meng, Felix Brandl, Anselm Doll, Masoud Tahmasian, Martin Scherr, Dirk Schwerthöffer, Claus Zimmer, Hans Förstl, Josef Bäuml, Valentin Riedl, Afra M. Wohlschläger and Christian Sorg

*327 Disparity Between Dorsal and Ventral Networks in Patients with Obsessive-Compulsive Disorder: Evidence Revealed by Graph Theoretical Analysis Based on Cortical Thickness from MRI*

Seung-Goo Kim, Wi Hoon Jung, Sung Nyun Kim, Joon Hwan Jang and Jun Soo Kwon

*345 Topological Correlations of Structural and Functional Networks in Patients with Traumatic Brain Injury*

Karen Caeyenberghs, Alexander Leemans, Inge Leunissen, Karla Michiels and Stephan P. Swinnen

*356 Abnormalities of Functional Brain Networks in Pathological Gambling: A Graph-Theoretical Approach*

Melanie Tschernegg, Julia S. Crone, Tina Eigenberger, Philipp Schwartenbeck, Mira Fauth-Bühler, Tagrid Leménager, Karl Mann, Natasha Thon, Friedrich M. Wurst and Martin Kronbichler

## Magnetic resonance imaging of healthy and diseased brain networks

## *Yong He1,2\* and Alan Evans <sup>3</sup>*

*<sup>1</sup> State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China*

*<sup>2</sup> Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China*

*<sup>3</sup> McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, Montreal, QC, Canada*

*\*Correspondence: yong.he@bnu.edu.cn*

#### *Edited and reviewed by:*

*John J. Foxe, Albert Einstein College of Medicine, USA*

**Keywords: connectomics, connectivity, graph theory, small-world, MRI**

An important aspect of neuroscience is to characterize the underlying connectivity patterns of the human brain. Recent advances in magnetic resonance imaging (MRI) techniques (e.g., structural MRI, diffusion MRI, and functional MRI) and network analysis approaches such as graph theory have allowed to investigate the patterns of structural and functional connectivity of human brain (i.e., human connectome, Sporns et al., 2005). Using imaging connectomics, many studies have demonstrated that large-scale human brain networks have many non-trivial topological properties such as small-worldness, modular structure, and highly connected hubs. Moreover, these quantifiable network properties significantly correlate with behavioral, environmental, and genetic factors, and change with age, learning, and disease. Network analysis of neuroimaging data is opening up a new avenue of research into the understanding of the organizational principles of the brain that will be crucial for all basic scientists and clinical researchers. Such approaches are fascinating but there are a number of challenging issues in the extraction of reliable brain networks from various imaging modalities and the analyses of the topological properties, such as the definitions of network nodes and edges, and the reproducibility of the network analysis results. Nonetheless, the field of imaging connectomics has significantly advanced in the past several years, largely due to the rapid development of network models, tools and methodologies, and their widespread applications in cognitive and clinical research.

In this research topic, we aimed at compiling works representing the state of the art in structural and functional brain networks in healthy and disease populations using neuroimaging data. We collected 29 articles that were from a number of internationally recognized scientists who have made significant contributions to this field. The article types are diverse and the topics covered various research directions including imaging techniques, computational modeling, network analysis approaches and tools, and applications.

(i) We assembled one hypothesis and theory article, one perspective article, and four review articles. In the hypothesis and theory article, Horwitz et al. (2013) highlighted recent efforts toward using large-scale neural modeling to explore the relationship between structural connectivity and functional/effective connectivity. Structural connectivity and functional/effective connectivity are the most fundamental concepts for the descriptions of brain networks, but their relationship is elusive. Horwitz et al. (2013) emphasized that the alteration of structural connectivity known in models does not necessarily result in matching changes in functional/effective connectivity and vice versa, suggesting that caution should be taken in the result interpretation of structural-functional connectivity relationship. Upon summarizing three commonly used strategies of imaging connectomics as biomarkers of brain diseases, Kaiser (2013) presented a novel fourth option for future disease biomarker studies, i.e., dynamic connectomes that use computational models of simulated brain activity based on structural connectivity rather than the structural connectome itself. Among the four review articles, Li et al. (2014) summarized brain connectivity studies related to the default-mode network (DMN) in the fields of social understanding: emotion perception, empathy, theory of mind, and morality, and suggested the vital roles of the DMN in this domain; Hoff et al. (2014) reviewed resting fMRI studies representing developmental changes in the functional brain networks from 20 weeks of gestation onwards, highlighting different developmental rates of network connectivity in different brain systems; De La Fuente et al. (2013) provided a systematic review regarding brain connectivity studies in attentiondeficit/hyperactivity disorder and proposed that the roles of the subcortical structures and their structural/functional pathways in this developmental disorder should be further studied; Bernhardt et al. (2013) reviewed the application of multi-modal imaging techniques and brain connectivity approaches in temporal lobe epilepsy, specifically highlighting findings from graph-theoretical analysis that assessed the topological organization of brain networks. Together, these articles suggest that the combination of multi-modal imaging techniques and advanced network analysis approaches such as computational modeling and graph theory provides unique opportunities to enrich our understanding of biological mechanisms in healthy and diseased brains. In these articles, a number of important research directions were proposed for future brain network studies based on neuroimaging data.

(ii) We assembled 23 original research articles, which can be roughly classified into imaging and analysis methodologies, tools, and applications in various domains.

## **METHODOLOGIES AND TOOLS**

One compelling imaging technique study done by Mandl et al. (2013) showed that functional diffusion tensor imaging (fDTI) method is capable to robustly detect neuronal activity of human brain networks within a practically feasible time period. Using an interesting meta-analytic clustering (MaC) approach, Torta et al. (2013) found that the cingulate cortex, an important brain hub, can be parcellated into three clusters, and that the clustering pattern of this region changes across different levels of task complexity. Importantly, two resting fMRI studies by Hayasaka (2013) and Yan et al. (2013) performed comprehensive validation analyses on the effects of global signal and head motion on brain network analysis and highlighted remarkable differences between the networks with or without these corrections. Another resting fMRI study by Kollndorfer et al. (2013) showed the reproducibility of functional connectivity patterns in four frequently used scanning conditions (i.e., fixation of a black crosshair on a white screen; fixation of the center of a black screen; eyes-closed and fixation of the words "relax"), suggesting that intrinsic brain connectivity measurements are reliable across these conditions and confirmed its potential in assessing brain networks in clinical settings. Lastly, Cui et al. (2013) developed a novel MATLAB toolbox named "Pipeline for Analyzing braiN Diffusion imAges" (PANDA) for fully automated processing of diffusion MRI data of the human brain, which substantially simplifies the image processing and facilitates imaging connectome studies.

#### **TASK MODULATION AND INDIVIDUAL DIFFERENCES**

Three papers utilized network analysis to study task-related modulation and individual differences. An important issue in connectomics is to understand how brain networks measured during resting state are reorganized by various task performances. Di et al. (2013) addressed this issue by studying meta-analytic coactivation patterns among regions based upon published neuroimaging studies and compared with those derived from resting fMRI data. They observed that the coactivation network showed greater global efficiency, smaller clustering coefficient, and lower modularity than the resting-state network, indicating a more efficient global information transmission during task performing. These findings highlighted topological reconfiguration of large-scale brain networks between task and resting-state conditions. Using cortical thickness covariance analysis of structural MRI data, Krishnadas et al. (2013) examined the association between neighborhood level deprivation and brain network structure, and found that the most deprived group showed modular patterns different from the least deprived group. These results provide preliminary evidence that the structural networks of the human brain might be associated with socioeconomic deprivation. Another interesting fMRI study done by Gao et al. (2013) demonstrated the association between the topological organization of whole-brain functional networks and individual differences such as extraversion and neuroticism.

## **NORMAL DEVELOPMENT AND AGING**

Four papers directly examined age-related changes in structural brain networks. Using DTI, Mishra et al. (2013) investigated the relationship between 10 major white matter tracts with distinctive functions in neonates and children around puberty: Stronger microstructural inter-tract correlations were observed during development from birth to puberty, indicating heterogeneous but organized myelination processes. Using DTI data of a large sample (*n* = 180), Chen et al. (2013b)specifically examined the topological organization of white matter networks in typically-developing participants, including early childhood (6.0–9.7 years), late childhood (9.8–12.7 years), adolescence (12.9–17.5 years), young adult (17.6–21.8 years), and adult (21.9–29.6 years). They showed that most prominent changes in the topological efficiencies of developing brain networks occur at late childhood and adolescence. Using structural MRI, Li et al. (2013) exclusively examined eight structural covariance networks in 240 healthy participants aged 18–89 years, and charted the age-related network reorganization involving language-related speech and semantics processing, executive control network (ECN) and the DMN network. In a longitudinal structural MRI study, Wu et al. (2013) illustrated age-related dynamic changes in network connectivity: The structural covariance networks develop into a fast distribution from young to middle age (∼50 years old) and eventually become a fast localization in the old age. These studies significantly increased our understanding of structural substrates underlying normal development and aging.

## **BRAIN DISORDERS**

Various kinds of brain disorders were investigated using network analysis approaches. Using combined resting fMRI and structural MRI, Chen et al. (2013a) demonstrated that the insular module in the cognitively normal group broken down to pieces in patients with Alzheimer's disease and that the corresponding gray matter concentration was significantly lower in the patient group. Importantly, they proposed a quantitative index by integrating the functional connectivity changes and structural changes in this brain module, which shows potential as diagnostic biomarkers of Alzheimer's disease at the single-subject level. Using an independent component analysis (ICA) and a dual regression technique, Rytty et al. (2013) found that patients with behavioral variant of frontotemporal dementia showed abnormally increased resting fMRI connectivity in the dorsal attention network and DMN network, which might provide neuronal basis for impairments in executive functions and attention in patients.

Two fMRI studies directly examined schizophrenic brain networks. Using a monetary incentive delay experimental task, White et al. (2013) observed dysregulated but not decreased functional activities in the salience network (SN) in schizophrenia, which offers physiological explanations for the delusional thought formation in this disease. Using resting fMRI data, Anderson and Cohen (2013) showed that patients with schizophrenia had disrupted functional network topology as characterized by less clustering and lower small-world connectivity. Specifically, a support vector machine classifier based on these connectivity features could discriminate individuals with schizophrenia patients from healthy controls with 65% accuracy. Worth noting is that Ottet et al. (2013) investigated topological patterns of DTIbased structural brain networks in the 22q11.2 deletion syndrome (22q11.2DS), usually considered to be a homogeneous genetic sub-type of schizophrenia. They showed a loss of global degree connectivity in brain hubs of the patients (∼58%) and the association between local efficiency of several key regions (the Broca's area, the Wernicke area and the dorsolateral prefrontal cortex) and symptom severity. These results provide evidence for the targeted alterations of specific brain hubs associated with language and thought regulation in individuals with a genetic risk for schizophrenia, which may help to understand the biological mechanism underlying hallucination.

Three studies highlighted brain network dysfunctions in mood disorders. Using resting fMRI data and high-model order ICA, Doll et al. (2013) examined the interactions across three major intrinsic networks of the human brain (i.e., SN, DMN, and ECN) in borderline personality disorder. They observed disrupted intranetwork connectivity in all three networks and a strong shift of inter-network connectivity from networks involved in cognitive control to those in motion processing, potentially reflecting the persistent instability of emotion regulation in patients. Also using the ICA approach, Manoliu et al. (2014) reported decreased intra-network connectivity within the SN in patients with major depressive disorder and that the extent of decrease was associated with severity of symptoms. Moreover, inter-network connections were decreased between the DMN and ECN, and were increased between the SN and DMN. These findings suggest an important link between aberrant salience mapping and network coordination involving cognitive processes and psychopathology in depression. Using graph theoretical analysis based on cortical thickness from structural MRI, Kim et al. (2013) provided direct evidence for disparity between dorsal and ventral networks in cortico-striato-thalamic circuit in patients with obsessive-compulsive disorder.

Network abnormalities were also demonstrated in other brain disorders including traumatic brain injury and pathological gambling. Combining task-related fMRI functional connectivity with DTI structural connectivity, Caeyenberghs et al. (2013) for the first time examined topological correlations of structural and functional brain networks in patients with traumatic brain injury and healthy controls. They found that graph metrics and hubs of brain networks showed no agreement in both groups, suggesting that the topological properties of functional networks could not be solely accounted for by the structural networks. However, prediction accuracy in switching performance could be improved by combining brain connectivity information from both imaging modalities. Using graph-based network analysis of resting fMRI, Tschernegg et al. (2013) reported that at the nodal level, pathological gambler had reduced clustering coefficient and local efficiency in the left paracingulate cortex and the left supplementary motor area, but an increased node betweenness for these regions, suggesting that regions in the reward system show reduced functional segregation but enhanced functional integration. These findings provide direct evidence for the topological abnormalities of the brain networks associated with pathological gambling.

Overall, the wealth of methods and applications covered by this research topic shows the exciting recent advances of multi-modal neuroimaging and network analysis as powerful approaches to study the neuronal circuits of healthy and diseased populations. We anticipate that these works will provide critical insights into the field of imaging brain networks. Lastly, we would like to thank all of the authors, reviewers and the Frontiers editorial office for their important contributions to this Research Topic.

## **REFERENCES**


and experimental study. *Front. Hum. Neurosci*. 7:275. doi: 10.3389/fnhum.2013. 00275


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 15 October 2014; accepted: 16 October 2014; published online: 03 November 2014.*

*Citation: He Y and Evans A (2014) Magnetic resonance imaging of healthy and diseased brain networks. Front. Hum. Neurosci. 8:890. doi: 10.3389/fnhum.2014.00890 This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2014 He and Evans. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling

#### *Barry Horwitz <sup>1</sup> \*, Chuhern Hwang1,2,3 and Jeff Alstott 4,5*

*<sup>1</sup> Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA*

*<sup>2</sup> National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA*

*<sup>3</sup> Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA*

*<sup>4</sup> Section on Critical Brain Dynamics, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA*

*<sup>5</sup> Brain Mapping Unit, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridgeshire, UK*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Willem De Haan, VU University Medical Center, Netherlands Xuhong Liao, Hangzhou Normal University, China*

#### *\*Correspondence:*

*Barry Horwitz, Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, 10 Center Drive, Room 5D39, MSC 1402, Bethesda, MD 20892, USA e-mail: horwitzb@nidcd.nih.gov*

Recently, there have been a large number of studies using resting state fMRI to characterize abnormal brain connectivity in patients with a variety of neurological, psychiatric, and developmental disorders. However, interpreting what the differences in resting state fMRI functional connectivity (rsfMRI-FC) actually reflect in terms of the underlying neural pathology has proved to be elusive because of the complexity of brain anatomical connectivity. The same is the case for task-based fMRI studies. In the last few years, several groups have used large-scale neural modeling to help provide some insight into the relationship between brain anatomical connectivity and the corresponding patterns of fMRI-FC. In this paper we review several efforts at using large-scale neural modeling to investigate the relationship between structural connectivity and functional/effective connectivity to determine how alterations in structural connectivity are manifested in altered patterns of functional/effective connectivity. Because the alterations made in the anatomical connectivity between specific brain regions in the model are known in detail, one can use the results of these simulations to determine the corresponding alterations in rsfMRI-FC. Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification. Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights. Note that increases in rsfMRI-FC in patients have been interpreted in some cases as resulting from neural plasticity. These results suggest that this interpretation can be mistaken. The relevance of these simulation findings to the use of functional/effective fMRI connectivity as biomarkers for brain disorders is also discussed.

#### **Keywords: neural modeling, fMRI, functional connectivity, brain disorders, human brain**

## **INTRODUCTION**

In the past few years, brain connectivity analyses have become important tools in the investigation of brain disorders [besides the articles in this Special Issue, see, for example, the Frontiers in Systems Neuroscience Special Issue on Brain Connectivity Analysis: Investigating Brain Disorders (Horovitz and Horwitz, 2012; Horwitz and Horovitz, 2012)] <sup>1</sup> . Probably the most common connectivity studies have used diffusion tensor imaging (DTI) <sup>2</sup> to investigate brain anatomical connectivity and functional magnetic resonance imaging (fMRI) to examine functional and/or effective connectivity. Although there still exists some confusion in the literature as to the definition of the latter two terms (Horwitz, 2003), for the purposes of this article we follow Friston (1994) and take functional connectivity to denote a statistical relationship between the functional neuroimaging signals in two or more brain regions (e.g., a correlation coefficient or a regression coefficient), and effective connectivity to mean the direct effect of one brain region's activity on another during a specified experimental condition (e.g., the functional strength of the directed anatomical link from one region to another during a particular task).

<sup>1</sup>Please note that we use the term "brain disorder" quite loosely. In particular, because there are both structural and functional changes during normal development and normal aging, studies of either of these processes can be considered here, since many of the issues that come about when comparing patients and healthy subjects would also be present when comparing subjects in different age groups.

<sup>2</sup>See Appendix for a list of all abbreviations used in this paper.

The earliest functional connectivity neuroimaging studies that used positron emission tomographic (PET) data were acquired during the so-called resting state (e.g., Horwitz et al., 1984), but gave way a few years later to task-based studies (Horwitz et al., 1992), especially when fMRI became available (e.g., Friston et al., 1997; Bokde et al., 2001). Thus, there developed a substantial literature on activation studies of patients with brain disorders employing functional/effective brain connectivity analysis methods (e.g., Horwitz et al., 1995; Bokde et al., 2006; Just et al., 2007; Rytsar et al., 2011). However, during the past decade or so, there has been an explosion in the number of studies using resting state fMRI (rsfMRI) to characterize functional brain connectivity in normal subjects (e.g., Biswal et al., 1995; Yeo et al., 2011) and in patients with a variety of neurological, psychiatric, and developmental disorders (e.g., Cherkassky et al., 2006; Wang et al., 2006; Alexander-Bloch et al., 2010; Lynall et al., 2010; Damoiseaux et al., 2012; Venkataraman et al., 2012; Lynch et al., 2013). The literature on functional neuroimaging connectivity studies in brain disorder patients is now huge, and obviously difficult to summarize. It is possible to generalize, however, and say that almost all published studies have found differences in functional (or effective) connectivity between patients and healthy control subjects. Often, the differences correspond to a decreased connectivity in the patients, although in many instances, increased interregional connectivity has been reported; sometimes, both types of differences are found together (e.g., Horwitz et al., 1995; Damoiseaux et al., 2012; Venkataraman et al., 2012). Note also that it has become widely appreciated that neuroimaging studies of brain connectivity, both functional and structural, have the potential for generating useful biomarkers for the detection and diagnosis of brain disorders and for the assessment of their treatment [for example, for Alzheimer's disease (AD), see (Horwitz and Rowe, 2011; Damoiseaux, 2012)].

Nonetheless, the question does arise as to how these alterations in functional/effective connectivity should be interpreted. For example, some researchers have suggested that an increased functional/effective connectivity may reflect some type of compensatory change that helps maintain normal function in spite of aberrant function in other parts of the brain. Also, can one attribute, as is often done, a reduced functional/effective connectivity to a decreased structural link between two brain regions? A decreased structural link may manifest itself as a reduced axonal input (either fewer axons or less effective synaptic inputs) from one neural population to another. How can we determine if these interpretations of functional brain connectivity analyses are justified? With respect to human brain disorders, it is obviously hard (indeed impossible at present) to actually do this using experimental data, since invasive techniques cannot be employed. Furthermore, the complexity of the mammalian brain mostly precludes any sort of direct comparison between measures of interregional neuronal connectivity and fMRI based measures in non-human animals, although some recent efforts in this direction (Logothetis, 2012), including using optogenetic approaches (Lee, 2011), show some promise. Rather, these issues have started to be addressed using computational neural modeling.

In this paper, we will discuss a few of these neural modeling efforts in the section entitled Simulated fMRI Data and Functional/Effective Connectivity, focusing especially on what has been learned about how to interpret differences in functional/effective connectivity between patients and healthy subjects in Simulating the Effect of Altered Anatomical Connectivity on Functional/Effective Connectivity. We will conclude in The Role of Simulation in the Development of fMRI Biomarkers with some thoughts on the role that neural modeling can play in developing fMRI functional/effective connectivity based biomarkers for various aspects related to the detection and treatment of brain disorders.

## **SIMULATED fMRI DATA AND FUNCTIONAL/EFFECTIVE CONNECTIVITY**

There have been a number of investigators who have developed multi-region network models that can simulate functional neuroimaging data. These models vary with respect to how "biologically realistic" are the elements that comprise each model. Efforts of this sort that deal with the kind of task-related flow/metabolic neuroimaging data generated by PET and fMRI began in the mid-to-late 90s (Arbib et al., 1995; Tagamets and Horwitz, 1998; Horwitz and Tagamets, 1999), and have increased dramatically since then (e.g., Corchs and Deco, 2004; Deco et al., 2004, 2008; Husain et al., 2004; Edin et al., 2007; Marreiros et al., 2008; Smith et al., 2013). Recently, a number of groups have developed modeling platforms for examining simulated rsfMRI data (for instance, Alstott et al., 2009; Honey et al., 2009; Cabral et al., 2011, 2012b; Smith et al., 2011; Ritter et al., 2013). Although some of these modeling efforts have focused on examining differences between healthy subjects and patients, others have used the computational models to address how specific tasks are implemented at the neural level. Relevant to the discussion that will follow, we will illustrate three of these modeling efforts.

The model developed by Tagamets and Horwitz (1998), although initially applied to regional cerebral blood flow (rCBF) PET data, was soon extended to blood oxygenation level dependent (BOLD) fMRI (Horwitz and Tagamets, 1999). The model was designed to simulate a short-term memory task for visual objects. It consisted of a number of distinct neuronal populations along the ventral visual processing stream arranged in the following brain regions (see **Figure 1**): primary and secondary visual cortex (V1), extrastriate visual cortex (V4 and IT), and prefrontal cortex (PFC). The visual feature that was modeled was object shape, and thus the V1 neurons were configured to respond to line orientation (for simplicity, the orientations were restricted to horizontal and vertical). The basic neuronal element in each module was a modified Wilson–Cowan unit (Wilson and Cowan, 1972), which consists of an excitatory-inhibitory pair that can be thought of as representing an extremely simplified cortical column. Each model population contained 81 basic elements. The populations were connected together based, as much as possible, on known primate neuroanatomy. For example, connectivity was such that the spatial receptive field increased as one moved down the object processing pathway. The PFC region contained four distinct simulated neuronal populations whose activities were designed to correspond to the experimental data of Funahashi et al. (1990), obtained from monkeys during the performance of a delayed response task. The model simulated

**FIGURE 1 | Large-scale neural network models of the visual and auditory object processing pathways (Tagamets and Horwitz, 1998; Husain et al., 2004).** Shown are the modules specific to the visual model (LGN, V1-V2, V4, IT) in black-bold and those specific to the corresponding auditory model (MGN, Ai, Aii, ST, PFC) in gray-italics. Within each module are sub-modules. The PFC module is common to both models and shown are its sub-modules. Each sub-module contains 81 basic neural elements consisting of an interacting pair of excitatory and inhibitory units (Wilson and Cowan, 1972). Connections between modules are display (solid:

a delayed match-to-sample task, in which a simulated object is presented for a short period of time, there is a delay period, and a second object is presented. The goal was to determine if the second object was the same as the first. An intertrial interval then occurred, and another trial began. The entire simulation corresponded to multiple trials, as would occur during an actual PET or fMRI study. The properties of the simulated neurons were configured so that their firing patterns were similar to those obtained from electrophysiological monkey studies. The spatiotemporal integrated synaptic activities (absolute value of the excitatory and inhibitory neuronal inputs) were assumed to represent the rCBF in each area for PET (Tagamets and Horwitz, 1998). For fMRI, the integrated synaptic activities were calculated for a time period of about 50 ms (the time needed to acquire a single MRI slice), convolved with a function representing the hemodynamic response, and then downsampled each TR (e.g., *TR* = 2 s) to represent simulated BOLD-fMRI (Horwitz and Tagamets, 1999). Good agreement was obtained between the simulated PET data and the experimental PET data of Haxby et al. (1995) (see Tagamets and Horwitz, 1998 for details). This model was later modified by Husain et al. (2004) to produce a simulation model for auditory object processing. Both the visual and auditory models were subsequently employed to simulate fMRI-functional connectivity data (time-series correlations) (Horwitz et al., 2005; Kim and Horwitz, 2008).

It is important to notice that these kinds of multiregion large-scale simulations require a combination of three component models. The first component is a structural model that indicates how the simulated brain regions are anatomically linked, and what are the strengths of the linkages. The second component is a neuronal model. The third component is a excitatory-to-excitatory; dashed: excitatory to inhibitory). Models perform a delayed match-to-sample task for either visual objects (combinations of horizontal and vertical lines) or auditory objects (combinations of pure tones and up- and down-frequency sweeps. Abbreviations: LGN, lateral geniculate nucleus; MGN, medial geniculate nucleus; V1-V2, primary and secondary visual cortex; V4, extrastriate visual cortex; IT, inferior-temporal cortex; Ai, primary auditory cortex; Aii, secondary auditory cortex; ST, superior temporal gyrus-sulcus; PFC, prefrontal cortex. Taken from Horwitz and Smith (2008).

hemodynamic response model that converts the neural activity into a neuroimaging signal. In the simulations just discussed, the structural model was based on primate neuroanatomy, the neuronal model was the Wilson-Cowan unit, and the hemodynamic model was a simple Poisson convolution function acting on the integrated synaptic activity.

An example of simulating human rsfMRI data was provided by Honey et al. (2009). They used a structural model based on diffusion spectrum imaging (DSI) data obtained from five normal human participants originally described by Hagmann et al. (2008) (see **Figure 2A**) <sup>3</sup> . The structural connections were evaluated from streamline tractography values between each pair of 998 cortical regions. The neural model assigned to each of these regions employed the neural mass model of Breakspear et al. (2003), which represents an ensemble of excitatory and inhibitory neurons possessing both ligand-gated and voltage-gated membrane channels. A non-linear hemodynamic model was used to convert simulated neural activity into simulated BOLD fMRI data (Friston et al., 2000) (see **Figure 2B**). Honey et al. (2009) used this formulation to compare simulated rsfMRI data against actual fMRI data obtained in the same subjects from whom the DSI data were acquired. Their main conclusion was that in both the simulated and experimental data, the underlying structural connectivity constrained the pattern of resting state functional connectivity, although some functional connectivity between non-anatomically connected regions was also present. These

<sup>3</sup>The two matrices shown in **Figure 2** were generated by the current authors using the structural, neural, and hemodynamic models originally employed by Honey et al. (2009) and Alstott et al. (2009).

findings were supported by a resting state fMRI functional connectivity (rsfMRI-FC) study in monkey by Adachi et al. (2012), who also performed a simulation study employing the modeling framework of an earlier Honey et al. paper (2007).

Gustavo Deco and his colleague have used a comparable modeling approach to that of Honey et al. (2009) to investigate other aspects of rsfMRI data (Deco et al., 2009; Cabral et al., 2011). For instance, Cabral et al. (2011) found that slow power fluctuations in gamma (60 Hz) oscillations at the local neural level could result in long-range interregional resting state synchrony at very low frequencies (*<*0.1 Hz), indicating that local neural dynamics can have an important effect on network connectivity patterns [see Hlinka and Coombes (2012) for a similar finding]. Cabral and colleagues employed the same structural model as used by Honey et al. [although downsampled to 66 regions of interest (ROIs) from the full set of 998 of the original], as well as the same hemodynamic model. However, they utilized a simpler neural model: the Kuramoto oscillator (Kuramoto, 1984), which has been used extensively to examine the behavior of coupled oscillatory systems. Other component models were employed in other studies by this group. For example, in Deco et al. (2009), the structural model was that of the macaque monkey obtained using anatomical connectivity values from the CoCoMac database (Kotter, 2004), and the neural model utilized the Wilson-Cowan formulation (Wilson and Cowan, 1972). An important insight they found was the critical role that conduction delays between connected brain regions play in allowing synchrony to emerge.

It is worth noting that the main reason different component models are used in different studies is because each study is attempting to understand just a few aspects of the data. So, a neural oscillator model was used when the goal of the study was to relate high frequency neural activity to low frequency BOLD activity, as was the case in the Cabral et al. paper (2011). Some of the other studies that were mentioned placed more emphasis on neural realism, and so models more directly inspired by neurons were employed. In all cases, because there are so many interacting neural units in these large-scale simulations, the simplest neural model that embodied the crucial features of the data was chosen. As more such studies appear in the future, it will be important to determine the degree to which the simulated results depend on the exact nature of the component models that are used. For example, resting state studies may well be somewhat insensitive to the exact neural and metabolic models that are employed, whereas task-based studies may show a strong dependence on the composition of the neural model that is used.

An important issue to mention here is that because these largescale models can produce multiregional simulated fMRI data that are comparable to experimental data, many of the same analysis techniques that are applied to the experimental data can be applied as well to the simulated data. This is important, given that network analysis techniques, especially graph theory, are commonly employed in MRI studies of structural and functional connectivity (Achard et al., 2006; Bassett and Bullmore, 2006; Bullmore and Sporns, 2009; Sporns, 2012), and as we shall see, these network metrics can be utilized for investigating brain disorders.

Finally, even though the current paper is focused on fMRI functional/effective connectivity, it is worth noting that there also is a vast literature in which brain connectivity analyses are performed on EEG/MEG data (e.g., Gevins and Bressler, 1988; Gross et al., 2001; Daunizeau et al., 2009; Brookes et al., 2011; Rong et al., 2011), and large-scale neural modeling has been employed to help interpret experimental findings (for example, see Wendling et al., 2009; Banerjee et al., 2012).

## **SIMULATING THE EFFECT OF ALTERED ANATOMICAL CONNECTIVITY ON FUNCTIONAL/EFFECTIVE CONNECTIVITY**

One important application of these large-scale simulation models has been the investigation of the effects of various types of brain alterations on functional/effective connectivity. As we pointed out in the Introduction, interpreting the results of a brain alteration in real experimental data is difficult because of the complexity of the underlying neural architecture, coupled with neuroplasticity that can occur in real brains subsequent to the alteration. In a large-scale simulation, however, everything is under the control of the researcher, and, in principle, everything that goes on during a simulation can be tracked and evaluated.

Cabral and colleagues published a study that nicely illustrates what can be learned about bran disorders from simulations of rsfMRI (Cabral et al., 2012b). In this investigation, the effects of structural disconnection on rsfMRI-FC was examined using a large-scale neural modeling framework. The structural model that was employed consisted of 90 ROIs derived from DTI data acquired from 21 healthy participants; the neural model for each ROI, based on the dynamical equations of Mattia and Del Giudice (2002), generated spontaneous neural activity; and the hemodynamic model that the authors used was the Balloon-Windkessel model of Friston et al. (2000). The simulated rsfMRI-FC was evaluated as the temporal correlation between ROI time series, and graph theoretic measures (Bassett and Bullmore, 2006; Bullmore and Sporns, 2009) were employed to characterize the pattern of connectivity among all the ROIs. Two types of structural disconnection were simulated—global and local. In the equations relating the change in neural activity (firing rate) in one region (region *n*) to that in other regions, there exists a term *kCnp*, where *k* is the global excitatory coupling between all regions and *Cnp* is the structural coupling strength from region *p* to region *n*. For the global disconnection simulations, *k* was uniformly reduced. It was found that a number of the graph theoretic metrics changed, resulting in a less globally correlated and globally integrated set of BOLD values. The second kind of structural disconnection that they simulated was a more localized type, in which Cabral and colleagues successively removed randomly 1% of the possible links (what they termed "pruning the matrix"). The results for this case were similar to that for the global disconnection case a reduction in functional connectivity leading to reduced global integration.

Cabral et al. (2012a) went on to explicitly compare simulated rsfMRI-FC with experimental data acquired from patients with schizophrenia (Lynall et al., 2010). The experimental data showed that, compared to healthy control subjects, the schizophrenia patients had weakened functional connectivity and an increased diversity of functional connections. Cabral and colleagues tested the hypothesis that these disrupted functional networks in the patients could be explained by a global decrease in structural coupling between cortical regions. They found that a small decrease in the global structural coupling parameter, *k*, yielded a reduced functional connectivity that resulted in graph theoretic changes similar to those documented by Lynall et al. (2010).

Other simulation studies have examined the effects of focal lesions on rsfMRI-FC, including investigations that employed structural models based on macaque connectivity (Honey and Sporns, 2008) and those that used structural data from humans (Alstott et al., 2009). We will discuss the latter of these. The structural, neural, and hemodynamic models used by Alstott et al. (2009) were the same ones as those employed by Honey et al. (2009): a DSI data set from 5 healthy human participants (Hagmann et al., 2008), the neural model of Breakspear et al. (2003) and the Friston et al. balloon model (Friston et al., 2000). A number of important findings were reported, including one showing that lesions along the cortical midline, in the temporoparietal junction and in frontal cortex resulted in large and widely distributed reductions in rsfMRI-FC; some of these alterations involved regions outside the lesion site. In contrast, lesions of sensory and motor regions produced functional connectivity changes that were more localized to the area of the lesion (see **Figure 3**).

The studies involving alterations in anatomical connectivity that we have so far mentioned involved simulating rsfMRI data. Task-based fMRI also has been examined using large-scale modeling, and one such paper by Kim and Horwitz (2009) investigated the effect of decreased structural connectivity on task-related effective connectivity. The general question that this study asked was: how should one interpret a significant difference between patients and controls in the effective connectivity between two nodes? In particular, does such a difference imply that there is a corresponding alteration in the underlying structural connectivity between the nodes? Kim and Horwitz used the largescale neural model of Tagamets and Horwitz (1998), discussed in Simulated fMRI Data and Functional/Effective Connectivity, to address these questions. They reduced the strength of the structural connection from IT to PFC (see **Figure 4**, upper) by an average of 80% in 20 simulated "patients," and compared the simulated fMRI obtained during the DMS task with comparable data from 20 "normal control" simulations. Structural equation modeling (SEM) (McIntosh et al., 1994) was used to evaluate effective connectivity for all the connections between all regions in the network. As shown in **Figure 4** (lower), the effective connection from IT to PFC (FS) indeed was significantly reduced in the patients relative to the controls. So, this simulation result suggests that reduced structural connectivity can be reflected as reduced fMRI effective connectivity. **Figure 4** also shows that the effective connectivity downstream from the induced structural disconnection (i.e., the connectivity within the PFC) also was generally reduced. This result is not unexpected: the disruption in the IT-FS connection leads to incorrect neural processing in downstream parts of the PFC network. The third result from this simulation is, at first glance, unexpected: the increased effective connectivity "upstream" (e.g., the V1–V4 effective linkage) in patients relative to controls. As mentioned in Introduction, numerous groups have reported increased patient functional/effective connectivity (e.g., for AD, Horwitz et al., 1995; Damoiseaux et al., 2012), and in many cases, this increase is attributed to some type of neural plasticity. The simulation produced by Kim and Horwitz (2009) indicates that this interpretation may not always be warranted. In the simulation, no structural alteration in the V1–V4 connections weights took place. Rather, the increased effective connectivity resulted from a reduced feedback effective connection from PFC to V4, which in turn led to V4 being more influenced by V1 activity than was the case in the normal subjects. A major conclusion from the Kim and Horwitz study was that interpretation of fMRI functional/effective connectivity changes in patients relative to controls requires a careful consideration of the entire network mediating the task under study.

What about the situation for rsfMRI-FC? Would similar findings as illustrated by the Kim–Horwitz study (Kim and Horwitz, 2009) occur, or are those interpretational problems found only in task-based fMRI studies? As Alstott et al. (2009) showed, both increases and decreases in rsfMRI-FC occurred following cortical lesions. For example, as illustrated in **Figure 3B**, a lesion centered in the left temporo-parietal junction resulted in strengthened rsfMRI-FC in the contralesional hemisphere. Some of these increases are due to direct loss of inputs from the lesioned area, resulting in greater functional connectivity between right hemisphere nodes. As was the case with the Kim-Horwitz example, these increases are not the result of any change in the strength of the anatomical connection weights.

The studies discussed above obviously did not consider all the complexities that are likely to be found in investigations of brain disorders. Future neural modeling efforts will be needed to address such issues as how the various kinds of neuroplasticity, which can operate over multiple time scales, even ones whose duration are within the time frame of a single scan, affect the functional/effective connectivity of relevant networks. Some of these neuroplastic changes may occur due to changes in anatomical connectivity.

It is worth noting, by the way, that we have oversimplified things by assuming that there is a clear distinction between anatomical and function/effective connectivity. At the level of neuron and synapse, however, this distinction breaks down: in which category does one place axonal sprouting and the formation of new synapses, or even the strengthening of a single synaptic contact? Indeed, one kind of connectivity change can lead to a change in the other—Hebbian learning would be an obvious example. These issues will need to be confronted in future neural modeling studies.

## **THE ROLE OF SIMULATION IN THE DEVELOPMENT OF fMRI BIOMARKERS**

An important issue that was alluded to in the Introduction was the utilization of neuroimaging for generating assorted biomarkers for brain disorders. Horwitz and Rowe (2011) have discussed the various uses for which such biomarkers could be employed<sup>4</sup> . These include detection or prediction of a disorder, differential diagnosis, and staging a disorder and investigating treatment efficacy.

A significant and obvious point related to biomarker development is that such markers are meant to be used on individual patients (or potential patients). As such, an important issue is how likely is it that fMRI will be able to provide sufficient signal-to-noise ratio to be usable in single subjects (Horwitz and Rowe, 2011; Damoiseaux, 2012; Vemuri et al., 2012). Most of the experimental studies we have mentioned were group studies, and although these investigations are important for discerning signal patterns that have the potential to discriminate between patients (actual or potential) and non-affected individuals (or between different types of patients), clinically useful fMRI biomarkers are still a future goal, not a present reality. Two areas of fMRI research that are likely to lead to improvements are in hardware development and in advances in the use of multivariate signal processing techniques (e.g., Smith et al., 2010); for a review, see Smith, 2012.

A second issue, implicit in our previous discussion, concerns what kind of fMRI technique (i.e., resting state fMRI or taskbased fMRI) is better to use for a particular brain disorder. The answer depends on two things: which brain disorder is the focus of interest, and which question is the biomarker attempting to address. In some cases, it may be that rsfMRI will be more appropriate. For example, getting small children to do a specific set of tasks could lead to compliance problems of one sort or another. In other cases, task-based fMRI might have a significant advantage. Specifically, task-based fMRI provides the opportunity to record behavioral measures during scanning, and thus, these behavioral measures can be correlated with the changes in connectivity. This

<sup>4</sup>The Horwitz-Rowe article focused on neurodegenerative disorders; however, many of the points made are relevant for numerous brain disorders.

**FIGURE 4 | Comparison of fMRI effective connectivity differences between simulated patients and normal subjects for a delayed match-to-sample task for visual shape (Kim and Horwitz, 2009).** The top part of the figure shows the nodes and connections of the neural net model used (Tagamets and Horwitz, 1998) (it is the same model shown in **Figure 1**, which should be consulted for abbreviations). Simulated patients' data were obtained by reducing the connection weight between the IT and FS modules an average of 20% of its normal value. The lower part of the figures shows the results of applying an effective connectivity analysis (structural equation modeling) to the normal and patient networks. Significant reductions in patients relative to controls are in violet, significant increases are in green. Modified from Kim and Horwitz (2009).

is a powerful method for determining which connectivity changes are aiding the person being scanned and which are reducing their performance. Similar behavioral correlations have been used with resting state connectivity changes, but the behavioral measure is on a subject by subject basis, not on a trial by trial basis. For example, Venkataraman et al. (2012) found two co-existing patterns of connectivity in their schizophrenia patients: increased frontalparietal connectivity that was associated with severity of positive symptoms, and decreased parietal-temporal connectivity that was related to negative symptoms.

As an illustration of the task vs. resting issue, consider AD. We know that the pathology of AD can be found in individuals' brains decades before clinical symptoms appear (Reiman et al., 1996; Hampel et al., 2011), and young adults at risk for developing late-onset AD show default mode network (DMN) alterations (Filippini et al., 2009). Given this situation, if an appropriate therapy were available, when should it be given? One might want to start it before a patient demonstrates cognitive deficiency (in which case there may be a significant reduction in viable brain tissue), but perhaps not years or decades before, given the likely costs of the treatment and the potential side-effects of the therapy. In analogy with cardiovascular disease, a "cognitive stress test" during fMRI scanning might provide a way to assess neural integrity. However, one study (Fleisher et al., 2009) has been used to argue against task-based fMRI studies and in favor of rsfMRI in AD. Fleisher et al. showed that rsfMRI of the DMN had a larger effect size than did an fMRI encoding task for distinguishing AD high-risk from low-risk groups. However, it should be noted that although functional connectivity was utilized for the rsfMRI portion of the study, the researchers only used differences in regional BOLD deactivation in DMN nodes during the encoding part of the investigation. As Horwitz and Rowe (2011) have suggested, a task-base network analysis, targeting a network that shows early impairment in AD (such as memory), might be more sensitive compared to examining individual region of interests, since network analysis is intrinsically multivariate. One would determine if the at-risk subject's data fit the network defined by healthy control subjects performing the same task. If the fit is bad, that would suggest that therapy might be warranted. This scheme is based on the notion that neuroplasticity enables behavioral performance to be maintained during the many years during which brain pathology builds up.

As we have just seen, progress has been slow in developing fMRI based biomarkers. Among the reasons for this are the difficulty in performing neuroimaging studies on patients, and importantly, not being able to actually "know the answer." Of the patients at risk for a given disorder, how many will actually get the disorder, and when will they get it? Patient variability is often huge, and different individuals could have different amounts of neuroplasticity over the years during which a disorder may have gone undiagnosed. How do we know that a group difference in some fMRI metric will be large enough in individuals to be able to distinguish a single subject with a high sensitivity and specificity? Note that the problem is not just scanner signal-to-noise, as was mentioned earlier. Rather, the additional problem is that there is large subject-to-subject variability in humans, even in healthy subjects—structural brain differences (e.g., see Amunts and Zilles, 2001), and functional differences (e.g., see Kanwisher and Yovel, 2006).

Computational neural modeling may provide a method to circumvent some of these issues in attempting to determine if an fMRI based metric can serve as a biomarker for detecting a brain abnormality. As an illustration, how weak can a brain structural disconnection be so that it is undetectable using rsfMRI-FC analysis? In our review of the simulation studies of Deco, Cabral and their group and Alstott, Honey, Sporns and their colleagues, the extent of the structural damage was quite large in many cases. For example, Alstott et al. (2009) found in one of their analyses many significant differences in functional connectivity in 5 subjects when they deleted 50 ROIs from an anatomical area (see **Figure 3** for two examples). Using the same set of models (structural, neural, and hemodynamic) as Alstott and collaborators, we targeted two anatomical areas for modification: the left precuneus (LPr) and the left medial frontal cortex (LMPF). All modifications were performed on the 25 ROIs closest in Euclidean distance to the center of the targeted areas. Specifically, the structural connectivities in a targeted area were scaled by 0.5 from the normal values. We examined focal, unidirectional, and bidirectional modifications. In focal alterations, connections among the 25 ROIs in a single anatomical area were scaled by 0.5, but connections between these targeted ROIs and all other ROIs in the cortex were left unmodified. Bidirectional and unidirectional structural alterations were only applied to the two separate anatomical regions—LPr and LMPF. In bidirectional modifications, the connections from one set of 25 target ROIs to and from the second set of 25 target ROIs were scaled by the specified amount of 0.5. In unidirectional modifications, the connections from one set of ROIs in LMPF to the LPr set of ROIs were scaled, but the connections from the latter set of ROIs to the former set were left intact.

Simulations were run for 10 "normals" subjects and 10 "patients." Variation in the subjects was introduced by adding or subtracting to all the structural connection weights random numbers from a Gaussian distribution with a standard deviation of 0.01. Pearson correlations between the time series of the simulated BOLD activity from each anatomical area for each "normal" subject and for each "patient" were evaluated. Given the small number of "subjects" (10 in each group), and the relatively weak reduction of structural connectivity between just two brain areas, it is not surprising that there were few robust group differences. Indeed, no significant group differences in rsfMRI-FC between the two targeted areas LPr and LMPF were found in any of the cases (focal, unidirectional, bidirectional). These simulation results thus indicate the relative insensitivity of simple rsfMRI-FC to detecting the presence of structural modifications that are weak and of restricted extent, even if one knows where to look. That is, not much change occurs when one input is reduced to areas that have inputs from multiple other areas. Simulations could be used to see if the situation is different when the modification affects a connection between nodes engaged in a task, as was the case for the Kim-Horwitz simulation (Kim and Horwitz, 2009) that was discussed earlier, but that would require adjusting the structural and neural models so that a specific task can be performed. Moreover, newer experimental and data analysis procedure could arise to improve the situation. For instance, high spatial resolution MRI may be able to find mild abnormalities in either structural or functional connectivity in the future.

## **CONCLUSIONS**

In this paper we reviewed some recent efforts at using neural modeling to help understand and interpret human neuroimaging data comparing patients with brain disorders to healthy subjects. Experimental neuroimaging data provide macroscopic measures of brain structure and function. In the case of fMRI, these data are indirect measures of function; the signals are those of the metabolic/hemodynamic consequences of neural activity. Among the factors confounding the interpretation of such data in patients are the sheer complexity of neural anatomy and connectivity and the immense plasticity of the brain. Large-scale neural modeling provides a way to study such a system and investigate how the size and extent of various modifications translate into alterations in neuroimaging signals. Furthermore, because we know what alterations actually took place in the modeled brains, potential interpretations of actual data can be checked against the simulated data.

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Our review of several studies that explored the fMRI consequences of alterations in anatomical connectivity lead to several conclusions. First, interpretation of changes in either functional or effective connectivity is not as straightforward as one might first suppose. Although a weakening of the structural connection strength between brain areas can appear as a decreased functional/effective connection, decreases and increases in functional/effective connectivity between areas not directly affected by the brain alteration are also found. Essentially, one must keep in mind that in a functional network, one cannot just change one link; functional networks are such that changes in one part of the network result in changes everywhere else (although not all these changes will be large enough to be statistically significant). Moreover, some of the changes in parts of a network unaffected by the structural alterations may result in a strengthening of the functional/effective connectivity, but these changes are not necessarily the result of neuroplasticity. Task-based fMRI may be a better choice than rsfMRI to deal with this issue, since it is often possible in task-based fMRI to acquire performance data during the scanning. Such data can then be correlated with the measured functional/effective connectivity, and the results of such an analysis may strengthen a claim for neuroplasticity mediating the altered connection. The net conclusion from all this is that the reverse inference—that a change in functional/effective connectivity in a patient means that there is a corresponding change in the underlying structural connectivity—is unwarranted.

We also discussed utilizing large-scale neural modeling as a tool for helping to develop fMRI resting state and/or task-based biomarkers for brain disorders. This is an area that is just beginning, but it does have potential advantages, especially in terms of cost and time. It is cheaper and less time consuming to run a large number of simulations than it is to find subjects and run fMRI experiments. But little work has been done in this area, so it will be a while before one can assess whether or not modeling can provide significant help in deciding which potential biomarkers are viable.

## **ACKNOWLEDGMENTS**

This work was supported by the Intramural Research Programs of the National Institute on Deafness and Other Communication Disorders, the National Institute of Mental Health, and the National Institute of Biomedical Imaging and Bioengineering, all part of the National Institutes of Health. The simulations performed for this paper utilized the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health, Bethesda, MD. The authors wish to thank the two reviewers for a number of useful comments and suggestions.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 10 June 2013; accepted: 22 October 2013; published online: 11 November 2013.*

*Citation: Horwitz B, Hwang C and Alstott J (2013) Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling. Front. Hum. Neurosci. 7:649. doi: 10.3389/fnhum.2013.00649*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Horwitz, Hwang and Alstott. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## **APPENDIX**

### **Abbreviations:**


## The potential of the human connectome as a biomarker of brain disease

## *Marcus Kaiser1,2,3\**

*<sup>1</sup> School of Computing Science, Newcastle University, Newcastle upon Tyne, UK*

*<sup>2</sup> Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK*

*<sup>3</sup> Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Frederik Barkhof, Vrije University Medical Centre, Netherlands Alex Fornito, University of Melbourne, Australia*

#### *\*Correspondence:*

*Marcus Kaiser, School of Computing Science, Newcastle University, Claremont Tower, Newcastle upon Tyne, NE1 7RU, UK e-mail: m.kaiser@ncl.ac.uk*

The human connectome at the level of fiber tracts between brain regions has been shown to differ in patients with brain disorders compared to healthy control groups. Nonetheless, there is a potentially large number of different network organizations for individual patients that could lead to cognitive deficits prohibiting correct diagnosis. Therefore changes that can distinguish groups might not be sufficient to diagnose the disease that an individual patient suffers from and to indicate the best treatment option for that patient. We describe the challenges introduced by the large variability of connectomes within healthy subjects and patients and outline three common strategies to use connectomes as biomarkers of brain diseases. Finally, we propose a fourth option in using models of simulated brain activity (the dynamic connectome) based on structural connectivity rather than the structure (connectome) itself as a biomarker of disease. Dynamic connectomes, in addition to currently used structural, functional, or effective connectivity, could be an important future biomarker for clinical applications.

**Keywords: brain connectivity, network disease, brain disorders, classification, diagnosis**

The study of how different components of the brain, may they be neurons or brain regions, are connected has become an emerging field within the neurosciences (Sporns et al., 2004; Bullmore and Sporns, 2009; Kaiser, 2011). The analysis of physical connections within neural systems gained momentum around 20 years ago with the availability of information on the nematode *Caenorhabditis elegans'* nervous system (White et al., 1986; Achacoso and Yamamoto, 1992) and the rhesus monkey's visual system of cortico-cortical connections (Felleman and van Essen, 1991; Young, 1992). Now called connectomics, the field aims to discover the structure of brain networks, representing physical connections such as axons or fiber tracts. As a next milestone, the first data sets of the Human Connectome Project are being released. What will the next 20 years bring? Like for genomics, the hopes are that features of the connectome of a patient can be a biomarker for diseases and an indicator for therapeutic interventions. Identifying biomarkers for diseases based on large-scale genome studies has been challenging. Is the link between connectivity and brain disease also over-weighted? What could a structural connectome in principle tell us about the brain organization in health and disease?

In analogy to genetics, we may distinguish a genotype and a phenotype of brain organization. The genotype is given by the structural connectivity either observed at the level of individual synapses (microconnectome) or at the level of fiber tracts between brain regions (macroconnectome) (DeFelipe, 2010) and we will refer to this as connectome. As for every novel field, the underlying techniques are still under development (Jbabdi and Johansen-Berg, 2011). Diffusion tensor and diffusion

spectrum imaging can give us information on potential structural connections of the macroconnectome. The phenotype represents activity, as seen in functional Magnetic Resonance Imaging (fMRI) or EEG, or behavior, as for cognitive clinical scores. We refer to these patterns as consequences on dynamics or behavior due to changed brain connectivity.

The problem of diagnosing a disease, as in genetics, is due to the fact that several mutations of the genotype might result in the same phenotype (disease). Observing brain connectivity, there might be several combinations of changes in fiber tracts leading to hallucinations or seizures, for example. Also, the same connectome organization might lead to different dynamics for changes that affect the internal anatomy and activity of network nodes but not the nodes' topology (**Figure 1**). The idea that many pathways can lead to similar behavior is linked to the concept of degeneracy (Tononi et al., 1999; Price and Friston, 2002), "the ability of elements that are structurally different to perform the same function or yield the same output." If the output (phenotype) is cognitive deficits in patients, the number of connectome (genotype) patterns that lead to such behavior can be seen as the degeneracy of a brain disease. Also, a higher degeneracy, meaning that more connectome patterns are linked to a disease, might result in a higher incidence in a population. A related observation has been made in the field of genetics when linking genetic changes to diseases: multiple genotypes might lead to the same phenotype (heterogeneity) (Addington and Rapoport, 2012). Therefore, detecting one connectivity pattern linked to a disease might only relate to a fraction of all patients. Moreover, many connectome changes will be

neutral in that they do not lead to a brain disorder; thus variability in the healthy population is expected to be large as well. As for genetics, connectomics is currently moving to large-scale studies, e.g., the 1,200-subject Human Connectome Project or the 1,000 subject Functional Connectomes study, to address this underlying variability.

within individual nodes.

Another problem besides large connectome variability ("noise") is that cognitive deficits might arise from small changes ("signal"). Development can be seen as a system of nonlinear dynamics (Turing, 1952). It has become clear that genetic encoding (Kendler et al., 2011) and self-organization shape the formation of neural systems in health and disease. For self-organization, the interaction with the environment (external factors) or physical constraints (internal factors) can influence the establishment and survival of axonal connections. Consequently, small changes during development might lead to a different connectome and as a result to a different resulting consequence for cognition and behavior of human subjects. As the dynamics in the brain are also non-linear, a small change in structural connectivity might be sufficient to lead to changes in cognition and behavior. Relatively small changes in connectivity might be sufficient to lead to a brain disorder. Therefore, some connectivity patterns seen in patients might be quite close to the organization of healthy subjects.

Let us look at some cases of how brain diseases could be linked to brain connectivity. Also, let us only use two cases of how a network structure (edge or node) in a patient could differ from that of a control group: a significant increase or a significant decrease of a network measure. We will only look at a single measure here, say number of streamlines for edges and total strength of its connections for nodes, but our general observations also hold for a combination of network measures (Costa et al., 2007; Kaiser et al., 2009; van den Heuvel et al., 2012).

First, a disease might affect a single brain region which could have an effect on brain dynamics by changing its own activity pattern, the pattern of directly connected neighbors of the region, and, indirectly, the activity in the rest of the brain mediated by intermediate brain regions. As a simplification, let us assume that only structural connections from that brain region will be altered. As each brain region (for a parcellation in humans of 110 cortical and subcortical regions including both hemispheres) is connected to around 10 other brain regions, there are 2<sup>10</sup> = 1,024 possible changes assuming that each connection could either be significantly increased or decreased in a patient. Thus even at the local scale, only affecting a single brain region, many variations of a disease are possible.

Second, a disease could affect a set of network nodes. For example, regions of the neocortex mature at different times during development: medial regions before lateral regions and posterior before anterior regions. A change in the maturation of the frontal lobe could affect multiple regions at the same time and might affect a whole network module (Nisbach and Kaiser, 2007). Say that 10 regions show a different internal structure that also manifests itself in altered fiber tracts between them and other brain regions. Therefore, assuming 10 fiber tracts per brain region, or 10<sup>2</sup> = 100 fiber tracts for all 10 affected regions, show changes leads to 2<sup>100</sup> = 1.3∗10<sup>30</sup> variants. Let us look at a simpler model where an increase (or a reduction) in at least 10 of those 100 fiber tracts is sufficient to lead to the behavioral features of a disease. There are - 100 10 = 1.7∗10<sup>13</sup> ways to choose 10 out of 100 connections. Given that 10 is the lower bound for disease onset, choosing 11, 12, 13, etc. connections leads to even more variations at this regional level.

Third, a disorder could lead to changes of a set of edges at the global level as a result of widely distributed changes. If there are 500 bidirectional connections (fiber tracts) between our 110 brain regions, there are 2<sup>500</sup> = 3.3∗10<sup>150</sup> possible changes compared to a benchmark brain based on a population of healthy subjects.

We know that there is huge variability not only in the surface shape of human brains but also in its related connectivity pattern (Van Essen, 1997; Hilgetag and Barbas, 2006). Clearly, only a small fraction of connectome patterns is linked to a brain disorder. Even if we assume that there are thousands of subtypes of brain disorders, e.g., different kinds of epilepsy, and that many diseases change synaptic efficacy without changing structural connectivity, there might still be billions of connectome changes that could lead to the clinical patterns observed in patients with one type of a disease. Clearly, no two patients are the same (neither are no two control subjects).

If there is a multitude of ways how connectome changes could lead to a disease, how can we use brain connectivity information to inform the diagnosis and treatment of clinical patients? First, some links between connectome and consequential brain dynamics might manifest themselves through changes of global network features despite the variability in the changes of individual connections. Examples are global topological changes, observed through diffusion tensor imaging, in remitted geriatric depression and amnestic mild cognitive impairment (Bai et al., 2012). However, the same global changes, say a deviation from the brain's small-world organization towards random or regular connectivity (Reijneveld et al., 2007), could be observed across diseases and therefore limit their use as a classifier for brain diseases.

Second, some changes might be so widespread that they affect the majority or all of the brain regions leaving fewer degrees of freedom for variability in connectomes. The overall pattern of altered structural connectivity in schizophrenia patients (Skudlarski et al., 2010), along with resulting functional connectivity changes (Fornito et al., 2012), would be one example for this case.

Third, changes that are linked to a brain disease might only affect specific circuits in the network. In that way, while the strength of most connections also varies in healthy controls, more consistent changes to specific fiber tracts would be expected for patients. As a consequence, changes in selected circuits would be common for a group of patients but a consistent change for all fiber tracts of a circuit would not occur in control subjects. While this is a potentially powerful approach it does need *a priori* knowledge about the affected circuit. Such circuits might be identified by large-cohort studies in patients or through "knock-out" studies, e.g., using transcranial magnetic stimulation (Hilgetag et al., 2001), in healthy subjects.

Finally, I would propose a novel approach to deal with the variability in brain disorders, which is the use of computer simulations of brain activity, based on the connectivity in individual patients. Such simulations are already emerging as a way to understand the structural correlates of dynamical changes and disease progression (Deco et al., 2011; Cabral et al., 2012; Raj et al., 2012). As shown above, multiple structural connectivity changes might lead to the same changes in brain dynamics, patient behavior, or clinical test scores. Simulating the activity in the brain of individual patients can inform us about the expected behavioral features and thus about the presence or absence of one sub-type of brain disorder. These models can go beyond the observation of patterns in the recordings of brain activity as simulated dynamics could include more complex models. For example, a model based on structural connectivity might include simulated activity of individual neurons or local circuits, which cannot be observed by non-invasive neuroimaging.

Using simulations in a clinical setting has several benefits. First, simulated behavioral features can be mapped to brain activity in patients that is available through fMRI, Positron Emission Tomography (PET), Magnetoencephalography (MEG), EEG, Electrocorticography (ECoG), or recordings in resected tissue (Roopun et al., 2010), depending on the disease. Second, the simulated behavior can be compared with the experimentally obtained behavior to validate and constrain a model: simulated activity can be compared with the clinical recordings of a patient. Third, observing dynamics in networks opens up the possibility to use the tools of nonlinear dynamics and time series analysis to find patterns that could be biomarkers for a given disease. Importantly, changes in brain dynamics might be visible even in cases where the structural connectivity is not significantly different from that of a healthy control group. Such simulations are becoming available both at the local (Blue Brain Project, Markram, 2006) and global level (Virtual Brain Project, Jirsa et al., 2010) and will be support through the Human Brain Project and other initiatives.

In conclusion, there is a large number of underlying structural connectome changes that might lead to the same functional and behavioral changes in healthy subjects and patients. This variety makes the detection of a brain disorder—not just the classification of the type of disorder (Hyman, 2010)—difficult. We propose the use of computer models to use the simulated dynamics (dynamic connectome) based on structural connectivity, rather than the directly measured structural connectivity alone, as a biomarker. In the same way that biology has moved from genes to gene expression data, the use of dynamic connectomes, observing or simulating activity in neural circuits, opens up future potential for clinical applications.

## **ACKNOWLEDGMENTS**

I would like to thank Drs Simon Eickhoff (Düsseldorf) and Stephen Jackson (Nottingham) for inspiring me to work on this question following a discussion at the Fusion Workshop at Korea University. This work was supported by the WCU program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (R32-10142), the CARMEN e-science project<sup>1</sup> as well as another project funded by EPSRC (EP/ K026992/1).

1http://www.carmen.org.uk


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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 29 May 2013; accepted: 01 August 2013; published online: 15 August 2013.*

*Citation: Kaiser M (2013) The potential of the human connectome as a biomarker of brain disease. Front. Hum. Neurosci. 7:484. doi:10.3389/fnhum.2013.00484*

*Copyright © 2013 Kaiser. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

#### *Wanqing Li 1,2, Xiaoqin Mai <sup>3</sup> \* and Chao Liu1,2\**

*<sup>1</sup> State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China*

*<sup>2</sup> Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China*

*<sup>3</sup> Department of Psychology, Renmin University of China, Beijing, China*

#### *Edited by:*

*Hauke R. Heekeren, Freie Universität Berlin, Germany*

#### *Reviewed by:*

*Xi-Nian Zuo, Chinese Academy of Sciences, China Qingbao Yu, The Mind Research Network, USA Joe Moran, Natick Soldier Research and Development Center, USA*

#### *\*Correspondence:*

*Xiaoqin Mai, Department of Psychology, Renmin University of China, No. 59, Zhongguancun Street, Haidian District, Beijing 100872, China e-mail: maixq@ruc.edu.cn; Chao Liu, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No.19, Xinjiekouwai Street, Haidian District, Beijing 100875, China e-mail: liuchao@bnu.edu.cn*

The Default Mode Network (DMN) has been found to be involved in various domains of cognitive and social processing. The present article will review brain connectivity results related to the DMN in the fields of social understanding of others: emotion perception, empathy, theory of mind, and morality. Most of the reviewed studies focused on healthy subjects with no neurological and psychiatric disease, but some studies on patients with autism and psychopathy will also be discussed. Common results show that the medial prefrontal cortex (MPFC) plays a key role in the social understanding of others, and the subregions of the MPFC contribute differently to this function according to their roles in different subsystems of the DMN. At the bottom, the ventral MPFC in the medial temporal lobe (MTL) subsystem and its connections with emotion regions are mainly associated with emotion engagement during social interactions. Above, the anterior MPFC (aMPFC) in the cortical midline structures (CMS) and its connections with posterior and anterior cingulate cortex contribute mostly to making self-other distinctions. At the top, the dorsal MPFC (dMPFC) in the dMPFC subsystem and its connection with the temporo-parietal junction (TPJ) are primarily related to the understanding of other's mental states. As behaviors become more complex, the related regions in frontal cortex are located higher. This reflects the transfer of information processing from automatic to cognitive processes with the increase of the complexity of social interaction. Besides the MPFC and TPJ, the connectivities of posterior cingulate cortex (PCC) also show some changes during tasks from the four social fields. These results indicate that the DMN is indispensable in the social understanding of others.

**Keywords: default mode network, social cognition, brain connectivity, morality, theory of mind, empathy**

## **INTRODUCTION**

## **THE DEFAULT MODE NETWORK AND SOCIAL UNDERSTANDING OF OTHERS**

Human beings are social animals that have a tendency to interpret stimuli according to their possible social relevance, and spend a huge amount of time assessing one's own and other's social relationships and positions by engaging in activities such as thinking about oneself and others and exchanging those thoughts during the whole of life (Schilbach et al., 2008). Dunbar and coleagues suggested a "social brain hypothesis," which deemed that the large brains observed in primates reflected the computational demands of the complex social systems that characterized the order of their members (Dunbar, 1993).

In the past two decades, the social brain of human has been intensively studied in several different domains: (1) understanding others, (2) understanding oneself, (3) controlling oneself, and (4) the processes that occur at the interface of self and others (Lieberman, 2007). However, in the strictest sense, social cognition is about understanding of other people, including their emotional, mental, psychological status, and behaviors (Lieberman, 2007). Increasing studies have shown that regions of the default mode network (DMN) largely activate in tasks requiring participants to understand and interact with others, such as perceiving and interpreting other's emotion status, showing empathy to other people, inferring other's belief and intention, and performing moral judgments on other's behavior (Schilbach et al., 2008; Laird et al., 2011). Besides overlaps with the DMN, the large scale brain networks for social domains also contain several regions outside the DMN, since these social behaviors usually comprise extensive cognitive processes such as obtaining, retrieving, and processing information about the lives, relationships, and mental states of others (Mars et al., 2012).

In the present article we will review results related to the DMN in the field of social understanding of others using brain connectivity methods. Several important fields of social behavior, emotion perception, empathy, theory of mind (ToM, or mentalizing), and morality, will be summarized for both healthy subjects and patients with autism, psychopathy and schizophrenia (see **Table 1**). The existing results were organized through two aspects. The first one is how the regions within the DMN interact with each other when people perform those social tasks, and the second one is how the DMN interacts with other distributed brain systems that contribute to the process of social cognition of others. Possible future directions will be discussed at the end.


#### **Table 1 | Brain connectivity studies on the social understanding of others.**

*(Continued)*

#### **Table 1 | Continued**


*(Continued)*

#### **Table 1 | Continued**


*PPI, psychophysiologic interaction analyses; DCM, dynamic causal modeling; ICA, independent component analysis; IDEA, iterative data-driven evolutionary algorithm; FC, functional connectivity; EC, effective connectivity; ASD, autism spectrum disorder; DTI, diffusion tensor imaging; MPFC, medial prefrontal cortex; vMPFC, ventral medial prefrontal cortex; dMPFC, dorsal medial prefrontal cortex; PCC, posterior cingulate cortex; ACC, anterior cingulate cortex; rACC, rostral anterior cingulate cortex; vACC, ventral anterior cingulate cortex; dACC, dorsal anterior cingulate cortex; OFC, orbital frontal cortex; TPJ, temporo-parietal junction area; IPS, intraparietal sulcus; MCC, midcingulate cortex; ParaCC, paracingulate cortex; AI, anterior insula; pSTS, posterior superior temporal sulcus; IFG, inferior frontal gyrus; MTG, middle temporal gyrus; PMdr, dorsolateral premotor cortex; AG, angular gyrus; SFG, superior frontal gyrus; FFA, fusiform face area; rIFGpo, the right pars opercularis of the inferior frontal gyrus; PHC, parahippocampal cortex.*

#### **THE DEFAULT MODE NETWORK**

The DMN is an anatomically defined brain system that preferentially activates when individuals are not focused on the external environment (Buckner et al., 2008). Core areas of the DMN include the medial posterior cortex [specifically the posterior cingulate cortex (PCC) and parts of the precuneus], medial prefrontal cortex (MPFC), as well as bilateral inferior parietal lobule (IPL) expanding to posterior temporal areas around the temporo-parietal junction (TPJ). Apart from these core areas, hippocampus and adjacent regions in the medial temporal lobe (MTL) and lateral temporal cortex (LTC) extending toward the temporal pole (TP) are also often reported as part of the DMN (Shulman et al., 1997; Buckner et al., 2008; Andrews-Hanna et al., 2010b) (see **Figure 1**).

The DMN was originally identified in a meta-analysis mapping brain areas that showed increased activity during passive tasks compared to active tasks in block-design positron emission tomography (PET) studies (Shulman et al., 1997). Three kinds of activity patterns within the DMN have been found since then. The first one is consistently decreased activity when subjects engage in goal-directed tasks as compared to control states (Gusnard and Raichle, 2001; Greicius et al., 2003); the second one is the high intrinsic activity during resting states with the eyes closed or visually fixating without engagement in any specific task (Raichle et al., 2001; Greicius et al., 2003; Beckmann et al., 2005); and the last one is the striking overlap between the DMN and regions activated in social cognitive tasks (Schilbach et al., 2008; Eickhoff et al., 2009).

So far, evidence have been found that brain regions within the DMN contribute to specialized functions organized into subsystems that converge on hubs. Buckner et al. (2008) pointed out that the DMN consisted of at least two interacting subsystems: the MTL subsystem containing both the hippocampal formation (HF) and parahippocampal cortex (PHC); and the core MPFC subsystem including the posterior cingulate/retrosplenial cortex (PCC/Rsp), ventral MPFC (vMPFC), and IPL. They proposed that the MTL subsystem was associated with mnemonic processes and activated during successful retrieval of old information, and the MPFC subsystem was activated in tasks requiring participants to engage in self-relevant mental simulations. Using

memories and associations from past experiences as its building blocks, the DMN participated in constructing self-relevant mental simulations that were exploited by a wide range of cognitive functions including remembering the past, thinking about the future, and conceiving the current viewpoint of others. Andrews-Hanna and colleagues further suggested that the DMN consisted of two subsystems that interacted with a common core system (see **Figure 1**): one was the dorsal MPFC (dMPFC) subsystem comprising the dMPFC, TPJ, LTC, and TP; and the other was the MTL subsystem comprising the HF, PHC, Rsp, vMPFC, and posterior IPL. The dMPFC subsystem was selectively activated when participants considered one's own and others' present mental states, whereas the MTL subsystem showed preferential activity when participants simulated the future using mnemonic imagery-based processes. Both of these two subsystems were strongly correlated with a midline common core system consisting of the anterior MPFC (aMPFC) and PCC, which is usually activated when people make self-relevant affective decisions. The midline core system interacted with the MTL subsystem and the dMPFC subsystem to facilitate the construction of mental models of personally significant events (Andrews-Hanna et al., 2010b).

## **MEASURING BRAIN CONNECTIVITY IN THE DMN**

An increasing number of researchers are interested in the brain connectivity among the DMN regions and have applied several newly developed approaches and methodologies to DMN studies. In the functional connectivity (FC) approach, researchers compute the statistical interrelation of neurophysiological time series representing temporal changes in different brain regions, and examine the stimulus-dependent and -independent synchronizations and interactions between these regions (Friston, 2005; Menon, 2011). In the effective connectivity (EC) approach, data can be obtained by dynamic causal modeling (DCM), which estimates and judges the negative or positive impacts of one region on another and how such impacts are affected by experimental context (Aertsen et al., 1989; Friston, 2002). Granger causality and other similar methods, unlike the bidirectional functional connectivity, which is a model-free concept, computes the unidirectional EC and emphasizes asymmetric causal interactions between neural systems. Granger causality estimates forward (bottom-up) vs. backward (top-down) connectivity between diverse regions. Nevertheless, it has been criticized for the lack of a biologically-based generative model and likelihood of obtaining pseudo estimated "causality" that is in fact induced by systematic differences across brain areas in hemodynamic lag (Friston, 2009; Smith et al., 2011). Functional and effective connectivity can be studied through both linear techniques (correlation coefficient, coherence) and non-linear techniques (phase synchronization, generalized synchronization) (Stam and van Straaten, 2012). It is worth noting that, negative correlations in brain connectivity analysis, sometimes referred to as anti-correlations, must be cautiously interpreted since they are usually present only after regressing whole-brain signals. This raises a point of controversy: whole-brain normalization leads to a bell-shaped correlation value distribution centered on zero, thereby guaranteeing negative correlations even if such correlations were not initially present in the data (Murphy et al., 2009; van Dijk et al., 2010). A brain network can also be defined on the basis of structural connectivity through Magnetic Resonance Imaging (MRI) morphology and Diffusion Tensor Imaging (DTI) tractography *in vivo* or tracer studies on postmortem tissue. Structural connectivity denotes a network of anatomical links and places constraints on which functional and effective interactions occur in the network (Bullmore and Sporns, 2009; Bressler and Menon, 2010; Menon, 2011).

## **BRAIN CONNECTIVITY STUDIES ON DMN AND SOCIAL UNDERSTANDING OF OTHERS EMOTION PERCEPTION**

Emotion plays a crucial role in human social cognition. Perceiving and interpreting other people's emotion status is one of the most important steps during social interaction. Traditional studies on the neural mechanism of emotion adopted a *locationist approach*, which asserted that each basic emotion faculty has its own specialized neural circuitry that is architecturally distinct, inborn, and shared with other animals (Panksepp, 2004). Early neuroimaging results were indeed congruent with this assumption, for example amygdala for fear (Adolphs et al., 1995), insula for disgust (Wicker et al., 2003), orbitofrontal cortex (OFC) for anger (Murphy et al., 2003), and subgenual anterior cingulate cortex (ACC) for sadness (Murphy et al., 2003). However, several recent meta-analyses and reviews favored the psychological *constructionist approach*, which suggested that a set of interacting brain regions involved in the basic psychological operations of both emotional and nonemotional processing were activated during emotion experience and perception (Lindquist and Barrett, 2012; Lindquist et al., 2012). Yet co-activation of different brain regions does not necessarily mean connectivity between them, so the evidence for the constructionist approach is inconclusive and brain connectivity results would be critical for examining this approach.

Most emotion perception studies using brain connectivity methods revealed changes between the DMN and other brain systems, especially between the prefrontal cortex and amygdala. In a gender discrimination task of angry and neutral faces, Passamonti et al. (2008) confirmed that the interaction between the ventral ACC and amygdala was influenced by the drive to obtain reward, with reduced negative connectivity in high reward-drive participants. The direction of this effect was limited to connection from the ventral ACC to the amygdala but not vice versa. Moreover, in another study, the rostral ACC was negatively coupled with the amygdala in high vs. low conflict resolution trials of a classic emotion Stroop task with fearful and happy faces, and the strength of the connectivity predicted successful conflict resolution (Etkin et al., 2006). Studies also found that the connectivity between different subregions of the MPFC and amygdala may make diverse effects on emotion function. For example, when people did a fear perception task, there was a dorsal-ventral division in ACC modulation of the thalamus-sensory cortex pathway, with a positive modulation of this pathway from dorsal ACC and a negative one from the ventral ACC (Das et al., 2005). In addition, Satterthwaite et al. (2011) demonstrated that the amygdala responded preferentially to threatening (fearful or angry) faces and had increased connectivity during threat trials with the OFC. Moreover, a study also showed that the neuroticism scores of subjects were negatively related to the left amygdala-ACC connectivity, but positively associated with the right amygdala-dorsomedial prefrontal connectivity, when processing negative emotional facial expressions (angry and fearful compared to neutral faces) (Cremers et al., 2010).

Besides the prefrontal cortex and amygdala, functional connectivity changes between other regions were also found in autism patients. For example, in a facial expression identification task, the healthy control group had significantly increased connectivity between the fusiform face area and PCC compared to autism patients (Kleinhans et al., 2008). In another study requiring subjects to passively view emotional facial expressions, typically developing children showed an anticorrelation between the right pars opercularis of the inferior frontal gyrus (rIFGpo) and the DMN, whereas autistic children showed a similar anticorrelated relationship between the rIFGpo and the posterior portion of the DMN, but not the anterior portion of the DMN (vMPFC) (Rudie et al., 2012).

General speaking, the FC in emotion perception studies concentrated on the relation between the vMPFC (including parts of ACC), and other emotion-related areas, mainly the amygdala and insula. The DMN has been theorized to make sensory inputs meaningful as "situated conceptualizations" for distinct emotions, since it reconstitutes past experiences for use in the present (Lindquist and Barrett, 2012; Lindquist et al., 2012). The vMPFC, as part of the DMN, is believed to receive reinforcement expectancy information from emotion learning systems that process the reinforcement provided by specific reinforcers of emotional expressions (Blair, 2007). Thus, the above results from functional connectivity in emotion perception may demonstrate that the vMPFC is indeed associated with successful regulation of human's emotional perception and responses.

## **EMPATHY**

Empathy can be defined as the process to generate an isomorphic affective state in the self to understand another individual's emotional state or condition while realizing that it is the other who causes this affective state (Decety and Svetlova, 2012; Engen and Singer, 2012). Neuroimaging of empathy is usually acquired by scanning people's brain when they fall into empathic states with various emotions such as disgust, reward, joy, and, particularly, pain (Jabbi et al., 2008; Singer et al., 2009; Bernhardt and Singer, 2012). Researchers proposed that at least three neural systems play vital roles in empathy: the mirror neuron system, the affective empathy system located in the anterior insula (AI) and midcingulate cortex (MCC), and the cognitive empathy system of theory of mind that almost overlaps with the DMN network. The affective empathy system and the cognitive empathy system are linked through the vMPFC (Walter, 2012).

Only a few empathy studies adopted brain connectivity methods to investigate the FC within the DMN, most of which were studying pain. For instance, although temporal correlation analysis demonstrated that the anterior DMN (aDMN) was deactivated in both the "Pain" and "No Pain" conditions compared to the resting-state, the decrease of connectivity was significantly stronger in the "No Pain" than "Pain" condition. In addition, independent component analysis (ICA) demonstrated that higher integration of the left medial OFC into the aDMN was associated with higher post-scan pain ratings (Otti et al., 2010).

Most of empathy studies focused on the connection between the DMN (e.g., MPFC) and other regions, especially the insula. When participants watched short videos of other people suffering painful injuries, the brain area of dMPFC and PCC showed greater connectivity with the dorsal ACC and AI than when participants received noxious thermal stimulation (Zaki et al., 2007). In another study, subjects were asked to view color photographs describing human body parts in painful or non-painful situations and then judge whether the person was suffering from pain or not. Results revealed that the frontoinsular cortex showed decreased FC with the superior MPFC in response to the painful compared to non-painful stimuli (Gu et al., 2010). Moreover, observing a friend experiencing social exclusion would trigger greater intensity of FC between the MPFC and both the dorsal ACC and bilateral insula than observing a stranger doing so (Meyer et al., 2013). Furthermore, Cheng et al. (2007) proposed that medical experts who applied painful procedures in their practice could regulate the unpleasant feelings generated by perceiving others in pain through modulating attentional demands. In accord with this hypothesis, experts showed negative FC between the MPFC and AI, whereas the controls showed no significant correlation with the MPFC.

As to the relationship between the amygdala and MPFC in empathy, studies found that the FC pattern between the amygdala and other brain areas was modulated by social context. For instance, the medial OFC and precuneus showed stronger covariation with the left amygdala when the visual stimulus was one person in a painful situation caused by another individual than when the situation was caused by accident (Akitsuki and Decety, 2009). Cox et al. (2012) argued that relative empathic ability (REA), the difference between affective empathy and cognitive empathy, is a useful index for empathy ability. Their results showed that the dominance of affective empathy was associated with stronger FC among social-emotional regions (ventral anterior insula, OFC, amygdala, perigenual ACC), whereas the dominance of cognitive empathy was related to stronger FC among areas implicated in social-cognitive regions (brainstem, STS, ventral anterior insula).

The FC differences found in empathy studies may reflect similar mechanisms as emotion perception, which involve the vMPFC's connection with the amygdala and insula (Akitsuki and Decety, 2009; Otti et al., 2010). Empathy has a deep evolutionary foundation stemming from the phylogenetically ancient practice of parental care, which assists the propagation of genetic legacy to future generations. The motivational systems originally developed to care for one's offspring have gradually been used to facilitate positive relationships between unrelated group members. Ultimately, empathy became a useful means of forming and maintaining strong social bonds between unrelated individuals (Decety et al., 2012b). By enabling human beings to feel the suffering of others, empathy can promote affective interactions and contribute to prosocial behaviors toward other conspecifics, depending on relevant social contexts and social relationships (Decety and Porges, 2011). Thus, it is very important for humans to identify the real protagonist of emotion—the one who causes this affective state. It follows that empathy, to a great extent, is based on emotion perception. Consistent with this line of thought, the region in the frontal cortex that is strongly implicated in both empathy and emotion perception is the aMPFC (Cheng et al., 2007; Otti et al., 2010; Cox et al., 2012; Meyer et al., 2013), which takes charge of the self-other distinction. There are also some other areas connected with the dMPFC (Zaki et al., 2007; Gu et al., 2010), which contribute to the recognition of other humans' mental states.

## **THEORY OF MIND**

Theory of mind refers to the ability to explain, predict, and interpret another person's behavior by attributing affective and cognitive mental states such as desires, beliefs, intentions and emotions to other people (Amodio and Frith, 2006; Abu-Akel and Shamay-Tsoory, 2011; Krause et al., 2012). The machinery of ToM involves at least three basic processes: representing cognitive and affective mental states, attributing these mental states to others, and finally applying (or deploying) these mental states to correctly comprehend and forecast behavior (Abu-Akel and Shamay-Tsoory, 2011). A number of neuroimaging studies have demonstrated the crucial role of the MPFC in ToM tasks (Northoff and Bermpohl, 2004; Uddin et al., 2007; Qin and Northoff, 2011). Some researchers also declared that ToM was subserved by the posterior DMN (pDMN) regions. For instance, Saxe argued that the right TPJ was vital for representing mental states, particularly false beliefs (Saxe, 2006), and Samson and colleagues proposed that the left TPJ (coupled with the frontal lobes) was crucial for the representation of mental states (Samson et al., 2004). In general, neuroimaging studies have identified a common pattern of brain activation underlying autobiographical memory, ToM, and the DMN (Fair et al., 2008; Spreng et al., 2009; Spreng and Grady, 2010).

Past ToM studies investigating the brain connectivity within the DMN revealed strong connections between the parietal and frontal cortex. For instance, Atique and colleagues compared the different patterns of functional connectivity between inferring another person's emotion (emotion mentalizing) and intention (intention mentalizing) in the DMN. The results revealed a double dissociation, such that a more anterior region of the right and left TPJ was more strongly activated during emotion mentalizing and showed stronger FC with the vMPFC, whereas a more posterior region was more strongly activated during intention mentalizing (Atique et al., 2011). Burnett and Blakemore found that an anterior rostral region of the MPFC (arMPFC) showed greater connectivity with the posterior superior temporal sulcus (pSTS) bordering on the TPJ and anterior temporal cortex during social emotion (such as embarrassment and guilt) than basic emotion, which was in line with the assumption that social emotions require the representation of another's mental states. They also found that the adolescent group possessed stronger connectivity between arMPFC and pSTS/TPJ during social vs. basic emotion than did the adult group (Burnett and Blakemore, 2009). Moreover, Mason et al. (2008) detected that the autism group had lower functional connectivity within the DMN network (between the left medial frontal gyrus and right TPJ) in the intentional inference condition than the control group. In addition, researchers found that when subjects made the decision to punish in-group members and out-group members for violating social norms (third-party punishment), the less in-group members were punished, the stronger was the FC between the dMPFC and left TPJ (Baumgartner et al., 2012).

Some other studies explored the connectivity between the DMN network and other regions during ToM processing. For example, in a study asking schizophrenia patients to infer the social interactions of two moving triangles, FC analyses showed that the degree of FC between task-positive (lateral frontotemporal network and insula) and task-negative (medial frontotemporal network and pDMN) regions was significantly reduced in schizophrenia patients as compared to controls (Das et al., 2012). Another study also detected that autistic patients had lower FC between the DMN (the left MPFC and the right TPJ) and a left hemisphere language network (the inferior frontal gyrus and posterior left middle temporal gyrus) in the intentional inference condition than the control group (Mason et al., 2008). Additionally, in a study using an affective speech comprehension task, researchers identified three functional modules with FC analysis, including a "medial" ToM network (the MPFC and TPJ regions), a bilateral "language" network (the inferior frontal and temporal areas), and the bilateral amygdala. The cooperation of these modules was observed during people's emotional speech comprehension, with the left angular gyrus playing a critical role when the medial network and the language network interacted (Herve et al., 2012). Furthermore, Lombardo et al. (2010)found that the vMPFC, PCC/precuneus, and TPJ all exhibited the same FC patterns during mentalizing of both self and others, which indicated that identical neural circuits were implementing processes involved in the mentalizing of both self and others.

To sum up, the main findings of ToM studies focused on the connection between the dMPFC and TPJ (Mason et al., 2008; Burnett and Blakemore, 2009; Baumgartner et al., 2012; Herve et al., 2012), with few studies on the FC between vMPFC (Lombardo et al., 2010; Atique et al., 2011) and aMPFC (Burnett and Blakemore, 2009), as well as some other regions, such as the insula and language network (Das et al., 2012; Herve et al., 2012). Relative to emotion perception and empathy, ToM is considered as a relatively high-level cognitive process (Gallagher and Frith, 2003; Amodio and Frith, 2006). Many species can predict the goals of others, while only humans and perhaps some nonhuman primates can separate one's own mental perspective from that of others (Van Overwalle, 2009; Van Overwalle and Baetens, 2009). The process of ToM critically involves self-projection, since we must imagine ourselves in the same situation as another person and use our own experiences to simulate and understand the mind of that person (Blakemore and Decety, 2001; Spreng et al., 2009; Spreng and Grady, 2010; Spreng and Mar, 2012). Hence, the ToM processes require not only representing current and mnemonic event materials, which mainly depends on the posterior hemisphere of the human brain, but also distinguishing self from others, which is the critical function of the frontal cortex. The involvement of dMPFC in ToM is perhaps due to its responsibility for evaluation and decision-making processes in self- and other-referential processing (van der Meer et al., 2010).

## **MORALITY**

Psychologists' interest in the moral dimensions of life and thoughts could date back to the dialogs of Plato and Aristotle's ethical treatises. In the recent 20 years, neuroscience has started a new era for moral psychology. Neuroimaging studies have found several brain regions related to morality, such as the ACC (Greene et al., 2004), TPJ (Young et al., 2007, 2011; Young and Saxe, 2008, 2009), vMPFC (Tangney et al., 2007; Zahn et al., 2009; Moll et al., 2011), and dorsolateral prefrontal cortex Greene et al., 2004, 2008. The distributed nature of the moral network led researchers to shift their focus from seeking domain-specific brain regions dedicated to morality to determining the contributions of domain-general processes to morality (Shenhav and Greene, 2010; Young and Dungan, 2011). The existing results show that the moral brain network is closely associated with the DMN (Buckner et al., 2008; Bzdok et al., 2012; Reniers et al., 2012).

Connectivities within the DMN have been found in some morality studies. Decety found that the adult group showed the strongest connectivity between the vMPFC and pSTS/TPJ during viewing of moral actions relative to non-moral actions when compared to other, younger groups (Decety et al., 2012a). Harrison et al. (2008a) compared the FC within the DMN when subjects were resting, judging moral dilemmas, or performing the Stroop task. They found that regions within the DMN, particularly the posterior and anterior cingulated cortex, showed greater correlated activity during the moral dilemma task compared to the resting state. Pujol and colleagues further discovered that, in contrast with control subjects, psychopathic individuals with documented histories of severe criminal offenses showed significantly reduced FC between the medial frontal cortex (aDMN) and posterior brain areas (pDMN) in the resting state (Pujol et al., 2012).

Due to the complexity of morality, researchers are also very interested in the relation between the DMN and other networks, particularly the amygdala. When categorizing illegal and legal behaviors in an implicit association moral judgment task, youths with psychopathic traits displayed reduced FC between the amygdala and the medial OFC compared with healthy controls (Marsh et al., 2011). Decety et al. (2012a) found a positive age-related increase of FC between the vMPFC and amygdala in response to intentional harm. Another study reported significantly reduced fractional anisotropy (FA), an indirect measure of microstructural integrity, in the uncinate fasciculus (white matter connections linking the amygdala and OFC) of psychopaths compared with controls (Craig et al., 2009). Cocaine-dependent subjects have been found to have less resting-state functional connectivity between the ACC, thalamus, insula and the brain stem compared with controls (Verdejo-Garcia et al., 2012). Furthermore, researchers also found that the strength of the coupling between the dorsolateral premotor cortex and the DMN was positively correlated with the impulsivity scores in juvenile offenders but negatively correlated with age in typically developing individuals (Shannon et al., 2011).

Moral judgment is one of the most complex social behaviors. It involves a variety of lower level cognitive processes, such as distinguishing between self and others, integrating social norms, computing goal-directed actions, showing empathy to others and inferring the intentions of others (Moll et al., 2008; Bzdok et al., 2012; Feldmanhall et al., 2013). Corresponding complexity has been shown in the above FC results. Moral judgment studies reported FC results that not only involved areas subserving emotion perception, empathy, ToM, but also other regions, such as the FC between the medial OFC and precentral sulcus (Decety et al., 2008), as well as the ACC and thalamus (Verdejo-Garcia et al., 2012). However, neuroimaging studies using brain connectivity methods are still scarce in the field of morality. Given the importance of moral judgment to society, high priority should be given to conducting more studies using the FC approach to further explore the neural mechanisms of morality.

## **DISCUSSION**

One of the consistent trends revealed in the above studies is that tasks from all the related fields of social understanding of others, from emotion perception to morality, elicit brain connectivity changes from the MPFC (extending to the ACC), a core region of the DMN, to other regions either inside (e.g., TPJ or PCC) or outside (e.g., insula or amygdala) of the DMN. Furthermore, more complex behaviors are subserved by brain regions which are situated higher in the frontal cortex. These results indicate that the MPFC plays a critical role in the social understanding of others, and different parts of MPFC take charge in distinct cognitive processes. According to Andrews-Hanna et al. (2010b), the MPFC can be divided into three subregions that belong to different subsystems of the DMN: the dMPFC in the dMPFC subsystem, the vMPFC in the MTL subsystem and the aMPFC in the midline common core system. The FC results reviewed in the current article provide support for the statements above.

## **CONNECTIVITY FROM THE vMPFC OF THE MTL SUBSYSTEM**

The vMPFC in the MTL subsystem is crucial in processing emotional features during social cognition. Connectivity changes between the vMPFC and other DMN regions (TPJ) have been found in ToM studies and morality studies. Atique and colleagues found that a more anterior region of the right and left TPJ showed strong FC with the vMPFC during emotion mentalizing (Atique et al., 2011). In contrast, Decety et al. (2012a) found an increase of FC between the vMPFC and pSTS/TPJ while viewing moral actions in adults compared to adolescents. The connection between the vMPFC and TPJ in these two fields can be attributed to the affective aspects of ToM that enables humans to infer emotions.

The dense connections between the vMPFC and emotional regions (e.g., amygdala, insula) means this frontal region can represent and regulate socioemotional states and synthesize a diverse range of information to represent affective mental states (Abu-Akel and Shamay-Tsoory, 2011). In all four fields, particularly emotion perception and empathy, the connectivity changes between the amygdala and vMPFC were repeatedly attested. The detection of connectivity between these two regions, to a certain extent, is consistent with discoveries in animal studies using fear conditioning paradigms which affirm that these regions play a critical role in the process of animal fear conditioning (Maren and Quirk, 2004; Jovanovic and Ressler, 2010; Fiorenza et al., 2012). Researchers have put forward a fear conditioning neuromechanism model, in which learning the conditioned responses in the central nucleus of the amygdala is modulated by two separate processes. One signals a positive prediction error from the basolateral amygdala, and another signals a negative prediction error from the vMPFC (Moustafa et al., 2013). This model is, in part, similar to the Integrated Emotion Systems (IES) model proposed by Blair (2007), which states that relatively independent emotion learning systems (e.g., the processing of fearful, sad and happy expressions in the amygdala, disgust expressions in the insula, as well as angry expressions in the inferior frontal cortex) input reinforcement expectancy information to the vMPFC while processing reinforcement provided by specific reinforcers of emotional expressions. The vMPFC represents the information and thus allows decision making, including moral decision making. The reduced connectivity between the MPFC and amygdala (Marsh et al., 2008; Glenn, 2011; Motzkin et al., 2011) instead of the insula and inferior frontal cortex in psychopaths relative to controls offers strong confirmation, as their impairments when processing care-based transgressions is thought to depend on the amygdala's role in the association of the transgression with the fear/sadness of the victim. Compared with the IES model, the amygdala-hippocampal-prefrontal interaction model includes the hippocampus, which is also frequently found in emotion related studies using functional connectivity methods (Kensinger and Corkin, 2004; Smith et al., 2006), and takes the effects of environment into account. However, there are still many open questions. For example, what is the actual role of the vMPFC? Does this region only signal a negative prediction error to the central nucleus of the amygdala, as Moustafa states, or does it play a part in successful decision making, as Blair asserts? How do other's emotions influence one's own moral decision?

## **CONNECTIVITY FROM THE aMPFC OF THE CORTICAL MIDLINE STRUCTURES**

The aMPFC and PCC are part of the core cortical midline structures (CMS) of the DMN, which mostly contributes to the elaboration of the experiential feelings of self (Northoff et al., 2006, 2011; Leech et al., 2011; Pearson et al., 2011; Qin and Northoff, 2011; Denny et al., 2012; Leech and Sharp, 2013). The aMPFC has been proposed to be critical in making self-other distinctions. For example, the aMPFC activates when participants make judgments or remember trait adjectives about themselves compared to other people (e.g., Kelley et al., 2002; D'Argembeau et al., 2005; Heatherton et al., 2006; Mitchell et al., 2006). The above results show the crucial role the aMPFC plays in processes of social behavior, especially empathy. For instance, medical experts who applied painful procedures in their practice showed negative FC between the MPFC and AI, while controls showed no significant correlation with the MPFC (Cheng et al., 2007). It could be interpreted that long-term practice allows the medical experts to regulate the unpleasant feelings through self-other discrimination to identify the real protagonist of pain. In addition, observing a friend experience social exclusion triggers greater intensity of FC between the MPFC and both the dorsal ACC and bilateral insula than observing a stranger doing so (Meyer et al., 2013). This result can be explained by the logic that the concept of friend, as compared to stranger, is closer to the self, thus social exclusion of a friend brings about greater FC.

## **CONNECTIVITY FROM THE dMPFC OF THE dMPFC SUBSYSTEM**

The main results of the reviewed studies with regards to the DMN are the associations between the dMPFC and TPJ in the dMPFC subsystem, which were present not only in ToM (mentalizing) but also in morality studies. Understanding complex social interactions among people who are presumed to be social, interactive, and emotive always involves the processing of selfreflective thoughts and judgments (Buckner et al., 2008). Thus it is not surprising that connections between the TPJ and dMPFC are commonly found in these studies, since these two areas are key regions known to be involved in inferring temporary goals, intentions, desires, and more enduring dispositions of others owing to previous localization results using the mentalizing paradigm (Gallagher and Frith, 2003; Mitchell et al., 2005; Hampton et al., 2008; Steinbeis and Koelsch, 2009; Van Overwalle and Baetens, 2009). For example, studies have shown that functional connectivity between the dMPFC and TPJ increased when healthy participants performed ToM tasks on social properties but decreased when autistic participants did (Mason et al., 2008; Burnett and Blakemore, 2009; Baumgartner et al., 2012).

Several different theories have been proposed to interpret the relationship between the dMPFC and TPJ (as well as other LTC regions such as pSTS). For example, it is suggested that the dMPFC is associated with the internally-focused process of considering the contents of another person's mind, whereas those temporal regions are related to externally-focused processes that do not require consideration of a target's internal states (Lieberman, 2007). Some researchers have argued that the TPJ is responsible for a domain-general computational mechanism for reorienting attention to the agency (e.g., other individual) and the MPFC is more domain specific for understanding human mental states (Decety and Lamm, 2007). Others have proposed that the TPJ is more specific for the, possibly uniquely, human ability to reason about others' affective and cognitive mental states, and the MPFC is more domain-general (Saxe, 2006). Thus the FC between the TPJ and the MPFC would be an index of either shifting between internally-focused and externally-focused processes or communication between domain-general and domain-specific processing during the understanding of others' mental states.

#### **CONNECTIVITY FROM OTHER REGIONS OF THE DMN**

Besides the MPFC and TPJ, several studies also revealed connectivity changes between the PCC/Precuneus in the CMS and other regions within and outside the DMN (Zaki et al., 2007; Harrison et al., 2008b; Assaf et al., 2010; Weng et al., 2010; Pujol et al., 2012). The PCC appears sensitive not only to explicit emotional engagement, for example, during tasks of emotional word processing and face-perception, but also implicit emotional engagement during self-directed attention or evaluation, as well as autobiographical memory Leech et al., 2011, 2012; Pearson et al., 2011; Leech and Sharp, 2013. Vogt et al. (2006) thus proposed that the PCC may respond to the general emotional content of events, particularly when the nature of processing is self-relevant.

In summary, during tasks from all four social fields, emotion perception, empathy, ToM, and moral judgments, connectivity changes were found between the MPFC and other regions within the DMN (e.g., TPJ, PCC) or outside the DMN (e.g., amygdala, insula). Evidence has shown that the MPFC is closely related to self-referential processing (Northoff et al., 2011; Wagner et al., 2012; Moran et al., 2013). The connectivity changes between the MPFC and other regions further confirm the viewpoint that humans use memories and associations from past experiences as the building blocks for understanding other's emotional and cognitive states. Furthermore, these studies suggest that different parts of the MPFC undertake distinct responsibilities. Specifically, connectivity changes between the emotion regions and vMPFC were repeatedly found in all four fields, particularly emotion perception and empathy; the aMPFC was found to be crucial, especially for empathy; and the associations between the dMPFC and TPJ were usually present in ToM (mentalizing) and morality studies. As social behaviors become more and more complex, the involvement of related regions in the medial frontal cortex gradually increased as well, which may reflect the transition of information processing from automatic to effortful cognitive processes. In consideration of all these findings, we propose that the vMPFC is engaged in identifying self-relevant information and assessing the salience of stimuli (Gusnard et al., 2001; Northoff and Bermpohl, 2004; Northoff et al., 2006); the aMPFC takes charge in making clear self-other distinctions (Andrews-Hanna et al., 2010b), and the dMPFC is involved in the evaluation and decision of whether a certain stimulus is applicable to the self or to another (van der Meer et al., 2010).

In addition to the MPFC regions, social understanding of others also includes cognitive processing for extracting existing storage and perceiving immediate material to represent current events, as well as for identifying and expressing the emotion itself. The former is closely related to the TPJ, which is believed to help in the establishment of a social context for a decision (Carter and Huettel, 2013), whereas the latter is managed by the amygdala, insula and other emotion regions. These three basic processes interact with each other and eventually lead to the formation of complex social behavior.

Reproducibility is a lingering issue with previous studies. For example, Andrews-Hanna and colleagues divided the MPFC into dMPFC, vMPFC, and aMPFC and proposed that they respectively belong to the dMPFC subsystem, MTL subsystem, and common core system (Andrews-Hanna et al., 2010a,b). van der Meer and colleagues further suggested that "the vMPFC is responsible for tagging information relevant for "self," whereas the dMPFC is responsible for evaluation and decision-making processes in selfand other-referential processing" (van der Meer et al., 2010). However, other studies did not emphasize the role of aMPFC, but instead showed that the vMPFC responds more to self, whereas the dMPFC responds more to others (Denny et al., 2012; Wagner et al., 2012). Compared with these studies, the present article specifically highlights the function of self-other distinction in the aMPFC for two main reasons: theoretically, there must be some transition from self to others and the aMPFC anatomically connects the vMPFC and dMPFC; in practice, as we have presented, this area has been repeatedly found to participate in the differentiating of self and others. However, to address the divergence and inconsistencies between studies, more brain connectivity methods such as those from graph theory, statistical physics, and non-linear dynamics should be put to use to confirm the relations and differences between the subregions of the MPFC and the DMN. Transcranial magnetic stimulation and transcranial direct-current stimulation should also be considered because they can provide causal evidence to evaluate the above theories.

## **CONCLUSION AND FUTURE DIRECTIONS**

In this article, we reviewed recent studies on the social understanding of others using brain connectivity methods. We focused on the brain connectivity within and outside the DMN in four different research fields: emotion perception, empathy, ToM, and morality. The reviewed studies suggest that the MPFC plays a key role in the social understanding of others, the subregions of the MPFC contribute differently to this function according to their roles in the different subsystems of the DMN, and more complex behaviors are related to anatomically higher regions in the frontal cortex. Starting from the bottom, the vMPFC in the MTL subsystem and its connection with emotion regions are mainly associated with emotion engagement during social interactions. Above the vMPFC, the aMPFC in the CMS and its connections with the PCC and ACC contribute mostly to making self-other distinctions. At the top, the dMPFC in the dMPFC subsystem and its connection with the TPJ are primarily associated with understanding others' mental states. Besides the MPFC and TPJ, the connectivities of the PCC also show some changes during tasks from the four social fields. These results indicate that the DMN is indispensable in the social understanding of others.

Several points require attention during future development of large-scale brain connectivity studies of social cognition. First of all, interest in brain connectivity arose from the study of brain lesions and neuropsychiatric disorders ranging from epilepsy to autism (Menon, 2011; Shafi et al., 2012). A rich body of literature on neuropsychiatric disorders suggest that abnormalities in the interactions of network components play a vital role in these disorders (Lytton, 2008; Vissers et al., 2012), and damage to specific functional connectivity networks can result in corresponding neuropsychopathy (Seeley et al., 2009). However, compared with lesions and patient studies, there are far fewer studies on healthy human participants applying the methods and theories of brain connectivity, let alone in the field of social cognition. This is a very promising approach for future work.

Secondly, most previous studies exploring the social brain in healthy participants only computed the functional or effective connectivity among regions of interest determined by prior experience or localization tasks, whereas a wide range of brain connectivity methods such as those from graph theory, statistical physics, and non-linear dynamics have been adopted in neuropsychiatric disorders studies (van den Heuvel and Hulshoff Pol, 2010; Menon, 2011; Xia and He, 2011; Stam and van Straaten, 2012; Yu et al., 2012; Griffa et al., 2013). Undoubtedly, these methods should be put to use to confirm the relations and differences between subregions in the MPFC or the DMN and deeply explore the complex social brain network in healthy participants.

Thirdly, so far most brain connectivity studies are conducted with fMRI, a technique based mainly on correlational evidence. However, investigating causality is the main goal of scientific studies, so building causal models accounting for the entire loop of social information processing within and between brains would be a promising future direction (Singer, 2012). Consequently, the methods for studying brain networks could be combined with many other methodologies, such as multivoxel pattern analyses (Carter et al., 2012), transcranial magnetic stimulation/transcranial direct-current stimulation (Young et al., 2010; Carter et al., 2012), genetic-imaging approaches (Glenn, 2011), and pharmacological interventions (Sripada et al., 2012) to explore the neural substrates of various human physiological and psychological states during social interaction.

#### **ACKNOWLEDGMENTS**

We thank Dr. Zheng Li for comments and suggestions. This work was supported by grants from the National Basic Research Program of China (2011CB711000, 2013CB837300), the National Natural Science Foundation of China (NSFC) (31170971, 61210010), and the Major Project of the National Social Science Foundation (12&ZD228) to Chao Liu and grants from the Major Project of the National Social Science Foundation (13&ZD155), Humanities and social science projects supported by Ministry of Education (13YJA190007), the Major Research plan of the National Natural Science Foundation of China (913241020,CNLYB1212) to Xiaoqin Mai.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 01 June 2013; accepted: 29 January 2014; published online: 24 February 2014.*

*Citation: Li W, Mai X and Liu C (2014) The default mode network and social understanding of others: what do brain connectivity studies tell us. Front. Hum. Neurosci. 8:74. doi: 10.3389/fnhum.2014.00074*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2014 Li, Mai and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## On development of functional brain connectivity in the young brain

#### *G. E. Anna-Jasmijn Hoff 1, M. P. Van den Heuvel <sup>2</sup> \*, Manon J. N. L. Benders 1, Karina J. Kersbergen1 and L. S. De Vries <sup>1</sup>*

*<sup>1</sup> Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, Netherlands*

*<sup>2</sup> Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Chandan J. Vaidya, Georgetown University, USA Wei Gao, University of North Carolina at Chapel Hill, USA*

#### *\*Correspondence:*

*M. P. Van den Heuvel, Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, Netherlands e-mail: m.p.vandenheuvel@ umcutrecht.nl*

Our brain is a complex network of structurally and functionally interconnected regions, shaped to efficiently process and integrate information. The development from a brain equipped with basic functionalities to an efficient network facilitating complex behavior starts during gestation and continues into adulthood. Resting-state functional MRI (rs-fMRI) enables the examination of developmental aspects of functional connectivity (FC) and functional brain networks. This review will discuss changes observed in the developing brain on the level of network FC from a gestational age of 20 weeks onwards. We discuss findings of resting-state fMRI studies showing that functional network development starts during gestation, creating a foundation for each of the resting-state networks (RSNs) to be established. Visual and sensorimotor areas are reported to develop first, with other networks, at different rates, increasing both in network connectivity and size over time. Reaching childhood, marked fine-tuning and specialization takes place in the regions necessary for higher-order cognitive functions.

**Keywords: resting-state functional MRI, functional connectivity, brain development**

## **INTRODUCTION**

The change from basic behavioral patterns during the first months after birth to being able to reason logically as an adult illustrates that development of the brain with age is very important. Although these cognitive manifestations of brain development are impressive, brain maturation may be even better appreciated by the macroscopic anatomical changes which the brain undergoes before birth. Considerable increases in both cortical folding and volume have been studied from 26 weeks gestational age (**Figure 1**) (Dubois et al., 2008; Ment et al., 2009). Even though this process continues also beyond the age of 2, important changes in cortical folding and volume are observed before 2 years of age. From the age of 2 onwards, both cognitive and behavioral development becomes more prominent, while the extent of macroscopic anatomical changes and myelination is fairly limited compared to changes before the age of 2 (Paus et al., 1999). Thus, development of these domains during late human development is more likely to rely on microstructural or functional changes (Yakovlev and Lecours, 1967; Paus et al., 1999).

For studying brain development, previous research has shed light on different aspects of the early developing brain, such as cerebral volume, cortical morphology, gray/white matter ratios, and brain metabolism (Chugani, 1998; Dubois et al., 2008; Hüppi, 2011). Studies have revealed several aspects of brain structure and function using techniques such as conventional MRI, diffusion-tensor imaging (DTI), positron emission tomography (PET), and electroencephalography (EEG) (Chugani, 1998; Smit et al., 2012; Vasung et al., 2013). An imaging technique to study the functional interactions between brain regions is resting-state functional MRI (rs-fMRI), which measures the level of correlation between endogenous brain signals. Evidence is emerging that this spontaneous activity is predominantly of a neuronal origin and can thus reflect functional connectivity (FC) within the brain (Fox and Raichle, 2007; Leopold and Maier, 2011). In case of a significant overlap of spontaneous activation patterns of two spatially distant brain regions, a level of FC is assumed and a so-called resting-state network (RSN) can be identified (Fox et al., 2005; Fox and Raichle, 2007). At least eight resting-state networks, among others motor, visual, attentional, and default-mode networks, have been described in adult humans (**Figure 2**) (Damoiseaux et al., 2006; Smith et al., 2009; Van den Heuvel and Hulshoff Pol, 2010). Also animal studies in rodents (Becerra et al., 2011; Jonckers et al., 2011) and monkeys (Hutchison et al., 2011; Mars et al., 2011) have shown analogous large-scale brain networks, validating concepts of functional network connectivity in humans.

This review will address some of the developmental changes that can be observed in the functional networks of the young brain using rs-fMRI. First, to illustrate how the early human brain develops qualitatively, connectivity patterns of preterm and full-term children up to the age of 2 will be discussed. After a concise description of late human brain development, i.e. from 2 years of age onwards, the focus will shift toward quantitative measurements of brain development. Lastly, a brief summary, a discussion of a number of technical limitations of rs-fMRI as well as future directions for research will be provided.

## **EARLY HUMAN DEVELOPMENT**

In recent years, early brain maturation has been studied by examining the resting-state dynamics in both prenatal and postnatal life. Recently, two studies have conducted fetal rs-fMRI showing that it may be possible to map FC of healthy fetuses (Schöpf et al., 2012; Thomason et al., 2013). Almost half of all bilateral functional networks could be identified *in utero* from 24 weeks gestational age onwards, with increasing connectivity strength toward full-term age (Thomason et al., 2013). Similar maturation effects have been observed in prematurely born infants (Fransson et al., 2007; Doria et al., 2010; Smyser et al., 2010). Five functional networks were indentified in extremely preterm to early preterm infants at term-equivalent age, encompassing the primary visual cortex, bilateral sensorimotor area, bilateral auditory cortex, precuneus area, lateral parietal cortex, cerebellum, and the medial and dorsolateral prefrontal cortex (**Figure 3**) (Fransson et al., 2007). Additionally, default-mode and executive control RSNs have been reported in very preterm to late preterm infants as well (Doria et al., 2010). One network in

the prematurely born infants (**Figure 3**, network **D**) could not be directly linked to a comparable network known in adults. Also, immaturity of the identified networks was characterized by less extension into the posterior-anterior direction compared to adult networks. Although contradicting results have been reported on lateralization of developing networks in which also unilateral networks have been described (Liu et al., 2008), more evidence seems to be present for bilateral FC patterns, already present at the neonatal stage (Fransson et al., 2007, 2009; Lin et al., 2008; Kelly et al., 2009; Gao et al., 2013). Based on the consistent findings of both fetal and neonatal rs-fMRI studies it can be hypothesized that the foundations of resting-state networks are already laid down before term age, with rapid neural growth in the last trimester of pregnancy (Doria et al., 2010).

The default-mode network (DMN) is a network that received considerable attention in FC studies in children. The adult DMN encompasses the posterior cingulate cortex (PCC), the precuneus, the medial prefrontal cortex (mPFC), the orbital frontal gyrus, the anterior cingulate cortex (ACC), the inferolateral temporal cortex, the parahippocampal gyrus as well as the bilateral parietal cortex (Raichle et al., 2001; Thomason et al., 2008; Van den Heuvel et al., 2009; Damaraju et al., 2010). Which exact collection of functions the network employs in humans has not yet been elucidated. However, the DMN is considered to be important for internally focused tasks, such as episodic memory, self-referential thought, and other social cognitive processes (Buckner et al., 2008; Uddin et al., 2010). Resting-state studies that could not detect a DMN in preterm infants have suggested the existence of a pre-DMN, or "proto-DMN" (Fransson et al., 2007, 2009, 2011; Doria et al., 2010; Power et al., 2010; Smyser et al., 2010). This network, composed of the bilateral parietal cortex and the precuneus/PCC, has been suggested to be a fragment of the posterior part of the adult DMN, forming the basics of the DMN at term age (Doria et al., 2010).

Elsevier (Ment et al., 2009).

on an axial T2-weighted template, with on the left hand side the left hemisphere. Colors indicate correlation strength, with increasing correlation strength toward the yellow part of the spectrum. **(A)** primary visual areas; **(B)** bilateral somatosensory and motor cortices; **(C)** bilateral temporal/inferior parietal cortex encompassing the primary auditory cortex; **(D)** posterior lateral and midline parts of the parietal cortex and lateral aspects of the cerebellum; **(E)** medial and lateral sections of the anterior prefrontal cortex. With permission reproduced from (Fransson et al., 2007). Copyright National Academy of Sciences, U.S.A. (2007).

Even though the frameworks of the DMN and other RSNs can be recognized at term age, some networks appear to be more developed than others. For example, FC of the visual and auditory networks is relatively mature compared to other networks in preterm infants around 36 weeks of gestation (Lin et al., 2008; Doria et al., 2010). Medial regions develop through different connectivity patterns as compared to lateral regions (Smyser et al., 2010). In these medial regions, such as in the anterior cingulate, an increase in interhemispheric connectivity can already be detected as early as 26 weeks post-menstrual age (PMA) (Smyser et al., 2010; Thomason et al., 2013). Seeds in lateral brain regions, for instance in the sensorimotor cortex, have connections that extend over a relatively large distance to their homotopic counterparts. Interhemispheric connectivity between these laterally located areas still cannot be detected by 38 weeks PMA (Smyser et al., 2010). They appear to first intensify local connection strength before the connection toward the homotopic counterpart is established (Smyser et al., 2010). Increasingly coherent interhemispheric activity and high thalamic FC during the period of accelerated neural development demonstrates the critical importance of this last period of pregnancy for brain network maturation (Smyser et al., 2010, 2011; Uddin et al., 2010; Thomason et al., 2013).

## **THE FIRST YEARS OF LIFE**

Next to different developmental rates of connectivity strength, development of network size appears to differ between networks as well. Changes in network size, represented by a percentage of brain volume, have been observed during the first years of life. Comparing infants of 2 weeks with infants at 2 years of age, several RSNs have shown to exhibit a significant increase in FC as well as cerebral volumes of cortical connectivity (Lin et al., 2008). However, the latter study showed that growth of network volume is not necessarily associated with a simultaneous increase in FC strength across all RSNs. Whereas FC strength of the sensorimotor RSNs is comparable to the visual RSNs, the sensorimotor network exhibits the most significant increase in volume between 2 weeks and 1 year, preceding the growth of the visual network, which occurs predominantly between 1 and 2 years of age (Lin et al., 2008). Lack of linear correlation between increases in connectivity strength and network size also applies to the DMN in the first 2 years (Gao et al., 2009). With a gradual increase in network size and a decrease in fragmentation during this period, it is possible that the more fragmented state of the DMN early in development could be a sign of immaturity, in which the network achieves adult-like properties around 1 year of age (Fair et al., 2008; Gao et al., 2009; de Bie et al., 2011). The dorsal attention network synchronizes in a similar way, although its configuration also seems to be influenced by network-network interactions with the DMN (Gao et al., 2013).

## **LATE HUMAN DEVELOPMENT**

From the age of 2 years onwards, neurodevelopment is characterized by a gain in higher-order cognitive abilities, such as attention and memory. Networks supporting these abilities show differences between children and adults, which is assumed to reflect a process of maturation. For example, EEG in healthy 5–8-year-old children has demonstrated that RSNs associated with higher-order cognitive functions, such as the DMN, cinguloopercular, ventral, and dorsal frontoparietal RSNs, still have a primitive architecture compared to adults (de Bie et al., 2011). Characteristics of such a primitive architecture are lower overall FC, weak within-network connectivity and presence of aberrant connections between distant brain areas as compared to adults. The sensorimotor area, which starts its development relatively early (de Bie et al., 2011), did not show these immature characteristics. Rather, FC of the sensorimotor area, which is similar to adults, may suggest mature-like characteristics of primary networks by the age of 7 (de Bie et al., 2011). As for the DMN, a more integrated network is found in teenagers and young adults (Fair et al., 2008, 2009). By the age of 10, each of the major regions of the DMN are present, with areas in the mPFC, the PCC, the left and right medial temporal lobe and the left and right angular gyrus (Supekar et al., 2010). Small spatial differences from adult patterns persist and FC of all RSNs, including the DMN, is still reduced. At 12 years of age, overall FC as well as network size is still decreased in comparison with adults (Jolles et al., 2011). However, areas associated with higher cognitive and emotional processing (for instance the executive control system, the dorsal attention system and the DMN) showed locally increased FC compared to the level of connectivity in these areas in adults. Hence, the basic configuration of RSNs may be subject to fine-tuning and specialization during the first years of adolescence, especially in the regions necessary for higher-order cognitive functions. Some studies have suggested that brain maturation may be reflected by a decrease in connectivity of short-range links and an increase of FC of long-range connections (Fair et al., 2007, 2008; Kelly et al., 2009; Supekar et al., 2009). Yet, caution is warranted regarding the interpretation of the results, as observed effects may at least be partly explained by effects of head motion, which has been shown to impose systemic effects on rs-fMRI measures (Power et al., 2012; Van Dijk et al., 2012).

## **QUANTITATIVE MEASUREMENTS OF BRAIN DEVELOPMENT: GRAPH THEORY**

Graph theory describes and quantifies complex whole-brain networks (for a comprehensive review of graph theory, see Bullmore and Sporns, 2009), which allows interpretation of different quantitative measurements into qualitative aspects of whole-brain organization.

Initially, communication between networks seems to be mostly localized to areas in close anatomical proximity (Fair et al., 2007, 2009; Supekar et al., 2009; Gao et al., 2011). During development, large-scale brain networks transform from a locally oriented organization to a more integrated topology (Fair et al., 2007, 2009; Supekar et al., 2009; Gao et al., 2011). The presence of "functional hubs" is an example of how graph theoretical measures may provide insight into functional cerebral architecture. Functional hubs, which are brain areas with a high FC density, are thought to be important for efficient neural signaling and integration of information (Buckner et al., 2009; Tomasi and Volkow, 2011). Cortical hubs and their related cortical networks in healthy, fullterm infants have been found to be bilaterally connected and mainly located in the homomodal primary sensorimotor, visual, and auditory brain regions (Fransson et al., 2011). As of the age of 2, the posterior cingulate cortex/retrosplenial (PCC/Rsp) connection exhibits considerable strength, which would make it suitable to function as a primary cortical hub within the developing DMN (Gao et al., 2009). With age, FC between hub and non-hub connections increases strongly, while connectivity between hubs remains relatively stable (Hwang et al., 2013).

## **METHODOLOGICAL CONSIDERATIONS**

Currently, the approaches most commonly used for the analysis of the rs-fMRI data are seed-based or region-of-interest (ROI) analyses, independent component analysis (ICA) and graph theory. The latter approach is used for describing properties of the functional connections rather than establishing them; some applications of graph theory have been discussed in the previous section. In ROI-based analysis the time series of a brain region of interest are correlated to time series of other brain regions. The functional connections of this predefined area of the brain, or seed, can thus be determined. Its relative simplicity may be a disadvantage, as whole-brain connectivity patterns—without predefined brain regions to correlate to—cannot be evaluated. Also, it can be more difficult to detect novel links (Gao et al., 2009). Alternatively, this limitation does not apply to ICA, which enables formation of a whole-brain connectivity map. Although an a priori hypothesis is not necessary to run the statistics, the interpretation of the analysis is more difficult than the ROIbased analysis method (Fox and Raichle, 2007; de Bie et al., 2011). When further comparing seed-based analysis and ICA, the resting-state data results appear to correspond fairly well (Gao et al., 2011; Rosazza and Minati, 2011). Yet, the level of correspondence slightly decreases when more components are included in the analysis (Rosazza and Minati, 2011). The application of different preprocessing steps enhances the heterogeneity among rs-fMRI studies, further complicating comparison and uniform interpretation of rs-fMRI studies (Lee et al., 2013). In addition, rs-fMRI data may also be subject to other confounding effects possibly leading to misinterpretation. Potential and unknown effects of other neurophysiological processes or sedation on the rs-fMRI data (Nallasamy and Tsao, 2011; Birn, 2012), as well as the already mentioned sensitivity to effects of motion (Power et al., 2012; Van Dijk et al., 2012), with reported intra-individual differences (Honey et al., 2009) could impinge on reproducibility and question the neuronal nature of observed developmental effects.

## **EFFECTS OF PREMATURITY ON FC**

Prematurity, especially in the context of a complicated postnatal course, may have adverse effects on gray matter volume, myelination, cerebral surface area, and overall cerebral volume (Hüppi et al., 1996; Inder et al., 2005; Kapellou et al., 2006). However, data on effects of prematurity on FC development are still limited. The networks identified in preterm infants have been reproduced in healthy full-term neonates, supporting at least similar RSN architecture (Fransson et al., 2009). In addition, despite small differences in the basal ganglia, the visual cortex and the cerebellum, no major differences have been reported between the anatomical locations of RSNs in preterm infants without serious postnatal complications and full-term controls (Damaraju et al., 2010; Doria et al., 2010; Smyser et al., 2010). The studies cited included prematurely born infants without acquired brain injury or early developmental problems and provided limited information on long-term neurodevelopmental outcome. Therefore, to what extent brain development of preterm infants may be different from full-term neonates warrants further exploration. Prematurely born infants with only minor cerebral abnormalities showed disruption in network architecture, especially in thalamo-cortical connections (Smyser et al., 2010). Compared to the networks of the full-term infant, preterm infants scanned at term-equivalent age had lower correlations and less connectivity between lateral seeds. Moreover, whereas infant born at term showed characteristics of a coherent network with possible DMN precursors, term-equivalent premature infants did not (Smyser et al., 2010). Follow-up data of preterm infants at 36 months of age showed lower overall connectivity compared to full-term peers (Damaraju et al., 2010). Long-term effects of premature birth on FC have been observed in young adulthood, in which alternative functional circuits involved in language have been described (Constable et al., 2013).

Summarizing, a number of studies have looked at resting-state dynamics in premature populations, but only limited data is available on possible effects of prematurity on FC development. More studies in both preterm and healthy term infants with long-term follow-up are therefore required to improve insights into brain development.

## **DIRECTIONS FOR FUTURE RESEARCH**

All of the aspects outlined in the previous section should be taken into consideration in the interpretation of developmental effects in rs-fMRI studies and also provide directions for further investigations. In addition, elucidating the structurefunction relationship of networks by further combination of imaging techniques could improve insights into the mechanisms behind functional network development. Similar maturational effects can be observed with DTI measuring structural parameters (Vasung et al., 2013) and arterial spin labeling MRI to map perfusion (De Vis et al., 2013). Furthermore, the translational aspect of developmental connectivity patterns toward executive functions and behavior merits attention as well, which ultimately may improve prediction of neurodevelopmental outcome or disease progression by rs-fMRI techniques. Considering that the last trimester of pregnancy may be especially relevant for adequate FC development (Doria et al., 2010), studying effects of prematurity related to both brain maturation and neurocognitive outcome could be an important application. Full-term infants with neonatal encephalopathy, in whom prediction of cognitive outcome is often difficult, may also be a relevant study population. In addition, a possible effect of gender on RSN patterns has been suggested (Biswal et al., 2010; Weissman-Fogel et al., 2010;

## **REFERENCES**


Zuo et al., 2010; Gong et al., 2011). Structural MRI studies have shown that in particular across puberty gradual sexual dimorphisms develop, with unknown relationships between gender, puberty and neural development (Blakemore, 2011). So far, only one study addressed gender differences in functional homotopy in children (Zuo et al., 2010). Therefore, to what extent gender effects might influence developmental patterns of FC in neonates, children or young adults remains unknown. Lastly, this review supports the notion that foundations of each of the RSNs are laid down before term age, after which fine-tuning and specialization of these networks take place. It has been suggested that a genetic substrate for functional networks may exist (e.g., Glahn et al., 2010). Twin-studies in children and adults indeed show significant effects of heritability, primarily on the level of whole-brain connectivity efficiency and functional organization (Fornito et al., 2011; Van den Heuvel et al., 2013). More research into the genetic control underlying functional network organization is needed, also toward possible identification of specific deficits in FC relevant for neuropsychiatric diseases, such as autism or schizophrenia.

## **CONCLUSION**

This review described developmental changes observed in the functional networks of the brain from 20 weeks of gestation onwards using rs-fMRI. Even though the techniques used to acquire and analyze rs-fMRI data leave room for improvement, all of the efforts so far have led to significant insights into brain development. Even before term age, a network with foundations of each of the RSNs can be recognized (Doria et al., 2010; Fransson et al., 2011). RSNs differ in their growth trajectories, but fine-tuning and specialization of RSNs is generally characterized by increasing FC, network volume, and coherence. The next step in developmental fMRI research may be to explore the origins of inter-individual network variation as well as associations with cognitive functioning and behavior by combining structure and function at different ages, in both healthy and diseased states.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 04 June 2013; accepted: 18 September 2013; published online: 08 October 2013.*

*Citation: Hoff GEAJ, Van den Heuvel MP, Benders MJNL, Kersbergen KJ and De Vries LS (2013) On development of functional brain connectivity in the young brain. Front. Hum. Neurosci. 7:650. doi: 10.3389/fnhum.2013.00650*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Hoff, Van den Heuvel, Benders, Kersbergen and De Vries. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## A review of attention-deficit/hyperactivity disorder from the perspective of brain networks

## *Angelica De La Fuente1, Shugao Xia 2, Craig Branch 2,3 and Xiaobo Li 2,3,4,5\**

*<sup>1</sup> Ferkauf Graduate School of Psychology, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA*

*<sup>2</sup> Gruss Magnetic Resonance Research Center, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA*

*<sup>3</sup> Department of Radiology, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA*

*<sup>4</sup> Department of Neuroscience, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA*

*<sup>5</sup> Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Jin Fan, Mount Sinai School of Medicine, USA Qingjiu Cao, Natural Science Foundation, China*

#### *\*Correspondence:*

*Xiaobo Li, Department of Radiology, Albert Einstein College of Medicine, Yeshiva University, 1300 Morris Park Avenue, Gruss 204, Bronx, NY 10461, USA.*

*e-mail: xli.aecom@gmail.com; xiaobo.li@einstein.yu.edu*

Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed neurodevelopmental disorder in childhood, which affects more than 5% of the population worldwide. ADHD is characterized by developmentally inappropriate behaviors of inattention, and/or impulsivity and hyperactivity.These behavioral manifestations contribute to diminished academic, occupational and social functioning, and have neurobiological bases. Neuronal deficits, especially in the attention and executive function processing networks, have been implicated in both children and adults with ADHD by using sophisticated structural and functional neuroimaging approaches. These structural and functional abnormalities in the brain networks have been associated with the impaired cognitive, affective, and motor behaviors seen in the disorder. The goal of this review is to summarize and integrate emerging themes from the existing neuroimaging connectivity studies based on advanced imaging techniques, applied in data of structural magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging, electroencephalography and event related potential; and to discuss the results of these studies when considering future directions for understanding pathophysiological mechanisms and developmental trajectories of the behavioral manifestations in ADHD. We conclude this review by suggesting that future research should put more effort on understanding the roles of the subcortical structures and their structural/functional pathways in ADHD.

#### **Keywords: ADHD, brain networks, structural MRI, fMRI, DTI, EEG/ERP**

## **INTRODUCTION**

Attention deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed neurodevelopmental disorder in childhood. Diagnoses of ADHD are made based on developmentally inappropriate behavioral symptoms, which have been categorized into three subtypes: inattentive, impulsive and hyperactive, and combined type. These core behavioral symptoms must be pervasive across situations, persistent for more than 6 months and observed before the age of 7 years, defined by the diagnostic and statistical manual of mental disorders (DSM-IV-TR) issued by the American Psychiatric Association (2000). The DSM-IV-TR reported that 3–7% of school-aged children have ADHD. However, most recent surveys have estimated significantly increased prevalence rates, from 6.9% in 1998 to 9.0% in 2009, shown in children aged 5–17 years (Akinbami et al., 2011).

Attention deficit/hyperactivity disorder is considered one of the most hereditable disorders with an estimated mean heritability of 75% (Faraone, 2005). Besides the genetic component, ADHD also has neurobiological and environmental underpinnings. The etiology of this highly inhomogeneous disorder is still unknown. Apart from the behavioral symptoms that are used for diagnostic measurements, both children and adults with ADHD have been found to have impairments in neural networks associated with sensory and cognitive processing functions. For instance, neuronal deficits in attention and executive function processing networks have been frequently reported in both children and adults with ADHD, by using sophisticated structural and functional neuroimaging approaches (Bush, 2005; Konrad and Eickhoff, 2010). And these structural and functional abnormalities in the brain have been associated with impaired cognitive, affective, and motor behaviors seen in ADHD approaches (Bush, 2005; Konrad and Eickhoff, 2010). In addition, spontaneous low-frequency functional activities have been reported in multiple brain regions, which formed the default mode network (DMN), during wakeful resting-state functional magnetic resonance imaging (fMRI) acquisition (Bartels and Zeki, 2005). Patients with ADHD have been reported to have both structural and functional abnormalities associated with the DMN (Konrad and Eickhoff, 2010; Castellanos, 2012).

In this study, we will review and summarize these existing studies, which have assessed the regional features and systems-level patterns of the structural and functional brain networks in ADHD, based on advanced computational techniques applied to data of

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structural MRI, diffusion tensor imaging (DTI), fMRI, electroencephalography (EEG), and event related potential (ERP). We will also discuss the results of these studies when considering future directions for understanding pathophysiological mechanisms and developmental trajectories of the behavioral manifestations in ADHD.

## **STRUCTURAL MRI-BASED BRAIN NETWORKS IN ADHD**

Structural MRI is the primary imaging tool used for studying brain anatomy and identifying changes in brain structures. Commonly used structural MRI measures include volume and density of the gray (GM) or white matter (WM) of the whole brain or sub-regions, as well as the regional and whole brain cortical GM thickness (Vaidya, 2012). Techniques for investigating the topological and structural features of the anatomical brain networks have also been developed by using regional volume or thickness as basic metric (Lerch et al., 2006; He and Evans, 2007; Zielinski et al., 2010).

The early estimations showed approximately 4–5% overall cerebral and cerebellar volumetric reductions in children and adolescents with ADHD, compared to that of typically developing controls (TDC; Castellanos, 2002; Carmona et al., 2005). Other structural MRI studies have reported volumetric reductions in the frontal lobe, [including orbitofrontal (OFC), superior frontal (SFC), and dorsolateral prefrontal (DLPFC) cortices], posterior and anterior cingulate gyri, precentral gyrus, caudate nuclei, corpus callosum (CC), as well as the cerebellum (Seidman et al., 2005; Shaw et al., 2006; Bush, 2011). Significantly reduced whole brain cortical GM thickness has also been found in children with ADHD when compared to TDC (Shaw et al., 2006, 2007; Makris et al., 2007). Studies also showed significantly thinner cortical thickness in regions including bilateral DLPFC and OFC, anterior and posterior cingulate cortex (PCC) and the temporooccipito-parietal junction, in adults with ADHD when compared to controls (Makris et al., 2007; Proal et al., 2011). The rate of cortical thinning in these regions has been shown to be inversely associated with the severity of hyperactivity and impulsiveness in normal development (Shaw, 2011).

Basal ganglia regions, such as the globus pallidus, putamen, and caudate have been reported to have structural abnormalities in children with ADHD. Structural MRI studies have detected reduced globus pallidus, putamen, and caudate volumes in voxelbased studies (Frodl and Skokauskas, 2012), and in manual tracing region of interest (ROI)-based deformation analysis (Qiu et al., 2009) in children with ADHD. Interestingly, they did not find any regional volumetric differences of the basal ganglia in adults with ADHD when compared to age-matched controls (Qiu et al., 2009; Frodl and Skokauskas, 2012). However, clinical studies found that the hyperactive/impulsive symptoms, observed in children with ADHD, significantly decline over time, whereas the inattentive symptoms rarely vanish (Lahey et al., 2005). Thus, the similar striatal volumes shown in the adults with ADHD and age-matched control may explain the vanished hyperactivity/impulsivity symptoms during the adulthood in many of the clinical cases. More cross-sectional and longitudinal investigations need to be done, to clarify the relationships among the striatum, its associated brain pathways, and the developmental trajectories of the disorder.

By now, most replicated findings from the voxel-based and ROI-based structural MRI studies have suggested significant decrease of the whole brain GM and WM volumes and significant regional underdevelopment in the prefrontal cortex (PFC), including the OFC, DLPFC, and SFC, basal ganglia substructures (striatum and globus pallidus), and cerebellum, in patients with ADHD (Frodl and Skokauskas, 2012). Structural changes, in the brain networks encompassing PFC and its connections to the striatum and cerebellum, have been found to be associated cognitive impairments, such as distractibility, forgetfulness, impulsivity, poor planning, and locomotor hyperactivity, in both children and adults with ADHD (Seidman et al., 2005; Arnsten, 2006).

From literature, structural MRI-based techniques for constructing the anatomical brain networks, such as in (Lerch et al., 2006; He and Evans, 2007; Zielinski et al., 2010), have not yet been implemented in cohorts with ADHD. Investigations of the topological features and pair-wise nodal communication patterns of the anatomical networks, and their relationships with the clinical and behavioral manifestations are important future directions in the research field related to ADHD.

## **FUNCTIONAL BRAIN NETWORKS IN ADHD**

Functional MRI techniques provide a way to understand normal brain functions and to test for regional brain dysfunctions associated with disorders (Bush,2005). Both task-based and resting-state fMRI have been frequently applied in children with ADHD, and have demonstrated atypical functional activations in the frontal, temporal, parietal lobes, and cerebellar regions (Shaw et al., 2006; Cubillo et al., 2010, 2011; Rubia et al., 2010). The frontal cortex can be divided into five major functional sub-regions: the orbital, dorsolateral, mesial (all which make up the PFC), the premotor, and motor regions. Social inhibition and impulse control are associated with the OFC (Fischer et al., 1990). Abnormal functional activations in OFC have been suggested to influence behavioral inhibition in children with ADHD (Bush, 2010). The DLPFC, another most frequently reported region of functional impairment in ADHD, has been implicated in planning, working memory and attentional processes (Danielson et al., 2011). In addition, one fMRI study conducted in adults with childhood ADHD showed reduced activations in bilateral inferior prefrontal cortices (IFC), left parietal lobe, caudate and thalamus, and reduced inter-regional functional connectivity between right inferior fronto-frontal, fronto-striatal, and fronto-parietal neural networks during a stop and switching task, when compared to controls (Cubillo et al., 2010).

Structures of the cingulo-fronto-pariental (CFP) cognitive/attention network, including the fronto-striatal and frontoparietal pathways, are thought to be the primary substrate for most attention and executive functions (Bush, 2011). The main regions that comprise the CFP network are the lateral frontal pole, dorsal anterior cingulate cortex (dACC), DLPFC, ventrolateral PFC (VLPFC), caudate, cerebellum, and the parietal cortex (Bush, 2010). This network controls goal-directed processes and provides the ability to respond to changing task demands (Castellanos, 2012). Significantly decreased activations have been reported in

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DLPFC,VLPFC, IFC, and superior parietal cortex (SPC) in ADHD, during multiple cognitive performance tasks and in resting-state (Rubia et al., 2010; Bush, 2011; Castellanos, 2012).

The dACC is an important component of the fronto-striatal circuitry of the CFP network, which has been consistently reported to have abnormal activation in ADHD (Sun et al., 2012). The dACC has a critical role in attention, cognitive processing, target detection, novelty detection, response selection, response inhibition, error detection, and motivation (Bush, 2010). Hypotheses about its functions include reward-based decision-making, response selection, error detection, and predicting task difficulty, which have shown to be impaired in children and adults with ADHD (Seidman et al., 2005). An attention task-based fMRI study found hypo-activation of the dACC in adults with ADHD when compared to controls (Bush, 2011). Resting-state fMRI studies frequently reported disrupted functional connectivity between the dACC and PCC (Castellanos et al., 2008), and abnormal developmental pattern of the dACC–DMN interactions in ADHD subjects (Fair, 2010; Sun et al., 2012). The atypical connectivity in ADHD may relate to delayed or disrupted maturation. ADHD adults presented with abnormal dACC-PCC connectivity patterns when compared to age-matched TD adults. Connectivity patterns were similar between the ADHD group and the young TD subjects, indicating atypical brain maturation in the ADHD group (Sato, 2012). In addition, significantly increased functional connectivity between the dACC and the bilateral thalamus, bilateral cerebellum, and bilateral insula have been shown during resting-state in children with ADHD, compared to TDC (Tian, 2006).

The thalamus is a key subcortical structure of the corticostriato-thalamo-cortical (CSTC) loops that serve attentional and cognitive processing. Significantly reduced regional activations in bilateral thalami (especially in the pulvinar nuclei), significantly decreased functional connectivity between bilateral pulvinar and right prefrontal regions, and significantly increased connectivity between the right pulvinar and bilateral occipital regions have been reported in children with ADHD, during a visual sustained attention task-based fMRI study (Li et al., 2012). Another study has found reduced functional connectivity between thalamus and basal ganglia areas (especially in putamen) in children withADHD, during resting-state (Cao et al., 2009).

Altered topological features and inter-regional functional connectivity in large-scale brain networks encompassing cortical and subcortical regions have been increasingly reported, indicating systematic and more widespread brain alterations in ADHD. Resting-state fMRI has been used across laboratories to identify neural networks such as the DMN, dorsal, and ventral attentional networks, as well as motor, visual, and executive control systems (Fox et al., 2006; Buckner et al., 2008; Castellanos, 2012). The DMN is a distributed network of brain regions, which is more active during rest than during performance of sensory and cognitive demanding tasks. Studies have found significantly decreased functional connectivity among the brain regions of the DMN, and between those with putamen and thalamus (Cao et al., 2009; Qiu et al., 2011). Incremental deactivations of the regions in the DMN have been associated with increased task difficulty as well as during transition from rest-to-task states in ADHD (Konrad and Eickhoff, 2010; Liddle, 2011). Furthermore, by applying the graph theoretical approach (GTT), which has been used to characterize the topology of global and regional brain communications (Konrad and Eickhoff, 2010; Ahmadlou et al., 2012), a resting-state fMRI study found significantly increased local efficiency combined with a decreasing tendency in global efficiency of the DMN, and significantly decreased nodal efficiency in the medial prefrontal, temporal, occipital, and subcortical regions in children with ADHD, when compared to the control group (Wang, 2009). Castellanos (2012) have suggested that ADHD could be considered as a DMN disorder. In addition, a resting-state fMRI study, by running network based statistics (NBS) in 90 cortical and subcortical regions, demonstrated abnormal inter-regional connectivity of the frontal-amygdala-occipital network and frontal-temporaloccipital network in young adults with ADHD (Cocchi, 2012). Impaired inter-regional connectivity within reward-motivation regions and their decreased connectivity with regions from the DMN and dorsal attentional networks have also been reported, and suggest impaired interactions between control and reward pathways that might underlie attention and motivation deficits in ADHD (Tomasi and Volkow, 2012).

Altered topological features and inter-regional functional connectivity in large-scale brain networks in ADHD, which have been reviewed in the fMRI section, are convinced in EEG/ERP studies as well. A GTT-based study in resting-state EEG data reported abnormal cluster coefficients and path lengths of the nodes in the left hemisphere, which were recognizable in the delta band, in patients with ADHD when compared to controls (Ahmadlou et al., 2012). An NBS study in sustained attention task-based EEG data showed distinct frontal-central-parietal patterns in the theta and alpha frequency bands in adults with ADHD compared to controls, where ADHD subjects displayed a more robust homogeneous response pattern in the 120–260 ms time range that included the P1, N1, P2 component, with a majority of latency peaks characterized by alpha and theta activation in the fronto-central sites (Shahaf et al., 2012). Similar findings have been interpreted as revealing a compensatory mechanism activated by ADHD patients in early stages of stimulus processing, by which more attention was directed to the task (Prox et al., 2007).

## **DTI-BASED BRAIN NETWORKS IN ADHD**

While functional brain imaging studies may reveal specific regions of dysfunction within the brain, it is important to know how the nodes within these networks are structurally connected. Micro-structural abnormalities in the WM may lead to disrupted functional communications between brain regions, ultimately resulting in disrupted behavioral functioning in ADHD (Nagel, 2011). DTI is an MRI method that provides *in vivo* information about the WM microstructure through water diffusion, which can reveal microscopic details about tissue architecture (Konrad and Eickhoff, 2010). Orientations of the WM tracts in the brain can be measured by the directions of diffusivity (Konrad and Eickhoff, 2010). The most common quantitative indices used to measure the WM integrity are mean diffusivity (MD) and fractional anisotropy (FA; Konrad and Eickhoff, 2010; Nagel, 2011).

Two primary analysis methods have been applied in DTI studies: voxel-based analysis (VBA) that allows for a complete overview of the WM integrity in the brain, and ROI-based analysis for

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more specific exploration of the WM abnormalities in certain brain regions. A recent meta-analysis reviewed the ROI-based studies assessing the WM integrity, and provided evidence of several disturbed WM regions in children with ADHD, including the inferior and superior longitudinal fasciculus, anterior corona radiate, cortico-spinal tract, cingulum, CC, internal capsule, caudate nucleus, and cerebellum (van Ewijk, 2012). Review of the VBA studies also confirmed WM changes in these regions, and found extensive differences across the four brain lobes, as well as areas within the basal ganglia, uncinate fasciculus, and forceps minor (van Ewijk, 2012).

Development of WM determinative and probabilistic tractography techniques has made it possible to estimate and visualize the structural connectivity of the WM pathways in human brain. Using tractography-based analyses, DTI studies have demonstrated increased FA inWM structures connecting parietaloccipital regions (Silk, 2009), and tracts connecting the temporal lobe and other distant cortical regions in children with ADHD compared to TDC, which were positively associated with symptom severity in the patient group (Peterson, 2011). Significantly reduced FA in the cortico-spinal tract (Carmona et al., 2005; Hamilton et al., 2008; Cubillo, 2010), the superior longitudinal fascicle that connects the prefrontal and parietal regions (Makris, 2008; Cubillo et al., 2010; Konrad, 2010), the cingulum bundle (Makris, 2008; Konrad, 2010), have also been reported in patients with ADHD. In addition, one study detected significantly increased MD in the frontal portion of the left fronto-occipital fasciculus in adults with ADHD when compared with controls (Konrad et al., 2006). DTI studies have also shown alterations within the cerebellar WM areas in children and adolescents with ADHD (Ashtari et al., 2005; Bechtel et al., 2009).

The neural pathways that are associated with the areas of abnormal WM reviewed above are the pathways connecting the cortical regions, cortical-striatum and cortical-cerebellum. The prevailing theory regarding the neurobiological basis of ADHD identified the fronto-striatal network as a probable substrate of cognitive and behavioral impairments seen in ADHD (Bush, 2005; van Ewijk, 2012). Studies found disturbed WM structural connectivity of the frontal-striatal network in both adults and children with ADHD, compared to group-matched controls (Konrad and Eickhoff, 2010; Tamm et al., 2012). Tract- specific analyses found reduced FA in bilateral fronto-striatal fiber tracts in children with ADHD, specifically in the orbitofrontal and ventrolateral tracts, associated with poor executive functioning performance (Shang, 2013). In addition, significant reductions of probabilistic WM connectivity between the thalamus and striatum has been reported in children with ADHD when compared to TDC (Xia et al., 2012).

The CC, the largest band of WM fibers in the brain that connects the left and right hemispheres, plays an important role in inter-hemispheric communication. Thus, abnormal microstructure of CC may affect cognitive functions that depend on bilateral collaboration (Vaidya, 2012). Across studies, children with ADHD showed reduced volume of the splenium, the posterior region of the CC that connects bilateral parieto–temporal cortices (Valera, 2007). Using both DTI and anatomical MRI, one study found microstructure abnormalities in the isthmus/splenium part of the CC, characterized by reduced FA values in adults withADHD when compared to healthy controls (Dramsdahl, 2012). These results are in line with two earlier studies, which observed reduced FA in the isthmus in children with ADHD (Chao, 2009; Cao, 2010).

## **DISCUSSIONS**

Attention-deficit/hyperactivity disorder is the most common neurodevelopmental disorder in childhood. Neuroimaging studies have attempted to identify the pathophysiology of the disorder by searching for abnormalities in brain regions and their connections that are involved in attention, executive function, motor control, response inhibition, working memory, and even during rest. We reviewed and summarized the important findings from the structural MRI, fMRI, EEG/ERP, and DTI studies, which have provided the abundant evidence of structural and functional alterations in widespread brain regions and their connections, in this severe and heritable brain disorder.

The majority of the existing neuroimaging studies, which have attempted to find the neurobiological underpinnings of ADHD, have focused on cortical regions and their connections, and has demonstrated global cortical maturation delay based on reduced cortical thickness and reduced GM and WM volumes, specifically in frontal lobe (Carmona et al., 2005), regional WM microstructural abnormalities in frontal, temporal and parietal lobes (Nagel, 2011; Dramsdahl, 2012; Shang, 2013), and aberrant neuronal activations, inter-regional functional connectivity and global network features over these cortical areas, during sensory and cognitive performance or even at rest (Wang, 2009; Ahmadlou et al., 2012; Castellanos, 2012; Cocchi, 2012; Shahaf et al., 2012). Furthermore, the existing studies also suggest that the structural and functional connectivity deficits and the ADHD symptoms might arise incidentally from a common etiologic mechanism, involving altered modulation of synaptic potentiating and pruning by dopamine and other factors during development, which result in altered patterns of cortico-cortical connectivity that might persist into adulthood (Liston et al., 2011).

Subcortical regions may also significantly contribute to the pathophysiology of ADHD. For example, the basal ganglia has been associated with the execution of appropriate goal-directed behaviors and may play a role in the behavioral impairments for response control seen in many children with ADHD (Qiu et al., 2009). Neuroimaging studies have demonstrated regional structural and functional deficits of the basal ganglia, especially in the striatum (Qiu et al., 2009; van Ewijk, 2012). Disturbed WM structural connectivity and atypical functional connectivity have been shown in the frontal-striatal network in both adults and children with ADHD (Konrad and Eickhoff, 2010; Tamm et al., 2012). It has been hypothesized that the impairments of the striatum and its brain connections are associated with the hyperactivity/impulsivity component in children with ADHD (van Ewijk, 2012).

However, the role of the thalamus and its mediating role in cortico-striatal and cortico-cortical pathways in ADHD have been relatively ignored. Very recently, investigation of high resolution structural MRI data revealed reduced bilateral thalamic volumes, as well as regional surface atrophy in the pulvinar nucleus of the left side thalamus in children with ADHD (Xia et al., 2012). In the same study, disturbed frontal-thalamo and thalamo-striatal WM

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connectivity have also been demonstrated in the children with ADHD. Furthermore, significantly reduced pulvinar activations and abnormal pulvinar-frontal and occipital-pulvinar functional connectivity have been shown in children with ADHD during a visual sustained attention task, which were also significantly correlated with their inattentiveness indices for clinical diagnoses (Li et al., 2012). This series of neuroimaging studies may drive the field forward by placing the pulvinar nuclei of the thalamus at the center of dysfunctional attentional networks in ADHD (Shaw, 2012).

As a summary, brain network associated dysfunctions have been found to be central in ADHD pathophysiology. It is important to gain a better understanding of how subcortical-cortical

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## **ACKNOWLEDGMENTS**

This work was partially supported by the Rose F. Kennedy Intellectual and Developmental Disabilities Research Center (RFK-IDDRC) through a program grant (HD071593) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). It was also partially supported by the RFK-IDDRC Pilot and Feasibility Award to Dr. Xiaobo Li.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 01 February 2013; accepted: 26 April 2013; published online: 15 May 2013.*

*Citation: De La Fuente A, Xia S, Branch C and Li X (2013) A review of attention-deficit/hyperactivity disorder from the perspective of brain networks. Front. Hum. Neurosci. 7:192. doi: 10.3389/fnhum.2013.00192*

*Copyright © 2013 De La Fuente, Xia, Branch and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

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## Imaging structural and functional brain networks in temporal lobe epilepsy

## *Boris C. Bernhardt 1,2\*, SeokJun Hong1, Andrea Bernasconi <sup>1</sup> and Neda Bernasconi <sup>1</sup>*

*<sup>1</sup> Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada <sup>2</sup> Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Qingbao Yu, The Mind Research Network, USA Xiaobo Li, Albert Einstein College of Medicine, USA*

#### *\*Correspondence:*

*Boris C. Bernhardt, Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, H3A2B4, Montreal, QC, Canada e-mail: boris@bic.mni.mcgill.ca*

Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks *in vivo*. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

**Keywords: TLE, connectivity, MRI, graph-theory, connectome**

## **INTRODUCTION**

Epilepsy is one of the most prevalent neurological disorders, affecting ∼1% of the general population. Of patients treated with antiepileptic drugs, about one third never achieve remission. Drug-resistance should be identified early and treated effectively, as uncontrolled epilepsy is harmful to the brain, has devastating socio-economic consequences, and is associated with increased mortality (Leonardi and Ustun, 2002; Pugliatti et al., 2007; Tellez-Zenteno et al., 2007; Cascino, 2009; Coan and Cendes, 2013). Epilepsy is broadly defined by a state of recurrent spontaneous seizures, which arise when balance between excitation and inhibition is disrupted (Scharfman, 2007; Engel et al., 2013). Compelling evidence from animal models, experimental paradigms, and clinical work in humans indicates that specific cortical and subcortical networks play a fundamental role in the genesis and expression of seizures (Avoli and Gloor, 1982; Bear et al., 1996; Bertram, 1997; Spencer, 2002).

Temporal lobe epilepsy (TLE) is the most common drugresistant epilepsy in adults. TLE is traditionally associated with mesiotemporal sclerosis, defined by cell loss and gliosis in the hippocampus, entorhinal cortex, and amygdala. Surgical resection of this epileptogenic lesion is the current treatment of choice (Wiebe et al., 2001), and leads to freedom from seizures in the majority of cases. Nevertheless, even in carefully selected cases, ∼30% of surgical candidates continue to have seizures (Mcintosh et al., 2004; Bernhardt et al., 2010). Although reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed neural networks (Spencer, 2002; Bonilha et al., 2007b; Elsharkawy et al., 2009; Engel et al., 2013).

In the past decade, advances in imaging acquisition and postprocessing have permitted *in vivo* mapping of the regional distribution of network abnormalities in TLE patients. In particular, quantitative structural MRI studies based on volumetry, voxel-based morphometry, cortical thickness mapping, and structural covariance analysis have shown widespread, coordinated, and progressive cortical gray matter loss in temporal and extra-temporal regions, such as the thalamus, fronto-limbic, and fronto-central neocortices (Bernasconi et al., 2003b, 2004; Natsume et al., 2003; Bonilha et al., 2004; Lin et al., 2007; Bernhardt et al., 2008, 2009, 2010, 2012; Keller and Roberts, 2008; Mcdonald et al., 2008b,c). Findings of gray matter alterations have been complemented by diffusion MRI data of the white matter. These studies have shown disruptions in inter-regional fiber diffusivity both within and beyond mesiotemporal and temporo-limbic networks, suggestive of decreased fiber arrangement and altered myelin membranes (Concha et al., 2005; Rodrigo et al., 2007; Yogarajah and Duncan, 2008; Ahmadi et al., 2009). Furthermore, studies based on both electrophysiological techniques as well as functional MRI have provided evidence for region-specific shifts in intrinsic functional networks (Bettus et al., 2009; Voets et al., 2012). More recently, reports of disruptions of inter-regional structural and functional connections in TLE have been complemented by graphtheoretical approaches (Liao et al., 2010; Bernhardt et al., 2011; Bonilha et al., 2012). These techniques, derived from complex system analysis, lend tools to characterize topological aspects that relate to the specialization and integration of inter-connected brain networks (Watts and Strogatz, 1998; Sporns et al., 2004; Bullmore and Sporns, 2009; Guye et al., 2010). In TLE, such approaches provide a novel window to study connectivity, and have begun showing alterations in higher-order network configurations.

The aim of this review is to summarize the current state of imaging evidence for network abnormalities in TLE. We will first outline findings that have provided insights into the topographical extent of regional structural abnormalities in TLE. We will then discuss studies on low-level inter-regional abnormalities, using connectivity mapping techniques such as seed-based structural MRI covariance, functional MRI connectivity, and diffusion MRI tractography. Subsequently, we will discuss graph-theoretical analyses to address the topological organization of brain networks in TLE. We will conclude by commenting on the potential clinical relevance of current network-based MRI analysis in TLE.

## **REGIONAL PATTERNS OF STRUCTURAL PATHOLOGY**

The hallmark lesion of TLE is hippocampal sclerosis. This lesion is characterized by various degrees of neuronal loss and gliosis within hippocampal subfields and the dentate gyrus (Sommer, 1880; Babb and Brown, 1987; Blumcke et al., 2002). In addition, up to 50% of TLE patients may show intense reorganization of neuronal networks, manifested by granule cell dispersion (Houser et al., 1992; Blumcke et al., 2002), selective loss of inhibitory neurons (De Lanerolle et al., 1989), as well as axonal sprouting (Babb et al., 1991). Histological reports of TLE patients and animal models of limbic epilepsy have consistently demonstrated that pathology is not limited to the hippocampus. Indeed, cell loss and gliosis may be found in proximal and even more distal temporo-limbic regions, including the amygdala (Yilmazer-Hanke et al., 2000), entorhinal cortex (Du et al., 1993, 1995), temporopolar (Choi et al., 1999; Meiners et al., 1999; Mitchell et al., 1999; Bothwell et al., 2001) and lateral temporal neocortices (Cavanagh and Meyer, 1956; Falconer et al., 1964; Turski et al., 1983; Clifford et al., 1987; Kuzniecky et al., 1987; Cavalheiro et al., 1991; Thom et al., 2009), as well as the thalamus (Turski et al., 1983; Clifford et al., 1987; Bertram et al., 2001; Sloan and Bertram, 2009). In animal models, tissue damage has been shown in extra-temporal neocortical regions, such as sensorimotor cortex, piriform, perirhinal, retrosplenial, and visual cortices.

In humans, pathological data in regions remote from the temporal lobes in TLE is sparse. This is, in part, due to difficulties in obtaining immediate *postmortem* specimens and the surgical approach tailored to the temporal lobe. In their seminal postmortem study, Margerison and Corsellis described neuronal loss and gliosis in frontal and occipital cortices in about 20% of patients (Margerison and Corsellis, 1966). More recent autopsy reports have confirmed and further extended these observations by showing varying degrees of architectural abnormalities involving virtually all lobes (Eriksson et al., 2002; Blanc et al., 2011).

A large body of electro-clinical work suggests that the epileptogenic network in TLE is broad. Seizure activity may involve not only the hippocampus, but also several other subcortical and cortical structures, including the amygdala, entorhinal cortex, lateral temporal, inferior, as well as orbitofrontal cortices (Lieb et al., 1987, 1991) together with the medial thalamus (Cassidy and Gale, 1998; Rosenberg et al., 2006). The close spatial correspondence between histopathological alterations and electrophysiological anomalies in TLE has provided a strong motivation to study structural brain changes, which have been of a high clinical and scientific value in mapping causes and consequences of drugresistant epilepsy. In particular, quantitative Magnetic Resonance Imaging (MRI) analysis has offered a unique perspective to study structural substrates of TLE *in vivo* and to gain further insights into their spatial patterns and clinical correlates (See **Figure 1**, for a schematic overview of structural MRI findings in TLE). Studies based on manual volumetric MRI analysis largely confirmed previous histological assessments, and provided a more comprehensive picture of the regional extent of structural abnormalities in TLE. Volumetric analysis demonstrated atrophy in multiple limbic structures, including the hippocampus, entorhinal cortex, amygdala (Cendes et al., 1993a,b; Bernasconi et al., 2001, 2003a,b), temporopolar, perirhinal, lateral temporal neocortices (Jutila et al., 2001; Moran et al., 2001; Sankar et al., 2008), and the thalamus (Dreifuss et al., 2001; Natsume et al., 2003; Bernhardt et al., 2012). In the hippocampus and thalamus, surface shape mapping has furthermore allowed localizing structural anomalies at a subregional level (Hogan et al., 2004; Kim et al., 2008; Bernhardt et al., 2012, in press). In the thalamus, for example, we found volume loss located primarily in mediodorsal segments (Bernhardt et al., 2012). Quantitative MRI postprocessing techniques, such as voxel-based morphometry (Bernasconi et al., 2004; Bonilha et al., 2004; Keller and Roberts, 2008) and analyses of cortical thickness have shown that TLE is associated with extensive regional neocortical abnormalities, encompassing not only mesiotemporal structures, but also prefrontal, fronto-central, cingulate, occipito-temporal, and lateral temporal neocortices (Lin et al., 2007; Bernhardt et al., 2008, 2009, 2010, 2012; Mcdonald et al., 2008c; Mueller et al., 2009b; Kemmotsu et al., 2011; Voets et al., 2011). Although the exact biological underpinnings of gray matter loss in different brain regions are not clear, they likely reflect a combination of neuronal loss and synaptic reorganization (Cascino et al., 1991; Sanabria et al., 2002; Blanc et al., 2011), possibly secondary to seizures (Sutula et al., 1988; Holmes, 2002; Cavazos et al., 2003). These findings have increased our understanding of whole-brain pathology associated with TLE. On the

other hand, new techniques such as hippocampal and thalamic surface-shape mapping (Kim et al., 2008; Bernhardt et al., 2012) have allowed searching for fine-grained, subregional structural anomalies within temporo-limbic seizure networks. Importantly, the MRI-derived knowledge of pathology is in overall agreement with data from animal models and *ex vivo* studies. These studies collectively support the concept of TLE as a disorder of distributed neural networks.

## **STRUCTURAL NETWORKS**

Quantitative structural studies have provided a comprehensive mapping of structural pathology in TLE. Nevertheless, the commonly applied mass-univariate group comparisons provide only a snapshot of putative network abnormalities in TLE. Indeed, while such topographic maps may localize an ensemble of affected regions, they do not directly address how these regions inter-relate.

The term *structural connectivity* refers to anatomical associations between brain regions, defining the actual physical wiring (Stephan et al., 2000; Stone and Kotter, 2002; Sporns et al., 2005; Sporns, 2011). The gold standard to define such connections has been anterograde and retrograde tract-tracing techniques. Tracers show good accuracy and sensitivity, in particular for mapping long-range connections, and have resulted in a rich and detailed cartography of connectivity in several mammalian species (Felleman and Van Essen, 1991; Scannell et al., 1995; Modha and Singh, 2010). Their invasiveness however, limits their application to animal studies (Sporns, 2011).

In humans, two major indirect approaches have been employed to map structural networks: *diffusion MRI tractography* and *structural MRI covariance* (see **Figures 2A–D**). Structural networks derived from diffusion-weighted MRI data provide an approximation of the underlying white matter architecture (Le Bihan et al., 1986, 1996; Johansen-Berg and Behrens, 2006; Jbabdi and Johansen-Berg, 2011) by describing the directionality and magnitude of water diffusion at each imaging voxel. These data can be further processed by tractography algorithms (Mori et al., 1999; Behrens et al., 2003), which reconstruct fiber pathways running along plausible diffusion trajectories in voxel-space (**Figure 2A**). While somewhat challenged in regions where different fiber populations intersect (Behrens et al., 2003; Jones et al., 2012), such as the cortical gray matter, tractography can generate consistent results, particularly in deep white matter. Findings have shown overall a good correspondence with the animal tracing literature, and have been cross-validated by comparative sacrificial tracing studies in nonhuman primates (Mori et al., 1999; Parker et al., 2002; Dauguet et al., 2007). Moreover, it has been shown that factors such as fiber diameter and density, membrane permeability, myelination, as well as fiber packing (Beaulieu, 2002; Concha et al., 2010) can influence the directionality and magnitude of water displacement at a given voxel. Diffusion imaging may, thus, be used to assess microstructural and architectural integrity *in vivo*. The most widely used diffusion tensor parameters are fractional anisotropy (FA), an index of deviation of water diffusion from a random spherical displacement, and mean

relative to controls (Concha et al., 2012). Prior to analysis, the tract was subdivided into bins with respect to the anatomical distance to the temporal and frontal lobes. **(C)** Structural covariance analysis. Shown is the cortical thickness correlation map of the left medial orbital cortex seed with the remaining cortical mantle in a group of healthy controls. High positive correlations are interpreted as

exemplary time courses of the seed region with selected cortical target regions with high and low correlations, respectively. **(F)** Voxel-wise functional connectivity abnormalities in TLE, highlighting target regions with altered time-series correlation to a spatial component that closely matches the "default mode" network (Voets et al., 2012).

diffusion (MD), a scalar marker of bulk diffusion at each voxel.

In TLE, previous diffusion MRI studies have consistently shown decreased FA in temporo-limbic tracts such as the fornix pathway (Concha et al., 2005; Ahmadi et al., 2009), parahippocampal fibers (Mcdonald et al., 2008a; Yogarajah and Duncan, 2008; Ahmadi et al., 2009), the uncinate fasciculus (Rodrigo et al., 2007; Diehl et al., 2008; Lin et al., 2008; Mcdonald et al., 2008a), and the cingulum bundle (Concha et al., 2008; Ahmadi et al., 2009), as well as in several frontal and posterior fiber tracts including the inferior and superior longitudinal fascicles (Focke et al., 2008; Lin et al., 2008; Mcdonald et al., 2008a; Ahmadi et al., 2009), the internal and external capsule (Arfanakis et al., 2002; Gross et al., 2006; Concha et al., 2008), and the corpus callosum (Arfanakis et al., 2002; Gross et al., 2006; Concha et al., 2008). Relative to the widespread pattern of FA changes, MD anomalies follow a more restricted distribution (Concha et al., 2005, 2008; Focke et al., 2008). In a recent study that assessed diffusion abnormalities along fiber tracts, our group could show that the effect size of MD alterations in TLE seems to decrease as a function of anatomical distance to the temporal lobe (**Figure 2B**), suggesting co-localization of these changes with the seizure focus (Concha et al., 2012).

The combined contribution of different microstructural and architectural properties to the diffusion signal precludes a straightforward, and universal biological interpretation of diffusion tensor indices and their alteration in disease (Jones et al., 2012). Diffusion MRI is, nevertheless, currently the only imaging method that can assess fiber architecture *in vivo* (Jones et al., 2012). Initial evidence from histopathological analysis of the fimbria-fornix pathways in operated TLE patients suggests that FA decreases may primarily reflect alterations in axonal membranes (Concha et al., 2010). MD changes, on the other hand, have been shown to vary with respect to the dynamics of seizure activity (Yu and Tan, 2008; Concha et al., 2012). Indeed, MD has been shown to decrease in the hyperacute phase after prolonged seizures or status epilepticus, likely due to intracellular cytotoxic edema. Conversely, few days following the subacute peri-ictal phase, MD may increase as a consequence of vasogenic edema (Scott et al., 2006). Neuronal loss and gliosis can lead to further MD increase as a consequence of the chronic expansion of the interstitial water content.

Structural networks may also be studied through covariance analysis of MRI-based morphological metrics, such as cortical thickness or gray matter volume (Bullmore et al., 1998; Mechelli et al., 2005; Lerch et al., 2006; Bernhardt et al., 2008, 2013). According to the framework of MRI covariance analysis, a high correlation in morphological markers between two regions across subjects can be interpreted as a network link, while a low correlation indicates no link (**Figure 2C**). Similar to diffusion tractography, this correlational framework does not infer direct anatomical connections between pairs of regions. Nonetheless, analyzing structural covariance may detect manifestations of persistent functional-trophic cross-talk, maturational inter-change, as well as common developmental and pathological influences (Lerch et al., 2006; Bullmore and Sporns, 2009; Zielinski et al., 2010; Bernhardt et al., 2011; Raznahan et al., 2011; Xia and He, 2011; Khundrakpam et al., 2012; Alexander-Bloch et al., 2013). One of the advantages of cortical thickness covariance analysis is the direct seeding from cortical gray matter regions in a highresolution space that is in principle not limited by the imaging voxels of the underlying MR image, but by the sampling density of the points on the cortical mesh. Correlation analysis of structural features may furthermore represent a relatively pragmatic approach toward structural network mapping. In fact, the commonly used T1-weighted images, a standard component of every clinical imaging protocol, have a short acquisition time. Moreover, these images are generally unaffected by distortion and signal dropout artifacts in orbitofrontal and temporo-basal regions often occurring in echo-planar functional and diffusion MRI sequences.

In TLE, several recent covariance analyses have mapped abnormal structural correlations between mesiotemporal and neocortical regions (Bonilha et al., 2007a; Bernhardt et al., 2008; Mueller et al., 2009b), thalamic and neocortical regions (Mueller et al., 2009a; Bernhardt et al., 2012), and within cortico-cortical networks (Mueller et al., 2009a). Correlating the thickness of the entorhinal cortex to that of the neocortex, our group found decreased structural coordination between mesial temporal regions and lateral temporal neocortices, suggestive of a connectional breakdown within temporo-limbic circuits (**Figure 2D**; Bernhardt et al., 2008). Moreover, covariance analysis of thalamo-cortical circuits (Hetherington et al., 2007; Mueller et al., 2009a; Bernhardt et al., 2012) has shown coupled structural and metabolic change of the thalamus with neocortical (Bernhardt et al., 2012) and with mesiotemporal regions (Hetherington et al., 2007; Mueller et al., 2009a), emphasizing a key role of this structure in the pathological network of TLE.

Diffusion MRI and structural MRI covariance analysis tap into different facets of structural brain networks. While diffusion MRI analysis may be the method of choice to study white matter tracts, and their potential architectural disruptions, structural covariance analysis may sensitively assess alterations in the trophicmorphological coordination between gray matter regions. Both approaches have advanced our understanding of the fundamental architecture of inter-regional connections, and their disruptions in TLE.

## **FUNCTIONAL NETWORKS**

The study of functional networks helps to elucidate *how* a structural architecture gives rise to alterations in neurophysiological dynamics. The term *functional connectivity* refers to the strength of statistical dependencies of neurophysiological signals between regions (**Figure 2E**).

Functional connectivity can be determined from time-series measured by functional MRI (Friston et al., 1993, 1996; Focke et al., 2008; Smith, 2012) or electrophysiological techniques, such as electroencephalography (EEG) (Lopes Da Silva et al., 1989; Tononi et al., 1994; Lachaux et al., 1999). Although functional MRI and EEG have complimentary temporal/spatial resolution tradeoffs, they can also be combined (Lemieux et al., 2011). In short, functional MRI does not directly measure neural activity, but only activity-dependent hemodynamic alterations, and has a relatively low temporal resolution in the range of 1–2 s [but see Feinberg et al. (2010); Smith et al. (2012), for a recent example of increasing the temporal resolution in functional MRI acquisitions]. Yet, this technique offers high spatial resolution in the millimeter range and allows imaging the entire brain (Lemieux et al., 2011). EEG, on the other hand, has a superior temporal resolution (in the order of milliseconds) but suffers from neurophysiological signals limited to the scalp.

One way to assess functional connectivity between different brain regions is through analysis of task-free (or, resting-state) paradigms, functional acquisitions during which the subject does not perform any task (Biswal et al., 1995, 2010; Greicius et al., 2003; Smith et al., 2009). Functional connectivity analysis of such task-free datasets has allowed the identification of brain networks that show strong coupling of intrinsic, spontaneous brain activity. Ample recent resting-state functional MRI assessments have revealed networks which are generally reproducible across subjects (Damoiseaux et al., 2006) that closely correspond to brain systems engaging in specific tasks (Biswal et al., 1995; Smith et al., 2009; Laird et al., 2011). Several studies have furthermore begun to explore the relationship between low-frequency resting-state networks derived from functional MRI and those measured from EEG (De Pasquale et al., 2010; Jann et al., 2010; Musso et al., 2010; Yuan et al., 2012). Moreover, several studies in primates have suggested a close correspondence between intrinsic functional MRI connections and known anatomical pathways (Mantini et al., 2011; Shen et al., 2012). In turn, other studies have demonstrated the utility of resting-state patterns to generate regional parcellations of specific anatomical areas (Margulies et al., 2007; Mars et al., 2011). Finally, analysis of resting-state connectivity patterns may be sensitive to detect disruptions of brain organization in disease conditions (Greicius, 2008; Fox and Greicius, 2010; Kelly et al., 2012).

Several EEG and combined EEG-fMRI studies have shown dynamic alterations in functional activations and connectivity patterns related to epileptic spikes (Gotman et al., 2006; Kobayashi et al., 2006; Laufs et al., 2007; Ponten et al., 2007; Bettus et al., 2011). Resting-state functional EEG and functional MRI connectivity analyses in TLE have also quantified chronic, inter-ictal changes in functional networks (Waites et al., 2006; Bettus et al., 2009). These studies have mainly focused on assessing associations of intrinsic signals between regions known to be involved in seizure activity, particularly among medial temporal lobe structures. Bettus and colleagues reported decreased functional connectivity in mesiotemporal regions proximal to the seizure focus; interestingly, ipsilateral decreases co-occurred with increased functional connectivity in contralateral regions (Bettus et al., 2009). Findings of contralateral connectivity increases are suggestive of compensatory network reorganization. Several studies have also suggested functional connectivity alterations in regions that comprise the "default mode" network (Raichle et al., 2001; Greicius et al., 2003; Voets et al., 2012), or between this network and other brain regions (Frings et al., 2009; Liao et al., 2010; Zhang et al., 2010). The default mode network includes a collection of medial frontal, midline parietal, and lateral parietal regions that show increased activation in the absence of a specific tasks, and whose function may closely relate to internal thought processes such as memory and mind-wandering (Buckner and Carroll, 2007; Christoff et al., 2009).

Changes in inter-regional functional coupling are thought to represent compensatory mechanisms secondary to structural pathology and seizure-related activity. Combining structural and functional image analysis, for example, our group recently showed disruptions in functional connectivity between mesiotemporal regions and neocortical target networks (**Figure 2F**) that may relate to abnormal gray matter density and altered diffusivity of inter-connecting fiber tracts (Voets et al., 2012) suggestive of a complex derangement in the structural-functional cross-links. There is also evidence for abnormal signal interactions in epileptic patients without a visible lesion on MRI (Vlooswijk et al., 2011). In addition, functional abnormalities have even been shown in regions unaffected by epileptic discharges (Bettus et al., 2011), suggestive of a widespread pathological process that alter the whole-brain intrinsic functional network architecture in TLE.

## **GRAPH THEORY—MODELING NETWORK TOPOLOGY**

Conventional analysis approaches, mainly based on betweengroup comparisons have shown low-level regional and connectional alterations in TLE. These methods, however, are not tailored at capturing the complexity of whole-brain pathological interactions in TLE, which may affect higher-order, topological aspects of brain network organization.

Graph theory is a framework for the mathematical representation and analysis of complex systems. It has been applied to the analysis of artificial and biological networks (Watts and Strogatz, 1998). Graph theoretical analysis has recently attracted considerable attention in brain research because it provides a powerful formalism to quantitatively describe the topological organization of connectivity (Bullmore and Sporns, 2009; Guye et al., 2010; Bassett and Gazzaniga, 2011; Bullmore and Bassett, 2011; Alexander-Bloch et al., 2013). In graph theory terms, a network is a collection of nodes that are interconnected by edges. Nodes usually represent brain regions, while edges represent (structural or functional) connections. A pre-requisite to connectivity analysis is the proper designation of nodes as distinct gray matter regions. Various parcellation schemes have been proposed, including approximating Brodmann areas based on imaging-derived surrogates of myelination (Glasser and Van Essen, 2011; Bock et al., 2013), sulcation-based atlases (Van Essen, 2005; Desikan et al., 2006), high-resolution parcellations (Hagmann et al., 2008; Honey et al., 2009), as well as schemes that take the imaging voxels/vertices themselves as nodes (Lohmann et al., 2010; Tomasi and Volkow, 2011). In addition, several studies have also used data-driven techniques such as independent brain components to define network nodes (Yu et al., 2011b, 2013). Nodal definitions have shown to have a large influence on graph-theoretical parameters (Tohka et al., 2012), and the definition of reliable, biological meaningful parcellations schemes continues to be an active area of current research (Geyer et al., 2011; Glasser and Van Essen, 2011; Van Essen et al., 2012).

As described in previous sections, *in vivo* studies provide several definitions for network edges, both in the structural and functional domain (*see* **Figure 3**, for an example of functional and structural network generation). Accordingly, graph-theoretical analysis has been conducted across various modalities such as functional MRI (Salvador et al., 2005; He et al., 2009; Honey et al., 2009), electrophysiology (Stam, 2004; Bassett et al., 2006), diffusion-weighted MRI (Hagmann et al., 2007, 2008; Iturria-Medina et al., 2007; Gong et al., 2009), and structural covariance (He et al., 2007; Bassett et al., 2008; Chen et al., 2008; Bernhardt et al., 2011). Collectively, these studies have shown that the global topology of brain networks in healthy populations is neither random nor regular, but characteristic of a *small-world*. Small-world networks are defined by clusters of tightly inter-connected nodes, which are themselves linked to other clusters through few interconnector links. This architecture results in overall short path

lengths between individual nodes and an overall high degree of clustering (Watts and Strogatz, 1998), an architecture that enables both the specialization and integration of information transfer at relatively low wiring costs (Sporns et al., 2004). Graph-theoretical methods may be used to examine intermediary levels of organization. Communities (also called modules) are groups of nodes that are richly connected to one another within the larger framework of the entire network (Meunier et al., 2010; Bullmore and Bassett, 2011). Modularity is one of the most ubiquitous properties of complex, large-scale networks (Bullmore and Sporns, 2009), and modules may express some degree of hierarchical organization (Bassett et al., 2008; Meunier et al., 2010; Bullmore and Bassett, 2011). Theoretically, there are several advantages to a modular and hierarchical organization, including greater adaptability and robustness to changing environmental conditions (Meunier et al., 2010). Moreover, it has been suggested that the more modular and hierarchically organized a system is, the more diverse its functional activation patterns (Alexander-Bloch et al., 2010; Kaiser and Hilgetag, 2010). Modularity may be undermined by disease processes, as suggested by disrupted modularity in schizophrenia (Bassett et al., 2008; Alexander-Bloch et al., 2010; Yu et al., 2011a), and frontal lobe epilepsy (Vaessen et al., 2012a).

Besides the characterization of global and modular properties of large-scale networks, graph-theoretical techniques allow the localization of key regions within the network layout, so-called *hubs*, through centrality-based metrics (Bullmore and Sporns, 2009; Van Den Heuvel and Sporns, 2011; Zuo et al., 2012). According to formulations of centrality, hubs can be defined as regions with a high degree centrality, which means that they have a high number of connections to other nodes (Zuo et al., 2012); they can be identified on the basis of high betweeness centrality, which signifies they are located along pathways of efficient information flow (Zuo et al., 2012); finally, they can be identified through a high eigenvector centrality, which is a recursive formulation quantifying connections to other highly connected hubs (Lohmann et al., 2010; Zuo et al., 2012). Depending on their embedding in specific modules and connectivity profiles, hubs can be further classified as to whether they primarily mediate within- or between-module connectivity (Sporns et al., 2007). Assessing hubs promises to highlight critical key regions in structural and functional networks, and may thus provide a better understanding of their potential role in pathological processes (Bullmore and Sporns, 2012). In Alzheimer's disease, for example, functional hubs coincide with regions of high amyloid-beta deposition (Buckner et al., 2009). Central hubs may form a so-called *rich club*, a collection of mutually densely linked nodes with disproportionally high centrality (Van Den Heuvel and Sporns, 2011; Harriger et al., 2012). This architecture is thought to contribute to the robustness of the core constituents of the brain network. In humans, a rich club inferred from diffusion MRI has been shown to comprise lateral prefrontal, midline and lateral parietal, as well as the hippocampus, putamen, and thalamus (Van Den Heuvel and Sporns, 2011). In the macaque monkey, rich club regions have been shown to be preferentially located on short paths through the network, thereby contributing effectively to global communication (Harriger et al., 2012).

In focal epilepsy, relatively few studies have employed graphtheoretical analysis of brain networks derived from MRI (Liao et al., 2010; Bernhardt et al., 2011; Bonilha et al., 2012; Vaessen et al., 2012b). We previously showed that in drugresistant TLE, structural networks derived from inter-regional MRI-based cortical thickness correlations are characterized by increased clustering and path length, a finding indicative of a more regular global topology (Bernhardt et al., 2011). These findings were complemented by the parallel observation of reduced network robustness, a measure of organizational stability (Bernhardt et al., 2011), and subtle alterations in the distribution of network hubs pointing toward a more paralimbic distribution in patients relative to controls. In a longitudinal study, we showed that structural network disruptions intensify over time. Furthermore, relating network parameters to postsurgical seizure outcome data indicated that patients who continued to have seizures after surgery had more marked network disruptions relative to those who became seizure-free (Bernhardt et al., 2011). These findings speak to the hypothesis that seizure recurrence after surgery may, in part, be related to an extended epileptogenic network (Ryvlin, 2003; Bernhardt et al., 2010). Our findings therefore suggest a possible clinical potential for network data in the presurgical workup.

Our finding of a more regularized topology of structural cortico-cortical networks in drug-resistant TLE closely resembled results from graph-theoretical analyses of intracerebral

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## **CONCLUSIONS AND FUTURE DIRECTIONS**

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 21 March 2013; accepted: 09 September 2013; published online: 01 October 2013.*

*Citation: Bernhardt BC, Hong S, Bernasconi A and Bernasconi N (2013) Imaging structural and functional brain networks in temporal lobe epilepsy. Front. Hum. Neurosci. 7:624. doi: 10.3389/fnhum.2013.00624*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Bernhardt, Hong, Bernasconi and Bernasconi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Functional diffusion tensor imaging at 3Tesla

## *René C.W. Mandl 1\*, Hugo G. Schnack1, Marcel P. Zwiers <sup>2</sup> , René S. Kahn1 and Hilleke E. Hulshoff Pol <sup>1</sup>*

*<sup>1</sup> Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands <sup>2</sup> Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour Centre for Cognitive Neuroimaging, Nijmegen, Netherlands*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Ed Roberts, Imperial College London, UK Joonas Autio, University of Oulu, Finland*

#### *\*Correspondence:*

*René C. W. Mandl, Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, A01.126, Heidelberglaan 100, 3584CX Utrecht, Netherlands e-mail: r.mandl@umcutrecht.nl*

In a previous study we reported on a non-invasive functional diffusion tensor imaging (fDTI) method to measure neuronal signals directly from subtle changes in fractional anisotropy along white matter tracts. We hypothesized that these fractional anisotropy changes relate to morphological changes of glial cells induced by axonal activity. In the present study we set out to replicate the results of the previous study with an improved fDTI scan acquisition scheme. A group of twelve healthy human participants were scanned on a 3 Tesla MRI scanner. Activation was revealed in the contralateral thalamo-cortical tract and optic radiations during tactile and visual stimulation, respectively. Mean percent signal change in FA was 3.47% for the tactile task and 3.79% for the visual task, while for the MD the mean percent signal change was only −0.10 and −0.09%.The results support the notion of different response functions for tactile and visual stimuli.With this study we successfully replicated our previous findings using the same types of stimuli but on a different group of healthy participants and at different field-strength. The successful replication of our first fDTI results suggests that the non-invasive fDTI method is robust enough to study the functional neural networks in the human brain within a practically feasible time period.

**Keywords: white matter, DTI, activation, MRI imaging, task performance and analysis**

## **INTRODUCTION**

Neurobehavioral functions depend on a dynamic flow of information between different gray matter brain regions that are interconnected via white matter pathways (Catani and Ffytche, 2005; Mesulam, 2005). Imaging techniques such as diffusion tensor imaging (DTI; Le Bihan et al., 1986; Basser et al., 1994) in combination with fiber tracking (Conturo et al., 1999; Jones et al., 1999; Mori and van Zijl, 2002) allow us to non-invasively study the anatomy of these pathways – but not their activity.

Numerous studies examined the various aspects of using diffusion-weighted MRI in a functional setup (DfMRI) to measure activation in gray matter, using weak (Le Bihan et al., 1986, 1988) or strong diffusion weighting (Boxerman et al., 1995; Prichard et al., 1995; Gulani et al., 1999; Darquie et al., 2001; Jin et al., 2006; Le Bihan et al., 2006; Jin and Kim, 2008; Stroman et al., 2008; Yacoub et al., 2008; Aso et al., 2009; Flint et al., 2009; Kershaw et al., 2009; Autio et al., 2011; Baslow et al., 2012; Branzoli et al., 2013; Tirosh and Nevo, 2013). In our first fDTI study (Mandl et al., 2008) we proposed a non-invasive functional diffusion tensor imaging (fDTI) method that has the potential to detect the white matter fibers that are active during neurobehavioral functioning. In that study, eight healthy participants were scanned on a 1.5 MRI Tesla scanner during a tactile experiment and a visual experiment to assess the validity of the fDTI method. The results of these experiments revealed activation in the contralateral thalamo-cortical tract and optic radiations during tactile and visual stimulation, respectively. Furthermore, these results not only suggested a slowly varying response function for both the tactile and visual stimuli but also that these response functions are different for the different types of stimuli. We speculated that the differences between the response functions could be due differences in perceived intensity of the different stimuli used – the checkerboard stimulus being perceived more intense than the tactile stimulus. **Figure 1A** shows the response functions for a single tactile and a single visual stimulus. In the current study we set out to replicate our previous findings using the same types of stimuli but on a different group of healthy participants with a new fDTI acquisition scheme usinga3Tesla MRI scanner.

Functional diffusion tensor imaging is based on the assumption that task-related changes in fractional anisotropy (Basser and Pierpaoli, 1996; FA) are a sign of local fiber activity. The principle of the fDTI method as applied in this study is outlined in **Figure 2**. In our first fDTI study (Mandl et al., 2008) the conservative non-parametric sign-test formed the statistical basis to test for tract activation. Note that in the present study we used the more familiar parametric t-test because the findings from our first fDTI study suggested that the usage of the *t*-test instead of the sign-test produces very similar results. We hypothesized that morphological changes of glial cells (e.g., oligodendrocytes) induced by activity-related increases in extracellular potassium concentrations could lead to shape changes of the extra-cellular space (ECS; Ransom et al., 1985; Sykova, 2004; Beshay et al., 2005) and, in turn, lead to a measurable increase in FA. Indeed, changes in the diffusion profile due to changes in the ECS in white matter have been shown *in vitro* using diffusion weighted imaging in the rat optic nerve (Anderson et al., 1996). An earlier study (Prichard et al., 1995) reported that electrical stimulation induced significant changes in the diffusion properties of brain tissue in rats. Using intrinsic optical imaging (MacVicar et al., 2002) slowly varying activity-related signal changes were measured in the rat optical nerve, which were attributed to glial cell swelling. However, a

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**FIGURE 1 | Response functions and task encoding.** The graphs represent the time course of the measured diffusion weighted MRI signal for a single tactile stimulus (dots) or visual stimulus (dashes; adapted from Mandl et al., 2008) and the scan in combination with a scan block as used in the fDTI experiment. **(A)** Both the tactile stimulus and visual stimulus (bar) started after 12 s with a duration of 60 s. **(B)** The results of the response function experiment show that the maximum of the response function (dotted line) for the tactile stimulus (bar) falls within the first scan of a scan block while the maximum of the response function (dashed line) of the visual stimulus is found between the first and second scan. **(C)** Therefore in the fDTI experiment the tactile task the first scan is contrasted against the second and third scan, while for the visual task the first and second scan are contrasted against the third scan. The signal during the stimulus is constantly increasing (reflecting a reduction in diffusivity in the transverse direction of the tract).

reconstructed for the complete brain **(C)** and, for each tract, the *t*-values found in the SPM along that tract were grouped into a single set of *t*-values **(E)**. For each tract a statistical test (student's *t*-test) is done **(F)** on the set of *t*-values to test if the average *t*-value found along the tract is significantly (*t* > 5) greater then zero.

study using the real-time tetramethylammonium (TMA+) iontophoretic method in combination with intrinsic optical imaging (Sykova et al., 2003) showed that the concentration of TMA+ in the ECS did not change although similar changes in the intrinsic optical imaging signal were measured. Therefore the authors concluded that it was unlikely that glial cell swelling was the primary mechanism for these intrinsic optical signal changes and they suggested that a more plausible explanation may be found in morphological changes of glial cells. Our first fDTI results (Mandl et al., 2008) support this conclusion as the FA-signal changes in the active fibers measured in that study were due to opposite changes in parallel and transverse diffusion coefficients which (for a large part) cancel each other out leading to only small changes in mean diffusivity (MD). If cell swelling was the underlying mechanism for the measured FA-signal changes then an overall reduction in MD is expected because cell swelling would result in a decrease in both transverse and parallel diffusivity in the active conditions. More recent studies (Stroman et al., 2008; Flint et al., 2009)

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showed that increased levels of potassium lead to changes in both intrinsic optical signal values and MRI proton density measurements for gray and subcortical white matter in rats, suggesting that activity-related changes in the ECS of gray matter as well as white matter can be measured using MRI. In addition, Tirosh and Nevo (2013) showed, using ultra-high field MRI, that neuronal activation results in a 19.5% reduction of the ADC in excised and vital newborn rat spinal cord and concluded that this reversible drop in ADC was due to a reduction in water displacement and could not be related to any hemodynamic effect because the tissue samples were blood-free. Indeed, the contribution of hemodynamic effects to diffusion-weighted functional imaging in gray matter is a topic of extensive research. Various hypercapnia challenge studies show that in gray matter changes in ADC can be measured even when strong diffusion weighting is used. Hypercapnia induces a strong vascular response but no neuronal activity. Because of the strong diffusion weighting, changes in cerebral blood flow and/or cerebral blood volume are not expected to pay a major contribution to the measured change in ADC (Yacoub et al., 2008). However, it was shown that gradient coupling between changes in extravascular susceptibility gradients (i.e., BOLD effect) and the diffusion gradients can result in substantial changes in ADC (Zhong et al., 1991; Hong and Dixon, 1992). These results implicate that DfMRI is not immune to possible hemodynamic effects and posed the question whether or not the reported DfMRI activity-related ADC changes can be fully explained by this gradient coupling effect (Song et al., 1996; Does et al., 1999; Goerke and Moller, 2007; Miller et al., 2007; Lu et al., 2009; Ding et al., 2012; Rudrapatna et al., 2012). Although the results presented in (Stroman et al., 2008; Flint et al., 2009; Tirosh and Nevo, 2013) do provide important evidence that neuronal activation significantly reduces water displacement that can be measured using DfMRI it does not provide information on the relative contributions of the different contrast mechanisms to the measured ADC changes in gray matter. In our first fDTI study we also could not rule out the influence of hemodynamic effects on the measured task-related FA changes in white matter. Indeed, two recent vascular challenge studies (Ding et al., 2012; Rudrapatna et al., 2012) in adult Spraque-Dawley rats using strong diffusion gradients (*b*-values equal or higher than 1000) showed signal changes in white matter ranging between 1 and 2% both in MD and FA. Similar to these results the absolute percent signal change for FA reported in our first fDTI study ranged between 0.98 and 1.45% but in contrast to these results the absolute percent signal change for MD was much lower (between 0.03 and 0.21%) suggesting that the measured ADC changes cannot be readily explained by hemodynamic effects alone and are more in line with possible shape changes of the ECS.

Normal activity-induced ECS changes, however, are expected to be very small as compared to the physiological noise (Gulani et al., 1999) and a reliable detection of the signal change would require a large number of measurements. In the proposed fDTI method we assume that these activity-related glial shape changes extend over the entire active fiber so that a substantial increase in signal-to-noise ratio (SNR) can be achieved by pooling the signal changes over the complete fiber. It is the adoption of a fiber-based statistics -rather than a voxel-based statistics- that enables us to measure the signal within a practically feasible time period.

In our first fDTI study a tactile experiment and a visual experiment were selected for their expected lack of overlap in activated fibers, which allowed us to study both the specificity and sensitivity of the fDTI method. Also the chance of task related motion artifacts was reduced because a subject's response was not required in either of the tasks. In the present study we used the same types of stimuli but with a new acquisition scheme ona3Tesla MRI scanner. For white matter voxels that are part of an *active* fiber, we expected that the FA was higher during the active condition than during the rest condition, thus showing a positive correlation with the task. For the tactile task, activation was expected for the afferent fibers of the thalamo-cortical tracts that connect the thalamus and the contralateral primary sensory area (Kandel et al., 2000). For the visual task, activation was expected mainly for fibers that are bilaterally part of the optic radiation (Kandel et al., 2000).

## **MATERIALS AND METHODS**

Twelve healthy subjects participated in this study. All experiments presented in this study were approved by the medical ethical committee for human subjects of the University Medical Center Utrecht, the Netherlands, and all subjects signed written informed consent prior to participation. For the tactile stimulus experiment the participants were instructed to keep their eyes closed for the duration of the whole experiment. During the active condition, the palm and fingers of the subject's right hand were brushed in a random fashion (approximately 1 Hz) by an investigator. In the visual response function experiment the subjects were instructed to look at a red fixation cross that was projected on the center of a screen visible from inside the scanner at all times. During the active condition a red and green checkerboard was shown that alternated at a frequency of 8 Hz.

## **IMPROVEMENTS OF THE fDTI ACQUISITION**

In the present study we utilized an fDTI acquisition scheme that was improved in several ways to increase specificity by further excluding possible confounding factors (the time settings of this acquisition scheme are detailed in **Figure 1**). (1) We optimized the time settings separately for the tactile stimulus and the visual stimulus because the results of the previous study suggested a considerable time lag in the order of tens of seconds for the visual stimulus, which was not found for the tactile stimulus (**Figure 1A**).

(2) For each type of stimulus, scan slice directions were chosen perpendicular to the expected active tracts in order to minimize the effects of possible motion artifacts that may introduce false positives. If a slice is corrupted because of motion artifacts then for a tract that runs completely through that slice, all points are affected. In contrast, for a tract that runs in the direction perpendicular to that slice only one point is affected. Therefore, the scan slice direction was set in the transverse direction for the tactile stimulus, which is perpendicular to the thalamo-cortical tracts and for the visual stimulus the scan slice direction was set in the coronal direction, which is perpendicular to the optic radiations. (3) The voxel size was set to be anisotropic pointing into the direction of the tracts that are expected to become active in order to reduce partial voluming. (4) In the fDTI experiment 6 blocks of

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3 DTI scans were acquired (**Figure 2A**) where stimulus periods (60 s) alternated with resting periods (120 s). Thus, instead of a simple on/off task-design each stimulus period was now followed by two resting periods. In this way possible effects of any periodic signal changes that are not related to the task such as cerebrospinal fluid pulsations (Kao et al., 2008) were minimized.

(5) For both tactile and visual fDTI experiments the stimulus period was shifted with respect to the corresponding acquisition. Now a stimulus period starts at the middle of the last scan of a block and stops at the middle of the first scan of the consecutive block. This shift between the start of the stimulus period and the start of the DTI scan(s) associated with activation was added for two reasons.

First, the time-course experiment described in our first fDTI study showed a slowly varying response function for both the tactile and the visual stimulus (see **Figure 2A**), which was later also reported for gray matter (Baslow et al., 2012). Because of the slowly varying response function the expected signal maximum now falls in the first DTI scan period for tactile stimulus and in the middle of the first and second DTI scans for the visual stimulus. Second, effects of possible faster varying signal changes (such as signal changes due to task-related head motion or blood oxygen-level dependent, BOLD signal) now affect activation and rest DTI scans equally, thereby canceling each other out. Because both activation and rest DTI scans were acquired partially during an activation period (**Figure 1A**).

(6) Rudrapatna et al. (2012) argued that measures derived from the diffusion tensor (e.g., FA) may be devoid of their usual meaning rendering interpretation difficult because the signal is slowly varying during the acquisition of all 7 scans (that is, 1 scan without diffusion weighting and 6 diffusion-weighted scans). To determine if this non-stationarity of the signal would substantially affect the fDTI measurements we circular shifted the diffusion gradient scheme by one for each subsequent epoch. This "round robin" diffusion gradient scheme is detailed in **Table 1**.

#### **SCAN ACQUISITION PARAMETERS**

For each experiment a separate T1-weighted scan, two conventional high-resolution DTI scans, and an fDTI scan were acquired. All scans were acquired on a Philips Achieva 3 Tesla whole-body MR scanner (Intera Achieva, Philips, Best, The Netherlands) using an eight-channel head coil.

A sagittal 3D T1-weighted whole brain scan was acquired for anatomical reference, inter-subject registration, the creation of a white matter mask and visualization of the results (acquisition matrix 304 × 299 × 200; FOV = 240 mm × 240 mm × 160 mm; TR = 10 ms; TE = 4.6 ms; flip angle = 8 degrees; SENSE parallel imaging in both phase encoding directions = 1.5; total scan duration 602 s). Next, two conventional transverse Stejskal-Tanner diffusion weighted single shot spin-echo, echo planar imaging (SS-EPI) DTI scans were acquired (FOV = 240 mm × 240 mm; acquisition matrix 128 × 128; reconstruction matrix 128 × 128; slice thickness 2 mm; 75 consecutive slices; flip angle = 90 degrees; TE = 68 ms; TR = 7047 ms; parallel imaging SENSE factor = 3; total scan duration 268 s, no cardiac gating; Mandl et al., 2008; van den Heuvel et al., 2008). The second conventional DTI scan differs from the first one in that the k-space readout direction (anterior– posterior) is reversed. These conventional DTI-scans were used for reconstruction of the fibers (**Figure 2C**). The functional time series of DTI scans (the fDTI set) were acquired during the execution of an alternating sequence of a neurobehavioral task and a resting condition. For the tactile experiment a total of seven sets of three transverse SS-EPI DTI scans (acquisition matrix = 96 × 96; FOV = 240 mm; 30 slices; slice-thickness = 7 mm; no gap; TE = 78 ms; TR = 6000 ms; parallel imaging SENSE factor = 3; 90 degrees flip angle; 6 non-collinear diffusion gradient directions with b-factor = 1000 s/mm2 and 2 scans without diffusion gradients; scan duration per DTI scan = 60 s) were collected. No cardiac gating was used as it would lengthen the experiments. For the visual experiment the fDTI scans were acquired in coronal direction with otherwise identical parameter settings. The first set is a dummy set that was added to eliminate possible scanner onset effects (e.g., gradient heating). Per set the order of the diffusion gradient directions was circular shifted (**Table 1**). For the dummy set the same ordering of the diffusion gradients was used as for the first real set. For each set one stimulus period was presented. A stimulus period started at the middle of the acquisition period of the last DTI scan of the previous block and stopped at the middle of the acquisition period of the first scan of the current block (**Figure 2A**). The subjects left the scanner room for at least 15 min to rest between the two experiments. The order of the fDTI experiments (first tactile then visual or vice versa) was balanced and randomized.



*G(x, y, z) is the gradient direction vector where x points in the subjects right–left direction, y points in anterior–posterior direction and z points in the inferior–superior direction. For each subsequent scan block the set of six gradients is rotationally shifted by one.*

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### **FIBER TRACKING**

The two conventional DTI scans were combined to remove susceptibility-induced distortions (Andersson et al., 2003). After correction (Andersson and Skare, 2002) of the gradient-induced distortions and subject motion the diffusion tensors were computed using robust tensor estimation (Chang et al., 2005) based on M-estimators yielding a single DTI volume. The DTI volume was used to reconstruct the fiber tracts for the whole brain with the FACT algorithm (Mori et al., 1999). Parameter settings: minimum FA > 0.15, maximum angle between current major eigenvector and previous major eigenvector <37 degrees, average maximum angle between current major eigenvector and major eigenvectors of neighboring voxels (*R*-value) < 37 degrees, minimum fiber length 50 mm, number of fiber starting points per voxel = 8. The fiber tracking was constrained within the white matter by using a white matter mask that was created on the T1-weighted scan using SPM2 (Wellcome Department of Cognitive Neurology, London, UK) and overlaid on the conventional DTI set. The rigid transformation needed to align the T1-weighted scan with the conventional DTI volume was computed between the T1-weighted image and the diffusion unweighted scan from the (susceptibility corrected) conventional DTI image with the ANIMAL software package (Collins et al., 1995) using mutual information as a similarity metric.

#### **STATISTICAL ANALYSIS**

Because the measured FA values and noise characteristics may vary considerably at different positions along a fiber, the effect size of a task-related signal change is not constant for all voxels that are part of the fiber tract. Therefore statistical tests that assume equal effect sizes for all parts of the fiber are not suited to test for fiber activation. In the fDTI method the comparison between active and rest FA values is performed *per voxel* using a general linear model (GLM). The resulting *t*-value then represents the difference between activation and rest independent of the effect size. The results for all voxels together form a statistical parameter map (SPM) that is used to test for task-related fiber activation (**Figure 2D**). The computation of the SPM is described below. For each of the reconstructed fibers (**Figure 2C**) the set of *t*-values in the SPM that coincides with the fiber is selected (**Figure 2E**) and tested whether its mean *t*-value is significantly greater than zero (here we used a fixed threshold of *t* > 5, uncorrected; **Figure 2F**). Because of the differences in response functions for tactile and visual stimuli, different task encoding regressors for the reconstruction of the SPM were used in the tactile and visual experiment (**Figure 1**). The first DTI scan (activation) was compared with the second and third DTI scan (rest) of the set for the tactile stimulus, while for the visual stimulus the first and second DTI scan (activation) were compared to the third DTI scan (rest).

### **STATISTICAL PARAMETER MAP CREATION**

To correct for inter-scan subject motion all different diffusion weighted (and unweighted) volumes were rigidly aligned (using cross-correlation as similarity metric) with their counterparts from the first DTI scan. Next, the FA maps were computed for each of the 18 registered DTI scans. For each FA time series (i.e., the FA value of a single voxel followed over time) a *t*-statistic was computed using a GLM with two regressors. The first regressor encoded for activation (activation = 1, rest = 0). The second regressor (with linear increasing values between 0 and 1) was added to correct for effects of possible scanner drift. The results of the first regressor form the SPM that is used to test for activation of the entire fiber tracts (**Figure 2D**). Realignment of the SPM with the reconstructed tracts was done using a linear transformation that was computed between the average diffusion unweighted scan of the conventional DTI scan and the fDTI set using cross-correlation as a similarity metric.

#### **ACCUMULATED RESULTS**

The accumulated results here were created analog to accumulated results presented in (Mandl et al., 2008). In short, for each subject a binary map of the complete set of voxels that coincides with the active fibers found is placed in one common space using the linear transformation that registers the subject's anatomy scan with the Montreal Neurological Institute MNI-305 template. Each of the transformed sets is then blurred with a 3-dimensional Gaussian kernel with a full width at half maximum of 7 mm followed by a threshold at a value of 0.1 yielding a second binary map. Finally these binary maps of the subjects are accumulated and overlaid on the subjects' average anatomy. Thus the value of a (colored) voxel represents the minimum number of subjects for which an active fiber can be associated with that voxel.

## **RESULTS**

**Figure 3** shows the active fibers found for a single subject in the tactile fDTI experiment and the visual fDTI experiment. For

**FIGURE 3 | fDTI results for a single subject.** Tracts that were found active during the visual task (blue) and the tactile task (yellow) using the fDTI method. During the tactile task, activation was found predominantly contra-laterally for the thalamo-cortical tracts running from the thalamus to the primary sensory cortical area. Activation during the visual task was found, amongst others, for tracts that are part of the optic radiation and the genu of the corpus callosum.

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each task, the results of all individuals were placed in a common space to study the cumulative activation patterns (**Figure 4**). For the tactile experiment contralateral activation of the left sensory thalamo-cortical tract was found. A maximum cumulative value 7 of 12 (indicating the number of subjects that had an active fiber running through that voxel) for the superior part of the left thalamo-cortical tract was found at (−27, −30, 34) in MNI space. The maximum cumulative value 8 (of 12) for the inferior part of the left thalamo-cortical tract was found at MNI coordinate (−19, −22, −3).

found for tracts that are part of the forceps major **(A)** MNI-coordinate

For the visual experiment bilateral activation was found predominantly in the optic radiations. The maximum cumulative value 8 (of 12) for the visual task was found at MNI coordinate (31, −58, 16), which is located in the forceps major (according to the JHU white matter tractography atlas; Hua et al., 2008). More superior, a local maximum cumulative value 7 (of 12) was found at MNI location (31, −24, 34), in the right superior longitudinal fasciculus.

The mean percent signal change found for the voxels part of the active fibers for the tactile task was 3.47% (SD = 1.86), −0.10% (0.44), −0.65% (2.01), 0.09% (0.57) and 0.10% (0.18) computed for the FA, MD, transverse diffusivity, parallel diffusivity and the diffusion-unweighted signal (B0), respectively. For the visual task the mean percent signal change was 3.79% (1.79) for the FA, −0.09% (0.70) for the MD, −0.35% (1.03) for transverse diffusivity, 0.63% (0.86) for parallel diffusivity and 0.14% (0.25) for the B0 signal.

## **DISCUSSION**

34; **C**) and (−19, −22, −3; **D**).

Here we report activation measured along white matter tracts in the brains of healthy volunteers during tactile and visual stimulation using fDTI on an MRI scanner operating at 3 Tesla. This replication of our previous fDTI results on a different group of healthy participants using a different MRI scanner operating at 3 Tesla can be seen as a further indication that the fDTI method can successfully be applied to measure white matter activation. The white matter activation patterns that we found are very similar to the activation patterns found in our first fDTI study. For the tactile stimulus, task-related changes in FAvalues were found in the contralateral sensory thalamo-cortical tract. A maximum cumulative value 7 was found for the superior part of the left thalamo-cortical tract at MNI coordinate (−27, −30, 34) which is in good agreement the results of a recent fMRI study that used a tactile stimulus similar to the one we used (Jang et al., 2013). That study reported that for stimulation applied to the palm of the right hand of 15 healthy volunteers the peak activation value was found at MNI coordinate (−38, −24, 60), which is located in the primary sensory motor cortex.

For the visual stimulus task-related changes in FA-values were predominantly found in the optic radiations and forceps major. The latter contains fibers that connect homotopic visual regions. Interestingly, activation was also found in the right superior longitudinal fasciculus – a major fiber bundle that connects to the intraparietal sulcus (Uddin et al., 2010). The intraparietal

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sulcus is a structure that has been associated with perceptual motor-coordination, visuo-spatial working memory and visual attention (Swisher et al., 2007). Structural changes in white matter adjacent to this structure that were induced by training of a complex visuo-motor skill have been reported previously (Scholz et al., 2009). In that study no significant correlation was found between these structural FA changes and training progress or performance level, and it was therefore suggested that these FA changes could be more related to the amount of time spend training. Our results suggest that visual attention may also play a role in the reported structural FA changes because in our visual task no learning was involved.

The aim of our first fDTI study was to demonstrate that it was possible to non-invasively measure task-related activation in the brains' white matter. Based on the results from that study we hypothesized that subtle task-related morphological changes of glial cells resulted in measurable FA changes. However, we could not exclude possible hemodynamic contributions to the measured signal. Indeed, two recent vascular challenge studies (Ding et al., 2012; Rudrapatna et al., 2012) showed measurable changes in the order of 1–2% for the both FA and MD in white matter. Their results showed that the changes for MD where of the same size (or higher) than the changes in FA. Also, changes in the diffusionunweighted signal (B0) were even more pronounced (this was also reported in humans; Kershaw et al., 2009). In contrast, the results from our first fDTI showed that the mean percent signal change in MD computed for all voxels part of the active fibers was much smaller then the mean percent signal change in FA. The same pattern was found in our current study with the mean percent signal change in FA for the tactile task being 3.47% and for the visual task 3.79% while for the MD the mean percent signal change was only −0.10% for the tactile task and −0.09% for the visual task. In addition, for the B0 signal the corresponding mean percent signal change was 0.10% for the tactile task and 0.14% for the visual task. This pattern of a relatively large mean percent signal change for FA compared with the mean percent signal change in MD and B0 is more in line with the hypothesized activity-related morphological glial cell changes and suggests that the observed signal changes cannot be explained by hemodynamics alone (Song et al., 1996; Does et al., 1999; Goerke and Moller, 2007; Miller et al., 2007; Lu et al., 2009). Thus, a possible hemodynamic contribution (and if present by itself a valid and interesting contrast mechanism for measuring activation in white matter) may not fully explain the measured fDTI signal. Other confounding factors that are not directly related to neuronal activation may also have contributed to the measured changes in FA. For instance, fMRI studies showed that changes in respiration patterns can introduce magnetic susceptibility changes leading to artificial activation patterns found in white matter (Windischberger et al., 2002). In our approach, however, the role of this type of magnetic susceptibility changes is probably quite limited because of the introduction of the temporal shift for the stimulus onset (**Figure 1A**). Because of this shift, this type (and other types) of fast varying task-related signal changes (e.g., BOLD contrast) are largely canceled out. Furthermore, the stimulus design used in this study (1 stimulus period followed by 2 rest periods) reduces the possibility that any type of sinusoid signal fluctuations could interfere with task-related signal changes and therefore contribute to the measured changes in FA-signal.

Diffusion-weighted MR acquisitions are known to be very sensitive to motion artifacts (e.g., cardiac pulsation, voluntary subject motion). In our first fDTI study we used the same 2 dimensional axial DTI acquisition for both the tactile and visual experiment. In a 2-dimensional sequence, motion artifacts typically affect the quality of the scan at a slice level. For tracts running in parallel with the slice direction (such as optic radiations) the sensitivity to motion artifacts is therefore relatively high because large parts of the tract run trough one single slice. This in contrast to tracts running perpendicular to the slice direction (such as the thalamo-cortical tracts) because here each slice only contains a small part (1 or 2 voxels) of the complete tract. In the current experiments we used a coronal slice direction for the visual experiment to eliminate this difference in sensitivity to motion artifacts. The fact that the results of the current study are similar to the results of our first fDTI study suggests that the chosen slice direction is not a dominant factor in the experimental setup.

Despite the application of the temporal lag to maximize the measured signal change and the increased SNR due to the increased main magnetic field strength the bilateral activation predominantly found for the visual experiment in the optic radiations appears to be less pronounced at 3 Tesla than the activation found for the visual experiment in the optic radiations at 1.5 Tesla (Mandl et al., 2008). This difference in sensitivity may be explained by the lower number of stimulus periods (6) in the 3 Tesla experiment as compared to the number of stimulus periods (12) in the 1.5 Tesla experiment. Moreover, due to the smaller voxel size at 3 Tesla (43.75 mm3) compared to the voxel size at 1.5 Tesla (64 mm3) the SNR between a single fDTI scan 1.5 Tesla is comparable to a single fDTI scan at 3 Tesla because the expected increase in SNR with a factor of <sup>√</sup>2 due to a doubling of the main magnetic field strength (3 Tesla vs 1.5 Tesla) cancels out. Also, because of the thick slices used in the 3 Tesla experiments (7 mm) internal dephasing may contribute to a reduction of the SNR.

In the current study we applied a circular shift to the gradient direction settings for subsequent fDTI scans to determine if the non-stationarity of the response function during acquisition (Rudrapatna et al., 2012) would substantially alter the results. The successful replication of the results of our first fDTI study suggests that the effect of a non-stationary signal on the detection of white matter activation is limited although we cannot exclude that additional variation introduced by the use of this circular shift lowered the sensitivity of the fDTI method.

Both 1.5 and 3 Tesla experiments were specifically designed to maximize the specificity of the fDTI method to minimize the change of spurious fiber activation because the main purpose of these experiments was to assess the feasibility of the fDTI method. Further experiments are needed to determine the optimal stimulus and MRI parameter scanner settings to optimize the sensitivity of the fDTI method.

In conclusion, we replicated the results of our previous fDTI study using the same types of stimuli but with an improved scan acquisition scheme on a different group of healthy participants usinga3Tesla MRI scanner. This replication of our previous

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fDTI results suggests that the fDTI method can be applied within feasible time period and is robust enough to become a valuable tool that can help us to get a better understanding of the dynamics of functional neural networks in the human brain.

#### **ACKNOWLEDGMENTS**

This work was supported by a grant from the Dutch Science Organization for Medical Research NWO ZON-MW VIDI Program (Hilleke E. Hulshoff Pol, 917.46.370).

## **REFERENCES**


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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 05 July 2013; accepted: 11 November 2013; published online: 03 December 2013.*

*Citation: Mandl RCW, Schnack HG, Zwiers MP, Kahn RS and Hulshoff Pol HE (2013) Functional diffusion tensor imaging at 3 Tesla. Front. Hum. Neurosci. 7:817. doi: 10.3389/fnhum.2013.00817*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Mandl, Schnack, Zwiers, Kahn and Hulshoff Pol. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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## Parcellation of the cingulate cortex at rest and during tasks: a meta-analytic clustering and experimental study

## *Diana M. E. Torta1,2\*, Tommaso Costa1,2, Sergio Duca2, Peter T. Fox3 and Franco Cauda1,2*

*<sup>1</sup> Department of Psychology, Università di Torino, Torino, Italy*

*<sup>2</sup> CCS fMRI-Brain Connectivity and Complex Systems Unit, Koelliker Hospital, Torino, Italy*

*<sup>3</sup> Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Chunshui Yu, Capital Medical University, China Daniel S. Margulies, Max Planck Institute for Human Cognitive and Brain Sciences, Germany*

#### *\*Correspondence:*

*Diana M. E. Torta, Department of Psychology, Università di Torino, Via Po 14, 10123 Turin, Italy e-mail: diana.torta@unito.it*

Anatomical, morphological, and histological data have consistently shown that the cingulate cortex can be divided into four main regions. However, less is known about parcellations of the cingulate cortex when involved in active tasks. Here, we aimed at comparing how the pattern of clusterization of the cingulate cortex changes across different levels of task complexity. We parcellated the cingulate cortex using the results of a meta-analytic study and of three experimental studies. The experimental studies, which included two active tasks and a resting state protocol, were used to control the results obtained with the meta-analytic parcellation. We explored the meta-analytic parcellation by applying a meta-analytic clustering (MaC) to papers retrieved from the BrainMap database. The MaC is a meta-analytic connectivity driven parcellation technique recently developed by our group which allowed us to parcellate the cingulate cortex on the basis of its pattern of co-activations during active tasks. The MaC results indicated that the cingulate cortex can be parcellated into three clusters. These clusters covered different percentages of the cingulate parenchyma and had a different density of foci, with the first cluster being more densely connected. The control experiments showed different clusterization results, suggesting that the co-activations of the cingulate cortex are highly dependent on the task that is tested. Our results highlight the importance of the cingulate cortex as a hub, which modifies its pattern of co-activations depending on the task requests and on the level of task complexity. The neurobiological meaning of these results is discussed.

**Keywords: meta-analytic clustering, voxel-based meta-analysis, meta-analytic connectivity modeling, activation likelihood estimation, data driven parcellation, k-means clustering, hierarchical clustering, voronoi parcellation**

## **INTRODUCTION**

The cingulate cortex is the thick part of the cerebral cortex surrounding the corpus callosum and is currently thought to be made up of four subregions (the four-region neurobiological model): The anterior cingulate cortex, which includes the perigenual cingulate cortex, the midcingulate cortex, the posterior cingulate cortex and the restrosplenial cingulate cortex (Vogt, 2009). Such four major subdivisions were built mainly on structural observations (Vogt and Pandya, 1987; Vogt et al., 1987, 1992, 2001, 2003; Vogt and Derbyshire, 2002; Vogt and Vogt, 2003; Vogt and Laureys, 2005; Fan et al., 2008; Palomero-Gallagher et al., 2008, 2009; Vogt, 2009) and were proposed to subserve specific functions, with the anterior cingulate cortex involved in affective evaluation (Allman et al., 2001), conflict monitoring (Carter et al., 1998, 1999; Botvinick et al., 2004), error monitoring and detection (Holroyd et al., 1998; Gehring and Knight, 2000; Gehring and Fencsik, 2001), response selection (Paus et al., 1993; Awh and Gehring, 1999) and attention control (Posner and

Dehaene, 1994; Crottaz-Herbette and Menon, 2006); the midcingulate cortex involved in cognitive tasks such as attention for action (Pardo et al., 1990; Badgaiyan and Posner, 1998), response selection (Corbetta et al., 1991; Paus et al., 1993), error detection and competition monitoring (Carter et al., 1998), anticipation (Murtha et al., 1996), and working memory (Petit et al., 1998); the posterior cingulate cortex in tasks related to visuospatial orientation and navigation of the body in environmental space (Vogt and Laureys, 2005), self-reflection and autobiographical memory (Spreng et al., 2009); and finally, the retrosplenial cingulate cortex in memory and visuospatial functions (Vogt and Pandya, 1987; Vogt et al., 1987; Parker and Gaffan, 1997; Burgess et al., 2001; Vogt, 2005; Iaria et al., 2007; Keene and Bucci, 2008; Vann et al., 2009). Until recently this "segregationist model," for which each portion of the cingulate cortex subserves specific functions, has been adopted.

Recent studies have used magnetic resonance imaging (MRI) or functional MRI (fMRI) to investigate the connectivity based parcellation of the cingulate cortex. Beckmann and colleagues (2009) used probabilistic diffusion tractography (a measure of anatomical connectivity), to characterize the cingulate probabilistic connectivity. By applying a connectivity-based parcellation, they found nine distinct clusters in the cingulate cortex

**Abbreviations:** MaC, Meta-analytic clustering; fMRI, Functional magnetic resonance imaging; ROI, Region of interest; MACM, Meta-analytic connectivity modeling; ALE, Activation likelihood estimation.

(Beckmann et al., 2009). Yu and colleagues investigated the functional connectivity of each of the subregions proposed in the four-region model and showed that each subregion was characterized by a specific pattern of functional connectivity (Yu et al., 2011). This finding led the authors to conclude that the anatomical segregation is confirmed by functional segregation (Margulies et al., 2007; Yu et al., 2011). While the functional connectivity of some specific subregions of the cingulate cortex during rest has been progressively disclosed (Koski and Paus, 2000; Margulies et al., 2007; Yu et al., 2011), to date far less is known about the functional profile and the parcellation of the cingulate cortex during tasks.

The advent of databases such as BrainMap (Fox et al., 2005; Laird et al., 2005a,b, 2009a,b), which stores thousands of fMRI experiments together with their results, has allowed performing the so-called voxel-based meta-analyses. Using these databases, many different studies can be pooled together to investigate the functional properties of specific brain regions when involved in active tasks. As a result of this approach, recent views have proposed that the same subregions of the cingulate cortex are involved in different functions ("integrationist model"). For example, Shackman and colleagues (2011) observed that in the rostral anterior cingulate cortex, pain, affect, and cognition overlap. Similarly, Torta and Cauda (2011) have reported that the midcingulate cortex is recruited in a variety of tasks ranging from pain to affect to attention and motor tasks.

Altogether these findings suggest that fMRI parcellations of the cingulate cortex lead to different results depending on the methodological approach. In addition, it has been shown that high-dimensional parcellations support the view of a hierarchical nested structure within the hub regions of the brain (Leech et al., 2011). Here, we aimed at comparing how the pattern of clusterization of the cingulate cortex changes across different levels of task complexity (i.e., tasks and non-tasks such as resting state). We performed a meta-analytic study using a metaanalytic tool recently developed by our group: the meta-analytic clustering (MaC) (Cauda et al., 2012). This method permits a voxel-wise data-driven clusterization of the patterns of coactivation of the cingulate cortex during the widest number of active tasks (Cauda et al., 2012). This technique starts from the meta-analytic data and produces a meta-analytic connectivitybased parcellation in a data-driven fashion (Torta and Cauda, 2011). We verified the results of the MaC study by performing three additional fMRI experiments on healthy volunteers. In this way we could observe possible modifications of the parcellation of the cingulate cortex during the widest number of active tasks, during specific tasks and during resting state.

For the control fMRI experiments, we scanned participants during resting state (experiment 2), during the presentation of emotional faces (experiment 3) and during the administration of painful stimuli (experiment 4). These latter two tasks were chosen as known to activate the cingulate cortex (Apkarian et al., 2005; Friebel et al., 2011). Subsequently we parcellated the cingulate cortex on the basis of the results of the two experiments and compared such findings to those obtained with the meta-analytic parcellation.

## **MATERIALS AND METHODS**

#### **META-ANALYTIC STUDY** *Database search*

We queried the BrainMap database (Fox et al., 2005; Laird et al., 2005a,b, 2009a,b) for studies on healthy volunteers that recorded activations in the cingulate cortex. We did not select studies on the basis of the task performed as we were interested in investigating activations of the cingulate cortex in a broad range of cognitive tasks. Such a choice allowed us to characterize the parcellation of the cingulate cortex in a "working mode" irrespective of the task. The results of this search were saved in a series of files containing locations, papers and behavioral domains. For the meta-analysis, boundaries of the cingulate cortex were already coded in BrainMap, indeed we queried the database for all the papers that showed at least one focus in the cingulate cortex (http://www*.*brainmap*.*org/sleuth/). For subsequent analyses on in-house acquired fMRI data, the volume of interest (VOI) around the cingulate cortex was drawn by one of the authors (Franco Cauda), expert in neuroanatomical clustering, on the Colin 27 template (http://neuro*.*debian*.*net/pkgs/ mni-colin27nifti*.*html) at the group level. The MNI cingulate coordinates were then converted to Talairach coordinates using the icbm2Tal transform (http://brainmap*.*org/icbm2tal/).

## **META-ANALYTIC CLUSTERING (MaC)** *Data preparation*

Here, we employed a methodology called MaC, delineated in a recent paper (Cauda et al., 2012). MaC works on meta-analytic connectivity modeling data (MACM; Robinson et al., 2010). Thus, as a first step, we performed a MACM. The MACM method is based on co-occurrences that are evaluated using the ALE approach. With a too small number of co-occurrences (i.e., foci) the ALE results are too dependent on the contribution of single foci (i.e., single studies) and are spatially very variable. As a consequence of this variability the parcellation results are unstable. Indeed the ALE method needs a minimum number of foci to produce a valid estimate (Laird et al., 2005a, 2009a; Eickhoff et al., 2009). To accomplish this need we created, in a completely unsupervised data-driven way, "blocks" of voxels each containing 50 foci.

The choice of 50 foci was supported by a simulation to evaluate the stability of the parcellation results using 10–100 foci in steps of 10. We parcellated the cingulate surface using blocks of different dimensions (namely with a different number of foci). For each dimension we repeated the entire process 100 times. We then checked the reliability of the results for each step evaluating if the results were stable for a given number of foci. The results were stable with blocks of n *>* 40 foci (see also Cauda et al., 2012) so we decided to opt for a minimum number of 50 foci as to further improve the stability obtained with 40 foci. We created blocks of voxels by employing the quad tree algorithm (Ballard and Brown, 1982). The quad tree is an algorithm that subdivides the twodimensional space by decomposing the region into four equal quadrants and subsequently into four further subquadrants until each of the subquadrants contains the pre-defined number of foci (50 in our case). Thus, when the blocks meet the criterion, they are not subdivided further. In contrast, if more than 50 foci are found in a block, the algorithm further subdivides the block until the criterion of homogeneity is reached. As a result, we obtained 37 blocks containing 50 foci each but having different sizes. The quad tree decomposition returns a structure consisting of the *y* and *z* Talairach coordinates of each block and the corresponding papers in which Talairach coordinates of the foci appear. All parcellations were performed on a 3D mesh. To build the 3D mesh, a constant gray matter (GM) thickness was assumed, to allow subsequent methods to operate in 2D. Indeed, the 3D data from the database were projected to a plane passing through the coordinates *X* = 0 and then analyzed. For visualization purposes these data were back projected to a renderized brain surface on the basis of each voxel's original coordinate. All foci were projected within +1 and −5 mm from the GM plane to this surface. All these clustering analyses were performed using custom developed MATLAB scripts (Mathworks, Natick, MA, USA).

We calculated the MACM using the ALE algorithm (Laird et al., 2005a; Eickhoff et al., 2009) to pool the active foci of each quad tree. Each coordinate (focus) was modeled by a 3- D Gaussian distribution, defined by a full-width half-maximum (FWHM) of 10 mm (Turkeltaub et al., 2002). The ALE statistic was computed at every voxel in the brain. We made a valid assessment of the significance of the result by testing the values from the ALE images against null distributions. A threshold was applied, while controlling the false discovery rate (FDR) (Genovese et al., 2002) at a significance level of *p <* 0*.*05. Importantly, the ALE algorithm has been formulated to limit the inter-subject and inter-laboratory variability typical of neuroimaging studies. This algorithm estimates the spatial uncertainty of each focus and takes into account the possible differences among studies, as to avoid that single studies may drive the results.

We subdivided the cingulate cortex into areas with homogeneous co-activations by employing two kinds of cluster analysis (Cauda et al., 2010; Frades and Matthiesen, 2010): Hierarchical and k-means clustering and using as input the results of ALE analysis (MACM). Hierarchical clustering groups data over a variety of scales by creating a cluster tree; trees represent multilevel hierarchies where clusters at one level are joined as clusters at the next level. Importantly, hierarchical clustering does not require a priori impositions on the number of clusters and by creating the dendrogram it allows the visualization of the hierarchy within the data. The advantage of the hierarchical clustering is that it can handle different forms of similarity or distance. A distance matrix is the only requirement for hierarchical clustering. Furthermore, hierarchical clustering allows an embedded flexibility regarding the level of granularity; that is, the extent to which an entity is divided in smaller parts. We used the hierarchical clustering as we were interested in visualizing the hierarchical structure of the data. To verify the goodness of the hierarchical clustering we used the cophenetic distance. This measure describes how well the cluster tree reflects the data for different distance measures and allows to verify the consistency of each link. This method, together with the visual inspection of the dendrogram and the reordered distance matrix, represents a way to find the optimal number of clusters of the data (Ward, 1963). The matrix was composed of rows, representing the blocks formed by the quad tree algorithm, and columns representing the probability of activation obtained by the ALE analysis of each voxel in the brain. The data matrix was used to create the distance matrix. There are different criteria to evaluate the distances between clusters in the hierarchical clustering literature. In this case, we employed the Ward method that uses an analysis of variance approach (Ward, 1963). Subsequently, we employed the k-mean clustering to assess the results using as input the number of clusters obtained from the techniques described above.

Unlike hierarchical clustering, k-means operates on actual observations and creates a single level of clusters. K-means is a partition method in which objects are classified as belonging to one or k groups with k chosen a priori. The cluster membership is determined by calculating the centroid for each group and assigning each object to the group with the closest centroid. This approach minimizes the overall dispersion within cluster by operating an iterative reallocation of cluster members. Advantages of this methods are its time and space complexity and its order-independent properties. Order independency means that k-means generates the same partition of the data irrespective of the order in which patterns are presented.

The results of the k-means clustering were further verified using the average silhouette values (Rousseeuw, 1987). To calculate the hemisphere prevalence, for each cluster we overimposed the clusterization results of the left and the right cingulate cortex. Areas pertaining to the left cingulate cortex were colored in green, areas pertaining to the right cingulate cortex were colored in orange. Areas of overlap of the clusterization results of both sides were colored in red.

#### **DENSITY ANALYSIS**

We observed that voxels in the cingulate cortex were unequally activated by tasks. That is, the activation of some voxels was more frequent and some regions of the cingulate cortex were found to be characterized by a wider number of active voxels. In order to substantiate this observation, we performed a density analysis of the active foci in the cingulate cortex. This was done in order to obtain a deterministic method to calculate the density of foci. We decided not to use ALE for this aim because it modifies the probability kernel including information such as the number of subjects. In this way results are not exactly a measure of density of foci. For density, we intended the number of foci per unit of area. In order to analytically calculate the number of foci per unit per area, we used the Voronoi tessellation algorithm (Klein, 1989). A Voronoi tessellation is a decomposition of metric space by distances between sets of points. The Voronoi algorithm tessellates a surface into polygons in such a way that the area of each polygon is inversely proportional to the density of points (foci) in that area.

#### **NETWORK ANALYSIS**

It has been suggested that some portions of the cingulate cortex may act as hubs interconnecting different networks (Leech et al., 2011). Graph analysis techniques allow the investigation of how complex brain networks relate to each other (Bullmore and Sporns, 2009), therefore resulting particularly suitable for inspecting the presence of hub areas. Graph and network analyses build graphs of elements in such a way that the position of an element in the graph reflects its relationship with neighboring elements.

We reordered the distance matrix so as to place more edges closer to the diagonal. Reordering was performed using a routine of the Brain Connectivity Toolbox that minimizes the cost function of the matrix (Rubinov and Sporns, 2010). Then, using the data in the reordered distance matrix, we constructed a network and we optimally represented the results employing a force-directed algorithm: the Fruchterman-Reingold method (Fruchterman and Reingold, 1991). In this algorithm, the nodes are represented by steel rings and the edges are springs between them. The attractive force is analogous to the spring force and the repulsive force is analogous to the electrical force. The basic idea is to minimize the energy of the system by moving the nodes and changing the forces between them. A threshold was applied to the resulting image so that only the circles (blocks) with highest network connectivity (first quartile) were represented as color-filled.

## **EXPERIMENTS 2–4**

We also performed three additional experiments on healthy volunteers.

#### **PARTICIPANTS**

We scanned two groups of healthy volunteers. The first group was composed of 10 right-handed adults (five females) (mean age = 22 ± 1*.*4) who participated in the resting state experiment (experiment 2) and in the experiment in which emotional faces were shown (experiment 3). The second group was composed of 17 healthy right-handed volunteers (eight females, mean age 28 ± 4*.*2) who participated in the experiment in which painful stimuli were applied to the hands (experiment 3). All of the participants were free of neurological or psychiatric disorders, not taking medications known to alter brain activity, and with no history of drug or alcohol abuse. We obtained the written informed consent of each subject, in accordance with the Declaration of Helsinki. The studies were approved by the institutional committee on ethical use of human subjects at the University of Turin.

## **TASKS AND ACQUISITION**

#### *Experiment 2—Resting state*

The first group performed a 6-minute resting state task. During the resting state scan, participants were asked to relax, to not fall asleep and to not think of anything in particular while they were being scanned.

#### *Experiment 3—Faces expressing emotions*

The same 10 participants took part in an experiment in which faces expressing different emotions were shown to them while lying in the scanner. The stimuli consisted of faces showing anger, disgust, fear, happiness, sadness, and neutral expressions. The faces were taken from Biehl et al. (1997). A total of four Caucasian actors (two males and two females) showing both emotional and neutral expressions were used for a total of 24 images (six conditions × four actors). The task scans consisted of four runs of a slow event-related design. Each run consisted of 24 stimuli,

four for each emotional and neutral condition, presented in random order. The subjects were instructed to look passively at the faces.

### *Experiment 4—Painful stimuli*

The second group participated in a task in which painful mechanical stimuli were delivered to the hands. Painful mechanical stimuli were delivered with a 256 mN pinprick probe which activates the high-threshold mechanoreceptors (Baumgartner et al., 2010). During the four runs of this slow event-related design, the participants received a total of 48 stimuli on the left and right hand (no more than three consecutive stimuli on the same hand). They were instructed to relax, pay attention to the stimuli and report a subjective rating of intensity at the end of each block.

#### *Data acquisition*

Data acquisition was performed on a 1.5 Tesla INTERA™ scanner (Philips Medical Systems) with a SENSE high-field, high resolution (MRIDC) head coil that was optimized for functional imaging. The functional T2∗-weighted images were acquired using echoplanar (EPI) sequences, with a repetition time (TR) of 2000 ms, an echo time (TE) of 50 ms, and a 90◦ flip angle. The acquisition matrix was 64 × 64, and the field of view (FoV) 200 mm. A total of 200 volumes were acquired; each volume consisted of 19 axial slices, parallel to the anterior-posterior (AC-PC) commissure line and covering the whole brain; slice thickness was 4.5 mm with a 0.5 mm gap. Two scans were added at the beginning of the functional scanning and the data were discarded to reach a steady-state magnetization before acquiring the experimental data.

In the same session, a set of three-dimensional high-resolution T1-weighted structural images was acquired for each participant. This data-set was acquired using a Fast Field Echo (FFE) sequence, with a TR of 25 ms, ultra-short TE, and a 30◦ flip angle. The acquisition matrix was 256 × 256, and the field of view (FoV) 256 mm. The set consisted of 160 contiguous sagittal images covering the whole brain. In-plane resolution was 1 × 1 mm and slice thickness 1 mm (1 × 1 × 1 mm voxels).

#### **ANALYSIS**

The data were pre-processed using slice scan time correction, 3D motion correction, spatial smoothing (4 mm FWHM) and temporal filters (linear trend removal and band pass filter of 0.008–0.08 Hz).

After pre-processing, voxels belonging to the cingulate cortex were submitted to a voxel-wise unsupervised fuzzy c-mean clustering technique (Cauda et al., 2010, 2013). This data-driven method decomposes the original fMRI time series into a predefined number of spatiotemporal modes, which include a spatial map and an associated cluster centroid time course. The extent to which a voxel belongs to a cluster is defined by the similarity (as measured, e.g., by correlation) of its time course to the cluster centroid. We used this clusterization approach as it has been previously optimized for resting state and MACM (Cauda et al., 2010, 2011b).

We used two clustering approaches to compare the complexity of clustering of the MaC data to that of the experimental tasks. First, we imposed the same number of clusters obtained for the MaC to the in-house acquired datasets. Second, we calculated the number of clusters of the experimental data without imposing the results of the MaC.

## **VALIDATION OF CLUSTERING PROCEDURES**

We used the upper tail rule developed by Mojena to validate the number of clusters (Mojena, 1977). Statistical stopping rules for clustering methods allow selecting the "best" number of clusters in the data. Stopping rules define explicitly what is meant by a significant change in the clustering criterion. That is, these rules help define when a further partition is not necessary. The method developed by Mojena (1977) uses the relative sizes of the fusion level in the hierarchy. The algorithm selects the partition associated to the first level in the cluster number sequence which satisfies the following condition:

$$Zj + 1 > m + ksz$$

where *m* is the mean of the fusion level of the previous fusion level, *sz* is the standard deviation of those values, and *k* is the standard deviate. The knee of the curve of the cluster sequence is an indicator of the right number of clusters. In simple words, this method takes the distance matrix and calculates the types of aggregation by calculating mean and standard deviation of the number of nodes as a function of the imposed clusters. Then the knee is calculated as a second derivative of the curve. The peak that is obtained represents the "optimal" number of clusters according to the stopping criterion.

## **RESULTS**

#### **DATABASE SEARCH**

The BrainMap query retrieved 1240 papers involving 24,540 subjects and a total of 1851 foci (see Table S1).

#### **MACM CLUSTERING**

The results of the dendrogram obtained from the hierarchical clustering suggested that the cingulate cortex can be optimally parcellated into three clusters (see **Figures 1**, **2**). The same result was confirmed by the silhouette plot. The silhouette plot is a measure of how close each point in one cluster is to points in the neighboring clusters. This method allows to understand of how well-separated the resulting clusters are.

We used the k-means method to cluster the MACM blocks into the three clusters as previously identified. To minimize the risk of inconsistent results obtained for the initial random placement of starting points, the k-means was computed 256 times (Nanetti et al., 2009). The same three clusters were identified all 256 times.

The results of the MaC evidenced that Cluster 1 covers 7% of the cingulate surface and is located in the dorsal anterior cingulate cortex (dACC). Cluster 2 covers 30% of the total cingulate surface, being predominantly located in the intermediate part of the cingulate cortex but also presenting two small locations in the anterior and posterior cingulate cortex. Cluster 3 covers 63% of the cingulate surface, encompassing the posterior but also the anterior parts of the cingulate cortex, and also presenting a small location in the midcingulate area (**Figures 2**, **3**).

**Figure 4** shows the mean MACM connectivity of the three clusters. Our results indicate that cluster 1 is functionally connected to the parietal cortices (the inferior parietal lobe BA 40, the superior parietal lobe BA 7), frontal cortices (superior and middle frontal gyri BA 9, 10) and to some sensory (primary somatosensory cortices BA 2, 3) and motor (primary and premotor areas BA 4 and 6) areas. In addition, this cluster shows connections to the temporal lobe (superior and middle temporal gyri BA 21, 22, 39) and is slightly left lateralized in the anterior cingulate cortex and slightly right lateralized in the posterior cingulate cortex. Cluster 2 is co-active with the inferior, middle and superior frontal gyri (BA 45, 9, and 10), the parietal cortex (BA 7, 40, 43), and the temporal lobe (BA 37, 39). Moreover this cluster presents a strong co-activation with subcortical structures such as the thalamus, the red nucleus, and the caudate. This cluster is also characterized by a right-prevalence of activations in the posterior cingulate cortex. Cluster 3 is characterized by a more frontal and limbic functional connectivity which includes the superior and middle frontal gyri (BA 9 and 10) and hippocampal and parahippocampal structures. All three clusters are extensively co-active with other portions of the cingulate cortex and with the insula (see **Figure 4** and Tables S2–S4).

The probabilistic map (**Figure 5**) shows that the highest overlap between all the block-related MACM maps is in the anterior insula, dorsal cingulate cortex, dorsolateral prefrontal cortices, sensorimotor, precuneal, and posterior parietal cortices.

The lower part of **Figure 1** represents the network derived from the distance between blocks of the cingulate cortex. The points represent blocks and are coded with a color indicating the cluster to which they belong. In this image the distance between points represents the Euclidean distance between the MACM maps of each block. The network representation is optimized using multidimensional scaling. Points belonging to cluster 1 are centrally located and surrounded by points belonging to clusters 2 and 3. This method rearranges multidimensional entities in a 2D space such that the distance represents the similarity. In this way, similar entities are closer in space whereas dissimilar entities are placed apart.

The same finding was confirmed by the results presented in **Figure 6**, which shows the network representation of the Euclidean distances between blocks. Here the graphical network representation is optimized with the Fruchterman–Reingold method. The color-filled circles display blocks with the highest degree of connectivity. This analysis supports the idea that blocks in cluster 1 are hub areas.

A representation of which behavioral classes activate each cluster can be found in Figure S1.

#### **DENSITY OF FOCI**

**Figure 7** displays the results of the Voronoi tessellation. The dACC showed the highest density of foci. Notably, this area was found to partially overlap with the location of cluster 1. Other

portions in the anterior cingulate cortex and in the cingulate motor zone showed a high density of foci. The two blocks showing the highest number of functional connections (see **Figure 6**) were found to be part of cluster 1 and were located in the dorsal cingulate cortex in the area with the highest density of foci. Together, these results indicate that such area exerts a pivotal role as a hub.

Figure S2 describes the behavioral related density of foci.

## **EXPERIMENTS 2–4**

Images of the task-evoked activations and of the resting state protocol are shown in the supplementary material (Figures S3–S5).

The resting state parcellation shows a tripartite subdivision of the cingulate cortex with an anterior, a mid and a posterior cluster. In contrast, the parcellations based on the functional connectivity of the cingulate cortex when involved in the elaboration of emotional faces are less sharp and more complex (**Figure 8**). The parcellation based on responses to painful stimuli goes further in the direction of the results of the emotions paradigm: The clusters were found to be intermixed in a complex and blurred pattern of connectivity. Areas clearly connected with one network in the resting state showed, in contrast, an intermixed pattern of connectivity during tasks. That is, areas previously found to be characterized by homogeneous functional connectivity (e.g., cluster containing only voxels belonging to that cluster) are found to contain voxels also belonging to other clusters.

The results of the parcellation of the cingulate cortex based on the activations elicited by experimental tasks and obtained without imposing a priori the number of clusters, revealed a

pattern of increasing complexity from the resting state data (2 clusters) to the emotion (3 clusters) and pain (5 clusters) paradigms (See **Figure 9** and Figure S6 for the results of the stopping criterion).

## **DISCUSSION**

This study was designed to investigate the pattern of clusterization of the cingulate cortex during the broadest range of tasks possible. We performed a meta-analytic study using a meta-analytic tool recently developed by our group: the MaC (Cauda et al., 2012). We further performed three additional fMRI experiments on healthy volunteers. In this way we could observe possible modifications of the parcellation of the cingulate cortex during the widest number of active tasks, during specific tasks and during resting state. Our main finding was that the cingulate cortex changes its pattern of co-activations depending on the level of task complexity. Indeed, the complexity of the pattern of parcellation increased and changed from the resting state experiment, to the two task-based experiments and the meta-analytic study. Importantly, we considered the task complexity not as reflecting a greater cognitive challenge, but rather as reflecting the kind of task (resting state-no task vs. active tasks) and the number of tasks (e.g., responses to painful stimulation vs. a general active mode, that is whenever a task is performed-meta-analytic study).

## **MaC CLUSTERING: CLUSTERING MACM RESULTS**

Voxel-based meta-analyses have provided a fundamental contribution to the building of new insights into brain functions. In a previous study, we found that an ROI based parcellation of the cingulate cortex produced three clusters (Torta and Cauda, 2011). Such results were confirmed by those of the present study which indicate that using MaC, the cingulate cortex can be efficiently divided into three clusters (see **Figure 1**). These three clusters have different dimensions and cover a different percentage of the cingulate parenchyma. Although each cluster is characterized by specific co-activations (Corbetta et al., 1991, 2008; Corbetta and Shulman, 2002; Seeley et al., 2007; Beckmann et al., 2009; Menon and Uddin, 2010; Alexander and Brown, 2011), all three have extensive co-activations within the cingulate cortex and the insula (Cauda et al., 2011b, 2012) and share an "attentional" pattern of co-activation (Craig, 2003, 2009; Medford and Critchley, 2010;

**anatomical location of the clusters.**

cluster dimension *k >* 100 mm and visualized using MRIcron (http://www.cabiatl.com/mricro/mricron/index.html). <sup>3</sup>

**FIGURE 5 | Probabilistic map showing the superposition of the MACM of all blocks.** The probabilistic map shows the probability of overlap between each block-related MACM map. The probability map is calculated by summing the voxel value of each block-related MACM map and dividing this value by the number of blocks. Single network maps before the creation of the probability maps were thresholded at *p <* 0*.*05, minimum cluster size *k >* 100 mm3. Regions in blue are characterized by a low probability of overlap, whereas green regions present a high probability of overlap.

Cauda et al., 2011b; Torta and Cauda, 2011). This overlap of common co-activations is maximal in the dACC and in the insula, as evidenced by the results of the probabilistic map (**Figure 5**). These findings support the view that the cingulate cortex and the insula form a saliency network devoted to the integration of information coming from the internal (e.g., homeostasis) and the external (e.g., sensory) environments (e.g., sensory inputs) (Vincent et al., 2008). In addition, this insular-cingulate system is thought to be in charge providing a stable "set maintenance" over the execution of tasks (Dosenbach et al., 2006). Moreover, we found that all clusters are strongly locally interconnected as evidenced by the presence of activity of the dACC and of the insula in the MACM profiles of the three clusters.

One possible criticism to our approach is that it may be considered not completely data driven. However, to obtain a voxel-wise analysis we first needed to aggregate a certain number of voxels to reach a minimum amount of foci. In this sense, our clustering cannot be entirely considered as voxelwise. Our method does not rely on pre-determined regions of interest but operates a local reduction of the resolution in a inverse relation to the density of foci. Other alternative solutions have been proposed (Bzdok et al., 2012). For instance, Bzdok and colleagues (2012) have obtained a partial voxel-wise clusterization by searching foci in the adjacency of a voxel, spreading the search to reach a minimum number of foci and then projecting the connectivity profile of these foci to the voxel.

#### **HUBS OF THE CINGULATE CORTEX**

The overlap of activations in the dACC, insula and thalamus (see **Figure 5**) suggests that such brain regions act as hubs through which homeostatic and sensory information is conveyed. This interpretation is also supported by the results of the Voronoi tessellation that pointed to the dACC as the area with the greatest density of foci. Indeed, it is highly likely that those areas having the greatest density of foci also have a greater number of connections. Further confirmation of the interpretation of the dACC as a hub area comes from the results of the distance matrix analysis (**Figure 1**, lower panel) and of the network analysis. Indeed, the findings of the graph analysis show that, in multidimensional scaling of MACM-based profiles, blocks of cluster 1 are close to each other and placed in a central position. These results indicated that such blocks have a similar and homogenous connectivity pattern. Moreover, they also suggest that cluster 1 may exert a pivotal role in regulating the activity of the other two clusters. A pivotal role of the cingulo-insular network in the regulation of the activity of other networks was suggested by Sridharan and colleagues (2008). These authors proposed that the cingulo-insular network plays a major role in switching between brain networks such as the central-executive network and the default-mode network (Sridharan et al., 2008). Interestingly, hypo-functionality of the dACC has been related to reduced awareness of the self. This would suggest that damage to hub areas may result in a failure to integrate stimuli coming from the external world into a coherent representation of the self (Amanzio et al., 2011).

#### **PARCELLATION OF THE CINGULATE CORTEX WHEN AT REST AND WHEN INVOLVED IN ACTIVE TASKS**

Whether the cingulate cortex has a segregated functional organization or not remains a matter of debate. Devinsky et al. (1995) and Bush et al. (2000) have reviewed evidence from neuroimaging, neurophysiological and anatomical data supporting the view that each part of the cingulate cortex is specialized in a family of cognitive operations, with the anterior cingulate more devoted to affective elaboration and the midcingulate to cognitive tasks. Previous studies, investigating the parcellation of the cingulate cortex on the basis of its connectivity (anatomical, Beckmann et al., 2009 or functional Yu et al., 2011) have upheld this view. A partial support for these results came also from a previous study performed by our group (Torta and Cauda, 2011). However, our present findings open up the possibility of a substantial divergence between the parcellation of the cingulate cortex when at rest and when involved in active tasks. Differences were also found depending on the kind and on the number of active tasks under exam. This result suggests that, when involved in active tasks, the cingulate cortex reconfigures its patterns of co-activation. In this view, the cingulate cortex may modify its connectivity from a resting state to a "working state": We, propose that such a modification may be related to the kind of task executed and consequently to how subpopulations of neurons modify their own specific time courses to begin to cooperate with different neuronal groups (see also Leech et al., 2011). This implies that such subdivisions are probably related to the kind of task and that, depending on the kind of task that is included in the analysis, they may change. In this sense, it may be argued that not even the MACM results allow a generalization of a "functional clustering" of the cingulate cortex, as also in BrainMap some tasks are more represented than others. The results of the experiments seem to support this view. However, two elements should be considered. First, these observations do not hamper the main results of our study, namely that the cingulate cortex reconfigures to a more complex parcellation when involved in active tasks. Second,

**FIGURE 8 | Control experiments.** Connectivity-based parcellation of the cingulate cortex in the three datasets. Probabilistic maps for functional connectivity defined clusters. The color scheme represents the probability of overlapping brain areas in each voxel across all the

subjects. Maps are projected on an inflated 3D brain surface with the BrainVoyager QX surface tool. This parcellation was obtained by imposing the same number of clusters obtained for the parcellation of the MaC data.

although some tasks are indeed more represented than others in BrainMap, it is also true that some cognitive functions such as attention are common across a great variety of tasks and thus more likely to be always recruited. Indeed, a growing body of literature indicates that the intrinsic functional connectivity of a brain region is modulated in function of a task, both during and after task execution (Liu et al., 1999; Friston and Buchel, 2000; Lowe et al., 2000; Hampson et al., 2002; Jiang et al., 2004; Peltier et al., 2005; Waites et al., 2005; Fransson, 2006; Hasson et al., 2009; Lewis et al., 2009; Tambini et al., 2010) and that the functional connectivity varies adaptively with a local efficiency that is higher locally and lower globally (Wang et al., 2012). Here, we did not directly measure functional connectivity, but used MACM, which is meant to assess the consistency of co-activations. However, it has been shown that MACM and resting state functional connectivity may lead to similar results (Cauda et al., 2011a). It is difficult to explain how such co-activations of brain areas across different paradigms and studies may emerge in absence of any functional connectivity between these areas [for a discussion see Koski and Paus (2000), Postuma and Dagher (2006), Laird et al. (2009b), Smith et al. (2009)]. Functional co-activations may be thus interpreted as forms of functional connectivity (Koski and Paus, 2000; Postuma and Dagher, 2006; Laird et al., 2009b; Smith et al., 2009). Furthermore, the mapping of functional connectivity via coordinate-based meta-analysis has been validated by comparing the results of MACM to resting-state connectivity (Smith et al., 2009). Both approaches produced very consistent results.

Another important question regards the possibility that complex "interdigitated" results may be related to under or overdecompositions in the clustering step. This issue specifically applies to clusterization in the control experiments. We used two different approaches to investigate if the cingulate cortex reconfigures its pattern of connectivity from resting state to "working state." First, we kept the number of clusters of the in-house acquired datasets equal to the number of clusters calculated for the meta-analytic data in order to have a one-by-one comparison between clusters. The logic behind this choice was to compare how the pattern changes across different levels of task complexity. Indeed, the evidence of greater functional complexity of a given region can be reflected either by an increase in the number of clusters (some of which probably very small) or by a more complex and interdigitated clusterization (as in our case). In addition, it should be considered that, as evidenced by Figure S2, single behavioral categories of stimuli do not activate contiguous cingulate areas but wide regions of interdigitated (sparse) blobs. This further supports the view that "sparseness" predominates in brain connectivity (Daubechies et al., 2009). However, as a second approach, in order to validate the results obtained with the first, we performed a parcellation of the resting state and task results, without imposing to the experimental datasets the same number of clusters as in the MaC. The results of this second analysis confirmed what we proposed with the previous approach, namely that from resting state to experimental tasks, there is an increase in the number of identified clusters (from 2 of the resting state to 5 of the pain task).

#### **LIMITATIONS OF THE STUDY; NEUROBIOLOGICAL MEANING OF THE RESULTS AND CONTROVERSIES**

Our results seem at odds with previous neurobiological evidence showing many additional partitions in the cingulate cortex (Bush et al., 2000; Vogt, 2009). However, some elements should be borne in mind when interpreting our findings. First, different parcellation schemes may co-exist in associative areas such as the insula (Cauda and Vercelli, 2012; Kelly et al., 2012). This could be explained by the intrinsically hierarchical nature of brain networks, as disclosed by neuroimaging studies (Doucet et al., 2011; Power et al., 2011; Yeo et al., 2011). Second, functional properties of these regions do not necessarily correspond to the anatomical subdivisions. For instance, it has been proposed that different tasks and stimuli may activate the very same region of the cingulate cortex as this area may be devoted to elaborate some common characteristics of the tasks (Vogt, 2005). Third, the aim of our paper was not to provide compelling evidence to the parcellation of the cingulate cortex when during tasks. This is difficult, as in meta-analytic studies, many papers on different behavioral domains are pooled together. Thus, it may well be that different behavioral domains lead to different co-activations and thus to different parcellations. This is what we show with the control experiments. In addition, it has been demonstrated that clusterization of brain networks likely produces sparse rather than segregated results (Daubechies et al., 2009). All these elements may explain why it is possible to obtain a clusterization of the cingulate cortex that is different from previously suggested ones. In addition, it has been shown that individual anatomical differences may produce slightly diverging results when parcellating the cingulate cortex (Beckmann et al., 2009). We could not control for these factors in the meta-analytic study, thus our results might have been affected also by this. Importantly, the main objective of our study was to show that when the cingulate cortex is studied during tasks, the co-activations of its subparts diverge in a way that, when clustered together, is different from what we have known on the cingulate cortex so far. Finally, previous

## studies using meta-analytic procedures have already shown that our understanding of the cingulate cortex during tasks remains behind its anatomical understanding. Our clustering is based mainly on co-activations (MACM procedure), thus in this sense it is possible that a part of the cingulate cortex, as for example the anterior cingulate cortex, is connected with others as they are functionally linked at a network level, although the two components remain anatomically segregated.

In conclusion, we have shown that the cingulate cortex changes its pattern of co-activations depending on the level of task complexity. In addition, clusters resulting from a parcellation of the cingulate cortex during tasks, rather than being strongly functionally segregated, are likely to reflect the activity of hub areas densely interconnected with local and whole-brain networks. This opens up the possibility that areas that appear to be active for a wide range of active tasks are differentially recruited by each of them and echo the activity of other networks in the brain (Leech et al., 2011).

## **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at http://www.frontiersin.org/Human\_Neuroscience/10.3389/fnhum. 2013.00275/abstract

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 16 March 2013; accepted: 27 2013. May 2013; published online: 154 June*

*Citation: Torta DME, Costa T, Duca S, Fox PT and Cauda F (2013) Parcellation of the cingulate cortex at rest and during tasks: a meta-analytic clustering and experimental study. Front. Hum. Neurosci. 7:275. doi: 10.3389/fnhum. 2013.00275*

*Copyright © 2013 Torta, Costa, Duca, Fox and Cauda. This is an openaccess article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Functional connectivity networks with and without global signal correction

## *Satoru Hayasaka1,2\**

*<sup>1</sup> Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA*

*<sup>2</sup> Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Amir Shmuel, McGill University, Canada Mingrui Xia, Beijing Normal University, China*

#### *\*Correspondence:*

*Satoru Hayasaka, Department of Biostatistical Sciences, Wake Forest School of Medicine, Medical Center Blvd., Winston-Salem, NC 27157, USA e-mail: shayasak@wakehealth.edu*

In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.

**Keywords: resting-state fMRI, brain network analysis, brain networks, network modules, fMRI analysis, graph theory, functional connectivity**

## **INTRODUCTION**

Since the early days of fMRI, neuroimaging researchers have documented highly correlated time courses in distinct brain areas even when a subject is not engaged in a cognitive task. For example, Biswal et al. described strong correlation between the left and right motor cortices while the subjects were at rest (Biswal et al., 1995). Another well-documented example is a collection of brain areas, known as the default mode network (DMN), that exhibit similar time courses when subjects are at rest (Raichle and Snyder, 2007). Brain areas following a highly correlated time course despite the lack of external stimulus or cognitive engagement are often referred as *functionally connected*. Conversely *functional connectivity* between distinct brain areas can be assessed by examining the temporal correlation or coherence between the recordings from those areas. While early connectivity studies focused on functional connectivity to/from a particular seed region in the brain (for example, Greicius et al., 2003; Fox et al., 2005, 2006), in recent years, functional connectivity among different brain areas is often examined in the form of functional connectivity networks (Eguiluz et al., 2005; Salvador et al., 2005; Achard et al., 2006). Such a brain network can be constructed by examining functional connectivity originating from each distinct brain area, and organizing such connections from all the brain areas in the form of a network, with each node representing a brain area and each edge representing functional connectivity between two nodes (or brain areas) (Bullmore and Sporns, 2009; Bullmore et al., 2009; Rubinov and Sporns, 2010).

When constructing a functional brain network, it is important to process fMRI data in a way that the resulting network does not include erroneous functional connectivity resulting from confounding biases or signals not necessarily of a neurological origin. Thus, in order to construct a network, it is a common practice to pre-process fMRI data before assessing functional connectivity. In particular, a band-pass filter is applied to focus on low frequency BOLD fluctuations (Cordes et al., 2001; Fox et al., 2005; Van Dijk et al., 2010). In addition, rigid-body transformation parameters, generated during motion correction and alignment, are regressed out from fMRI time series data to lessen the impact of motion in the connectivity analysis (Fox et al., 2005). Physiologically confounding noises also need to be corrected. This is often carried out by regressing out the average time courses from the ventricles, white matter, and/or the whole-brain (Fox et al., 2005), often referred as global signals.

Among the pre-processing steps described above, regressing out global signals is somewhat controversial. The controversy stems from an argument that regressing out the average wholebrain signal inherently induces negative correlation, or anticorrelation (Murphy et al., 2009). There have been a number of studies supporting or refuting the need for global signal regression in connectivity analyses (Chang and Glover, 2009; Fox et al., 2009; Weissenbacher et al., 2009; Van Dijk et al., 2010; Anderson et al., 2011; Carbonell et al., 2011; Chai et al., 2012; He and Liu, 2012). Interestingly, some of these studies found that the distribution of correlation coefficients is positively biased (Fox et al., 2009; Murphy et al., 2009; Chai et al., 2012). Moreover, Fox et al. (2009) found that extensive brain areas are positively correlated with the whole-brain signal; this may explain the positive bias in the correlation coefficients since a large number of brain areas exhibit correlation to the same signal.

It should be noted that most of these studies described above are seed-based functional connectivity studies, in which correlation coefficients are calculated between the time series from a particular seed region and each individual voxel in the brain. On the other hand, in graph-theory-based network analyses, functional connectivity networks are constructed by calculating correlation coefficients in all possible pairs of brain areas or voxels. Thus, it is still unclear how correcting for the global signal affects the resulting functional connectivity networks. During construction of functional connectivity networks, emphasis is often placed on highly positive correlations rather than negative correlations or anti-correlations. Moreover, investigators routinely select a certain proportion of high correlation coefficients to define edges in their networks; thus a positive bias in the distribution of correlation coefficients may not impact the network structure. Therefore, in this report, I investigate the impact of (not) regressing out global signals in functional connectivity networks. In particular, I constructed networks with and without global signal correction using the resting-state fMRI data from the same set of subjects. Then I examined how the network organization differed between these networks. Namely, I focused on the distribution of correlation coefficients, the locations of high degree nodes—or hubs, and the modular organization in voxel-based functional connectivity networks.

#### **MATERIALS AND METHODS**

#### **fMRI DATA**

I used the same dataset as the study described in Hayasaka and Laurienti (2010). I used this data set since it has been extensively studied and characterized in my previous work (Hayasaka and Laurienti, 2010). This data set consisted of fMRI time series data from 10 normal subjects (5 females, average age 27.7 years old, *SD* = 4*.*7). The fMRI data were acquired while the subjects were resting using a gradient echo echo-planar imaging (EPI) protocol with *TR/TE* = 2500/40 ms on a 1.5 T GE MRI scanner with a birdcage head coil (GE Medical Systems, Milwaukee, WI). Other acquisition parameters included: 24 cm field of view, and 64 × 64 acquisition matrix. The time series data included 120 images acquired over 5 min. The acquired images were corrected for slice timing and motion, and subsequently were realigned. Then the images were spatially normalized to the MNI (Montreal Neurological Institute) space and re-sliced to 4 × 4 × 5 mm voxel size using an in-house processing script based on the SPM package (Wellcome Trust Centre for Neuroimaging, London, UK). The resulting fMRI time series data were band-pass filtered (0.009–0.08 Hz) to attenuate respiratory and other physiological noises. These processing steps are widely used in fMRI functional connectivity studies (Fox et al., 2005; Van Den Heuvel et al., 2008). More details on my data pre-processing steps can be found elsewhere (Hayasaka and Laurienti, 2010; Joyce et al., 2010).

### **GLOBAL SIGNAL REGRESSION**

I considered four different methods of global signal correction. Although there are many possible ways of correcting global signals, examining a large number of such methods may be beyond the scope of this work. Thus, I focused on the methods that have been widely used in the literature examining the impact of global signal correction (Chang and Glover, 2009; Fox et al., 2009; Murphy et al., 2009; Weissenbacher et al., 2009; Van Dijk et al., 2010; Anderson et al., 2011; Chai et al., 2012; He and Liu, 2012; Hallquist et al., 2013). Mean time courses from the entire brain (the average of voxel values within the brain parenchyma mask including gray and white matter), the deep white matter (average time course in an 8 mm radius sphere within the anterior portion of the right centrum semiovale composed entirely of white matter), and the ventricles (average of time courses within the ventricle mask) were extracted and used in global signal correction as described below. In the first method, 6 rigid-body transformation parameters, generated during the realignment (note: NOT normalization) step, were regressed out from the fMRI time series data (Fox et al., 2009). This method was referred as the no correction method (NoCorr), since no global signals, besides the motion parameters, were regressed out from the data. This method demonstrated a situation in which global signal correction is completely omitted. In the second method, in addition to the motion parameters as described above, the average time course from the deep white matter and the ventricles were regressed out, but not the average whole-brain signal (Chang and Glover, 2009; Fox et al., 2009; Weissenbacher et al., 2009; Anderson et al., 2011; He and Liu, 2012). This method was referred as the no whole-brain signal method (NoWB). In the third method, only the whole-brain signal was regressed out in addition to the motion parameters (Murphy et al., 2009; Van Dijk et al., 2010; Anderson et al., 2011; He and Liu, 2012). This method was referred as the whole-brain only method (WBonly). Finally, in the fourth method referred as the full method (Full), the motion parameters as well as the average signals from the white matter, ventricles, and whole-brain were regressed out (Fox et al., 2009; Van Dijk et al., 2010; Chai et al., 2012; Hallquist et al., 2013). The Full networks served as the baseline in this study, characterizing differences in the network organization when one or more global signal variables are omitted. **Figure 1** describes the overview of the different methods. It was noted by one of the reviewers that regression after filtering has been criticized by some studies (Hallquist et al., 2013; Saad et al., 2013).

### **NETWORK CONSTRUCTION**

Processed in one of the four methods described above, the fMRI time series data from each subject were then used to construct a functional brain network, with each node representing a voxel and each edge representing a strong linear correlation between two voxel time courses. To ensure all the networks from all the subjects have the same set of nodes, a binary mask image was generated comprising 15,996 voxels within the AAL (automated anatomical labeling) atlas (Tzourio-Mazoyer et al., 2002). Among these voxels within the mask, a cross-correlation matrix was calculated, with each element being the correlation coefficient between two voxel time courses. The resulting correlation matrix

were regressed out (Full).

consisted of 255,856,020 correlation coefficients (excluding the main diagonal elements, which are 1).

series (NoCorr). In the second method, in addition to the motion

I then examined the distribution of the correlation coefficients in the correlation matrix. The exact marginal distribution of each correlation coefficient *r* is

$$f(r) = \frac{\Gamma((t-1)/2)}{\pi^{1/2}\Gamma((t-2)/2)} \left(1 - r^2\right)^{(t-4)/2} \tag{1}$$

where *t* is the number of time points (Johnson et al., 1995; Cao and Worsley, 1999). However, correlation coefficients in the correlation matrix are not independent. Rather, collectively they represent a 6-dimensional "*connexel*" field (Worsley et al., 1998; Cao and Worsley, 1999). Consequently the collective distribution of all the correlation coefficients in this correlation matrix does not follow (1). Nevertheless, since the marginal distribution (1) is centered around 0 and symmetric, the histogram of the correlation coefficients should be centered at 0 and symmetric. Any deviation from mean = 0 can be an indication of a systematic bias in the correlation matrix. Or, if there is a true global signal present in all the voxels that also biases the distribution of correlation coefficients. Let *W*<sup>1</sup> = *Y*<sup>1</sup> + *G* and *W*<sup>2</sup> = *Y*<sup>2</sup> + *G* be two voxel time courses, where *Y*<sup>1</sup> and *Y*<sup>2</sup> indicate intrinsic time courses in both voxels and *G* is the global signal present in both *W*<sup>1</sup> and *W*2. If the global signal *G* is uncorrelated with neither *Y*<sup>1</sup> nor *Y*<sup>2</sup> [i.e., Cov(*Y*1, *G)* = 0 and Cov(*Y*2, *G)* = 0], then the covariance between the two voxel time courses *W*<sup>1</sup> and *W*<sup>2</sup> is

$$\begin{aligned} \text{Cov}(W\_1, W\_2) &= \text{Cov}(Y\_1 + G, Y\_2 + G) \\ &= \text{Cov}(Y\_1, Y\_2) + \text{Cov}(G, G) \\ &= \text{Cov}(Y\_1, Y\_2) + \text{Var}(G) \end{aligned}$$

The variance of *W*<sup>1</sup> and *W*<sup>2</sup> are Var(*W*1*)* = Var*(Y*1*)* + Var*(G)* and Var(*W*2*)* = Var*(Y*2*)* + Var*(G)*, respectively. Thus, even if *Y*<sup>1</sup> and *Y*<sup>2</sup> are uncorrelated [i.e., Cov(*Y*1, *Y*2) = 0], the correlation coefficient between *W*<sup>1</sup> and *W*<sup>2</sup>

$$\begin{aligned} \text{Corr}\left(W\_1, W\_2\right) &= \frac{\text{Cov}(W\_1, W\_2)}{\sqrt{\text{Var}(W\_1)\text{Var}(W\_2)}} \\ &= \frac{\text{Var}(G)}{\sqrt{(\text{Var}(Y\_1) + \text{Var}(G))(\text{Var}(Y\_2) + \text{Var}(G))}} > 0 \end{aligned}$$

is always positive since Var(*G*) is always positive. Because of this, the distribution of correlation coefficients in this case no longer follows (1) but follows a non-central form

$$f(r) = \frac{(1-\rho)^{(t-1)/2} \left(1-r^2\right)^{(t-4)/2}}{\pi^{1/2} \Gamma\left((t-1)/2\right) \Gamma\left((t-2)/2\right)}$$

$$\sum\_{j=0}^{\infty} \frac{\left(\Gamma((t-1+j)/2)\right)^2}{j!} (2\rho r)^j \tag{2}$$

where ρ = Corr*(W*1*, W*2*)*. This distribution is no longer symmetric around 0. In the literature on functional connectivity, there have been some reports that the distribution of correlation coefficients is positively biased when global signals are not corrected (Fox et al., 2009; Murphy et al., 2009; Chai et al., 2012). Thus, to examine whether there is such a systematic bias, I generated a histogram of the correlation coefficients for each method (NoCorr, NoWB, WBonly, or Full) for each subject. The means from the correlation coefficient distribution were compared across different correction methods by paired two-sample *t*-tests.

The correlation matrix from each subject and each correction method was then used to construct a functional connectivity network. In particular, the correlation matrix was thresholded to generate a binary adjacency matrix with 1 indicating the presence and 0 indicating the absence of an edge between two nodes, with each edge representing a strong positive correlation. I chose a positive threshold in a way to control the number of nodes N and the average node degree *K* in the resulting network. In particular, I selected a correlation threshold such that the ratio *S* = log*(N)/* log*(K)* is the same across subjects. I chose *S* = 3*.*0 since it has been shown to capture the network characteristics effectively (Hayasaka and Laurienti, 2010) and the resulting edge density is comparable to that of a self-organized network of a similar size (Laurienti et al., 2011). I examined the results with different values of *S* ranging between 2.5 and 3.5, and the results were similar across *S*-values in comparisons of network characteristics across the methods (results not shown). Thus, throughout this paper, only the results for the networks with *S* = 3*.*0 are shown.

Once the network was generated, various network characteristics were compared. This includes whole-network metrics such as clustering coefficients *C* and path length *L* (Watts and Strogatz, 1998; Stam and Reijneveld, 2007). While *C* represents the probability that a node's neighbors are also neighbors to each other, *L* is the average of shortest distances between any two nodes in a network, in terms of the number of edges separating them or the geodesic distance. These metrics were compared across different methods by paired two-sample tests. Moreover, I examined the consistency of high degree nodes, or hubs, across subjects. This was done by examining the spatial overlap of top 20% highest degree nodes across subjects (Hayasaka and Laurienti, 2010). The resulting overlap images were compared across different correction methods. If global signal correction does not influence the overall structure of the network, then the overlap maps should appear similar across different correction methods. On the other hands, if systematic biases are introduced by global signal correction, or by the lack thereof, then the overlap maps may appear different across the correction methods.

#### **MODULAR ORGANIZATION**

In a network, some groups of nodes may have a large number of connections among themselves compared to connections between such groups. These highly interconnected sets of nodes are often referred as modules. If a network has a modular structure, then its nodes can be grouped into a number of modules, with each node belonging to a single module. The human brain networks have been shown to have modular organization (He et al., 2009; Meunier et al., 2009; Power et al., 2011; Rubinov and Sporns, 2011). Despite the difference in the number of nodes in these previous studies, the number of modules is similar and the modular parcellation is comparable (Moussa et al., 2012). Thus, I hypothesize that, if a lack of global signal correction alters the macro-scale organization of a functional brain network, such altered organization may manifest as changes in the modular organization.

To investigate the modular organization, I applied an algorithm called Qcut (Ruan and Zhang, 2008). Qcut is an iterative algorithm to find a near optimal modular parcellation of a network, by maximizing modularity Q, a metric that quantifies how parcellated a network is relative to a random network of a comparable size. Q is zero if the network exhibits no community structure, whereas a large Q is a strong indicator of community structure in a network (Clauset et al., 2004). The upper limit of Q is 1. For each fMRI network, before running Qcut, I identified sub-networks that were isolated from the largest connected network component (or the giant component), and grouped such nodes into a "*junk*" module. Then the giant component was analyzed by Qcut, resulting in a modular parcellation. The resulting Q was compared across different correction methods, and the consistency of some modules was examined.

#### **RESULTS**

#### **CORRELATION COEFFICIENT DISTRIBUTION**

**Figure 2** shows distributions of correlation coefficients for all the subjects under different correction methods. While the distribution was centered at 0 for all the subjects for the Full and WBonly methods, the distribution was positively skewed in some subjects for the NoWB and NoCorr methods. Between NoWB and NoCorr, the distribution appeared more skewed for NoCorr. This was confirmed by the mean of these distributions. The mean (*SD*) of the mean correlation coefficient across subjects was 0.00006 (0.0004) under the Full method, 0.00006 (0.0004) under the WBonly method, 0.050 (0.035) under the NoWB method, and 0.086 (0.056) under the NoCorr method. I compared the mean correlation coefficient between different methods by paired two sample *t*-tests (since the networks originate from the same set of subjects). I found a significant difference between the Full and NoWB methods (*p* = 0*.*001), as well as between the Full and NoCorr methods (*p <* 0*.*001). However, no significant difference was found between the Full and WBonly methods (*p* = 0*.*70). Significant differences were also found between WBonly and NoWB methods (*p* = 0*.*001) as well as between the WBonly and NoCorr methods (*p <* 0*.*001). These results indicate that the correlation matrix may be systematically biased when the wholebrain signal is not regressed out. These results are consistent with previous reports on seed-based connectivity studies (Fox et al., 2009; Murphy et al., 2009; Chai et al., 2012).

#### **NETWORK METRICS**

**Table 1** shows the average *C* and *L* for the four different methods. While clustering coefficient *C* appeared similar across different correction methods, path length *L* was somewhat larger for the NoWB and NoCorr networks, in comparison to the Full and WBonly networks. Paired two-sample *t*-tests revealed that the path lengths were marginally larger for the NoWB, WBonly, and NoCorr networks when compared to that of the Full networks (*p* = 0*.*09, *p* = 0*.*03, and *p* = 0*.*05, respectively). This may be because the NoWB, WBonly, and NoCorr networks fragmented more than the Full networks. In fact, the size of the largest connected network component Nc, or the size of the giant component, was smaller in the NoWB, WBonly, and NoCorr networks compared to the Full networks (paired *t*-test *p* = 0*.*03, *p* = 0*.*02, and *p* = 0*.*006, respectively) (see **Table 1**). Since my method of path length calculation was based on the reciprocal mean of the geodesic distance between nodes (Latora and Marchiori, 2001; Hayasaka and Laurienti, 2010), disconnected network components were accounted as increased path length. Furthermore, the proportion of connected nodes (i.e., nodes with at least one connection) was much lower in the NoWB and NoCorr networks compared to the Full networks (paired *t*-test *p* = 0*.*04 and *p* = 0*.*01, respectively) (see **Table 1**). However, the proportion of connected nodes was only marginally smaller in the WBonly networks compared to the Full networks

(paired *t*-test *p* = 0*.*08). It should be noted that the difference in the path length *L* as described above cannot be simply attributed to the differences in the distribution of correlation coefficients. This is because a distribution of correlation coefficients does not describe the network structure or topology, as it lacks information on how nodes are connected to each other.

#### **NODE DEGREE DISTRIBUTION**

**Figure 3** shows the degree distributions for the networks constructed with different correction methods. In all the methods, the degree distributions seem to follow an exponentially truncated power-law distribution, as I previously reported (Hayasaka and Laurienti, 2010). However, the shape of the distributions appeared more variables in the NoWB and NoCorr networks. To confirm this, the variance of the largest node degree was compared across different correction methods by an *F*-test. The variability was significantly larger in the NoWB

**Table 1 | Average network metrics.**


*The average network metrics across subjects were calculated for the networks with different global signal correction methods. Clustering coefficient C and path length L, as well as the size of the giant component Nc and the proportion of nodes with at least one connection are presented.*

**FIGURE 3 | Degree distributions.** Degree distributions of the Full, NoWB, WBonly, and NoCorr networks are shown. Although all the distributions seem to follow an exponentially truncated power-law distribution, the degree distributions appear more variable across subjects in the NoWB and NoCorr networks.

and NoCorr networks compared to the Full networks (*F*-test *p* = 0*.*008 and *p* = 0*.*005, respectively), or compared to the WBonly networks (*F*-test *p* = 0*.*01 and *p* = 0*.*008, respectively). However, no significant difference in variability was found between the Full networks and the WBonly networks (*F*-test *p* = 0*.*82).

#### **NETWORK HUBS**

Next, I examined the locations of high-degree nodes, or hubs, in the networks with different correction methods. In particular, the consistency of hub locations was examined by an overlay image of top 20% highest degree nodes (see **Figure 4**). All the methods yielded a concentration of network hubs in the posterior cingulate cortex and the precuneus. This finding was consistent with my previous results (Hayasaka and Laurienti, 2010) as well as the other voxel-based network studies (Eguiluz et al., 2005; Van Den Heuvel et al., 2008; Buckner et al., 2009). However, the NoWB and NoCorr networks also showed a concentration of hub nodes near the superior edge of the interhemispheric fissure while such concentration was not observed in the Full and WBonly networks. To the best of my knowledge, this area has not been reported as the hub area of the brain in voxel-level fMRI networks. Moreover, resting-state MEG (magnetoencephalography) networks often do not exhibit concentration of hubs along the interhemispheric fissure (Bassett et al., 2006; Deuker et al., 2009; Jin et al., 2013; Rutter et al., 2013). The concentration of hub nodes in this area was more consistent and extensive in the NoCorr networks than the NoWB networks. Thus, it is possible that this concentration is an artifact of not correcting for the whole-brain signal. It should also be noted that, while the Full networks showed a concentration of hub nodes in the anterior cingulate cortex, the NoWB, WBonly, and NoCorr networks did not show such a concentration in the same area.

#### **MODULAR ORGANIZATION**

**Table 2** shows the mean modularity Q of the networks under different correction methods, as well as the mean number of modules found in these networks. Compared to the Full networks, modularity Q did not differ significantly in the NoWB, WBonly, and NoCorr networks (paired *t*-test *p*-values, *p* = 0*.*12, *p* = 0*.*20, and *p* = 0*.*05, respectively). However, there were significantly more modules in the NoWB and NoCorr networks compared to the Full networks (paired *t*-test *p* = 0*.*02 and *p* = 0*.*004, respectively). Compared to the WBonly networks, the NoWB and NoCorr networks had significantly more modules as well (paired *t*-test *p* = 0*.*03 and *p* = 0*.*01, respectively). There was no significant difference in the number of modules between the Full and WBonly networks (*p* = 0*.*08). These results indicate that the brain network is parcellated into a larger number of communities when the whole-brain signal is not corrected.

I examined the consistency of the default mode network DMN module across subjects under different correction methods. In particular, for each method, I generated an overlap image of the DMN module, identified manually as the module comprising



*The mean modularity Q from the networks under different correction methods, as well as the mean number of modules are shown.*

concentrated in the posterior cingulate cortex and the precuneus.

WBonly networks.

a large portion of the posterior cingulate cortex and the precuneus, the areas known to be part of the DMN. **Figure 5** shows the overlap images demonstrating the consistency of the DMN. Surprisingly, the DMN overlap images were similar across different methods. This result indicates that the difference in correction methods did not impact the DMN module.

I also examined the consistency of the junk module, the module consisting of nodes and subgraphs disconnected from the giant component of the brain network. **Figure 6** shows the overlap images of the junk module across subjects under different correction methods. While the junk module was not spatially consistent across subjects in the Full and WBonly networks, the junk module consistently included nodes around the major white matter tracts in the NoWB and NoCorr networks. It should be noted that, my network data only consisted of gray matter nodes defined by the AAL atlas. Between the NoWB and NoCorr networks,

**FIGURE 5 | Consistency of the default mode network module.** The overlap of the default mode network module across subjects is shown for the networks with different correction methods. The areas of overlap appear similar across different methods.

network consists of nodes and subgraphs that are disconnected from the giant component. Under each global signal correction method, the consistency of such junk modules across subjects was examined by generating an overlap image. While the junk module showed only

module consistently included nodes around the major white matter tracts in the NoWB and NoCorr networks. Between the NoWB and NoCorr networks, the overlap was more consistent and extensive in the NoCorr networks.

the overlap was more consistent and extensive in the NoCorr networks. To further investigate these differences, I counted the number of nodes in the junk module (i.e., isolated nodes and subgraphs) that are adjacent to major white matter tracts. Such nodes adjacent to white matter tracts were identified from the gray matter voxels constituting a brain network (**Figure 7A**). Among these voxels, ones with white matter probability greater than 40% were identified using the white matter probability map from the SPM package; the resulting mask included nodes that were adjacent to white matter tracts, as it can be seen in **Figure 7B**. The average numbers of junk module nodes within this mask for different correction methods are shown in **Table 3**. Compared to the Full networks, there were more junk module nodes (i.e., isolated nodes and subgraphs) adjacent to white matter tracts in the NoWB, WBonly and NoCorr networks (paired *t*-test *p* = 0*.*02, *p* = 0*.*02, and *p* = 0*.*002, respectively). Compared to the WBonly networks, the NoWB and NoCorr networks had significantly more junk module nodes adjacent to white matter tracts (paired *t*-test *p* = 0*.*02, and *p* = 0*.*003, respectively). These results indicated that, without regressing out the whole-brain signal, some nodes may be systematically disconnected from the rest of the network, especially around white matter tracts.

Next, I examined the accuracy of the gray matter mask used in this study [i.e., voxels belonging to areas identified by the AAL atlas (Tzourio-Mazoyer et al., 2002)]. This was done by first eliminating the nodes adjacent to major white matter tracts (**Figure 7B**) from the whole-brain networks, and then by comparing path length *L* of the resulting networks to that of

**FIGURE 7 | Nodes surrounding white matter tracts.** Among the gray matter voxels included as part of a brain network **(A)**, voxels adjacent to the major white matter tracts were identified **(B)**. These voxels were identified from a white matter mask image from the SPM package, with at least 40% white matter probability.

the whole-brain networks, as suggested by one of the reviewers. As mentioned above, the whole-brain network consisted of 15,996 nodes, whereas the networks without nodes adjacent to white matter tracts consisted of 12,660 nodes. In other words, the network size was reduced by 20%. The path lengths *L* for the network with and without the nodes adjacent to white matter tracts are shown in **Table 4**, along with the *p*-values from a paired *t*-test comparing them. While the path length *L* was significantly shorter without nodes adjacent to white matter tracts in the NoCorr networks (*p* = 0*.*038), no significant difference was found in the other correction methods. The difference may simply be a result of a reduced network size, or there may be a systematic connectivity difference in nodes adjacent to white matter tracts.

## **DISCUSSION**

I have constructed voxel-based functional brain connectivity networks from the same set of resting-state fMRI data but with four different methods of global signal correction. I found that the correlation coefficients were positively biased in the methods without the whole-brain signal correction. The bias was stronger if no global signal was corrected at all. I also found that, without correcting the whole-brain signal, the resulting networks may include a large number of isolated nodes and subgraphs disconnected from the giant component. This resulted in increased path length L, with a stronger effect on the NoCorr networks than the NoWB networks. While high degree nodes, or hub nodes, were consistently observed in the posterior cingulate cortex as

**Table 3 | The number of junk module nodes adjacent to white matter tracts.**


*The number of nodes within the junk modules which are within the white matter adjacency mask (Figure 7, right) is listed for different correction methods.*

#### **Table 4 | The path length** *L* **of the networks with and without nodes adjacent to major white matter tracts.**


*The path length L was compared between the two types of networks by a paired t-test. The resulting p-values are also shown.*

previously reported regardless of the correction method, the networks without whole-brain signal correction exhibited consistent concentration of hub nodes along the superior portion of the interhemispheric fissure. Since this area has not been reported as the hub region in previous research, especially the ones based on neuromagnetic activities observed by MEG, it is likely that such a concentration of hub nodes may be an artifact resulting from a lack of whole-brain signal correction. I also examined the modular organization of the networks with different correction methods, and found that the networks without whole-brain signal correction were parcellated into a larger number of modules. Despite the difference in global signal correction, the DMN module was observed consistently across subjects. I also found that, in the networks without full global signal correction (whole-brain signal in particular), nodes near the major white matter tracts were systematically disconnected from the rest of the network. This was particularly apparent in the NoCorr networks.

As described above, there are some different characteristics between the networks with and without whole-brain signal correction. One possible explanation for such differences is the highly connected area along the superior edge of the interhemispheric fissure in the NoWB and NoCorr networks, in comparison to the Full or WBonly networks. Since the number of edges in a network is indirectly controlled by the way the correlation matrix is thresholded (see Network Construction), an abundance of edges in one area of the network can result in reduced edges in other areas of the network. From **Figure 4**, I can infer that extra edges were allocated near the interhemispheric fissure in the NoWB and NoCorr networks, and these extra edges would deprive connections in other areas of the brain. This resulted in a larger number of disconnected nodes and subgraphs in the NoWB and NoCorr networks compared to the Full or WBonly networks. Such disconnected components concentrate around the white matter tracts, as it can be seen in **Figure 6**. These alterations appeared more pronounced in the NoCorr networks than the NoWB networks. This may be because the NoWB networks are corrected by global signals to a certain degree, while the NoCorr networks are not adjusted by any global signals at all. Such a systematic fragmentation around white matter tracts can be observed even in the WBonly networks, when compared to the Full networks. Despite the alterations in the number of connections as described above, the modular organization of the NoWB and NoCorr networks was not completely altered. In fact, possibly because of the modular nature of the brain networks, the DMN module in the NoWB and NoCorr networks was surprisingly similar to that of the Full or WBonly networks (see **Figure 5**).

In this study, I focused on alterations in various network characteristics when resting-state fMRI data were not corrected for global signals, compared to that of the networks constructed with a global signal correction method regressing out wholebrain, white matter, and ventricle signals (Fox et al., 2006, 2009). However, global signal correction method used for the Full networks is far from perfect. This correction method entails simply regressing out mean signals from the fMRI time series, which is more simplistic than methods using the physiological signals recorded during the fMRI data acquisition (Chang and Glover, 2009). Rather than regressing out the global signals, perhaps a more sophisticated approach, such as principal component analysis (PCA) or independent component analysis (ICA), may be effective in extracting neurologically relevant data from physiological noises (Chai et al., 2012). Although these shortcomings exist, regression-based methods are easy to implement as a part of cross-correlation calculation, since it only involves regressing out a number of nuisance covariates from the fMRI time series. These global covariates can be calculated from the fMRI data itself; thus this would be ideal for re-analyzing fMRI data acquired without the accompanying physiological recording. Thus, this type of global signal correction method would be amenable to various types of existing fMRI data, even those that are publicly available for downloading.

It should be noted that there are infinitely many ways of correcting for global signals, and the four methods presented in this work simply represent popular methods used among neuroimaging researchers. It is possible that there are other correction methods suitable for constructing functional connectivity network. However, the goal of this paper is to evaluate existing methods; my intention is not to develop better correction methods. With increased interests in this field in recent years, it is possible that some brain network researchers will develop methods more suitable than the ones examined in this work.

One limitation in this work is the lack of ground truth in evaluating different correction methods. This is due to computational challenges arising from generating a gold standard with thousands of time series (each corresponding to a voxel time course) with a small number of known correlations among them representing the "true" connectivity in an adjacency matrix. This is a very difficult mathematical problem, as if there were thousands of simulated regions in the simulation described in Saad et al. (2012) and each region's connectivity would have to exactly match the ground truth adjacency matrix.

I also would like to emphasize that this study does not answer whether or not there is a genuine "global signal" that is present throughout the brain. This study only outlines the differences in network organization arising from correcting/not correcting for global signals. There are a number of papers describing the existence of such global signals and consequently discouraging the use of global signal correction (Murphy et al., 2009; Scholvinck et al., 2010; Saad et al., 2012, 2013; Hallquist et al., 2013). Because of the limitations listed above, I cannot conclude which correction method should be used, if used at all. So I will leave that determination up to each reader. If one suspects that there exists a true "global signal" that covers extensive cortical areas due to a brainwide synchronized neurological processing, then a global signal regression is not necessary. However, I would like to reiterate that, without global signal correction, a concentration of hubs appears at the superior portion of the interhemispheric fissure, which cannot be detected by MEG. Moreover, nodes around white matter tracts tend to systematically disconnect from the rest of the brain network if the whole-brain signal is not corrected.

In summary, I demonstrated alterations in networks characteristics resulting from not correcting for global signals. Such alterations include increased connections along the interhemispheric fissure and isolated nodes and subgraphs around the white-matter tracts. However, incomplete global signal correction or lack thereof may not alter some brain network modules, such as DMN. Thus, each practitioner of brain network analysis, especially dealing with networks in voxel-level, should consider the results presented in this work and select an appropriate correction method that is suitable for his/her study.

### **ACKNOWLEDGMENTS**

This study is supported in part by the National Institute of Neurological Disorders and Stroke (NINDS) (NS070917). I would like to thank the three reviewers for their insightful comments in revising this paper. Additional details on MRI data acquisition have been added because of the reviewers' suggestions. Analyses on nodes adjacent to white matter tracts have been added also because of the reviewers' suggestions. The names of these insightful reviewers can be found on the first page of this article.

### **REFERENCES**


**Conflict of Interest Statement:** The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 18 May 2013; accepted: 03 December 2013; published online: 18 December 2013.*

*Citation: Hayasaka S (2013) Functional connectivity networks with and without global signal correction. Front. Hum. Neurosci. 7:880. doi: 10.3389/fnhum.2013.00880 This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Hayasaka. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Addressing head motion dependencies for small-world topologies in functional connectomics

## *Chao-Gan Yan1,2,3\*, R. Cameron Craddock1,2, Yong He4,5 and Michael P. Milham1,2\**

*<sup>1</sup> Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA*

*<sup>2</sup> Center for the Developing Brain, Child Mind Institute, New York, NY, USA*

*<sup>3</sup> The Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, New York University Child Study Center, New York, NY, USA*

*<sup>4</sup> State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China*

*<sup>5</sup> Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China*

#### *Edited by:*

*Alan Evans, McGill University, Canada*

*Reviewed by:*

*Qingbao Yu, The Mind Research Network, USA Alex Fornito, University of Melbourne, Australia*

#### *\*Correspondence:*

*Chao-Gan Yan, Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA e-mail: ycg.yan@gmail.com; Michael P. Milham, Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY 10022, USA e-mail: michael.milham@ childmind.org*

Graph theoretical explorations of functional interactions within the human connectome, are rapidly advancing our understanding of brain architecture. In particular, global and regional topological parameters are increasingly being employed to quantify and characterize inter-individual differences in human brain function. Head motion remains a significant concern in the accurate determination of resting-state fMRI based assessments of the connectome, including those based on graph theoretical analysis (e.g., motion can increase local efficiency, while decreasing global efficiency and small-worldness). This study provides a comprehensive examination of motion correction strategies on the relationship between motion and commonly used topological parameters. At the individual-level, we evaluated different models of head motion regression and scrubbing, as well as the potential benefits of using partial correlation (estimated via graphical lasso) instead of full correlation. At the group-level, we investigated the utility of regression of motion and mean intrinsic functional connectivity before topological parameters calculation and/or after. Consistent with prior findings, none of the explicit motion-correction approaches at individual-level were able to remove motion relationships for topological parameters. Global signal regression (GSR) emerged as an effective means of mitigating relationships between motion and topological parameters; though at the risk of altering the connectivity structure and topological hub distributions when higher density graphs are employed (e.g., *>*6%). Group-level analysis correction for motion was once again found to be a crucial step. Finally, similar to recent work, we found a constellation of findings suggestive of the possibility that some of the motion-relationships detected may reflect neural or trait signatures of motion, rather than simply motion-induced artifact.

**Keywords: functional connectomics, head motion impact, network analysis, resting-state fMRI, small-world, topological parameters**

## **INTRODUCTION**

The graph of functional interactions in the human connectome is increasingly being used as a defining component of an individual's neurophenotype (Craddock et al., 2013). Not surprisingly, cataloging variations in the connectome, from one individual or population to another, has emerged as a key objective in modern day neuroscience. Seemingly simple from a conceptual viewpoint, the task of characterizing and comparing connectomes has proven to be a significant challenge for the imaging community—both due to the computational complexity of the connectome graph and the richness of interactions between its connections and subgraphs (i.e., modules). In response, the examination of connectomes in terms of their network properties has emerged as a potentially promising solution that reduces its complexity to a set of topological parameters (see **Table 1**) that are easily amenable to comparison across individuals and populations (Bullmore and Sporns, 2009). Initial studies have demonstrated the sensitivity of these measures to differences in both diagnostic status and behavioral indices (Bassett and Bullmore, 2009; Bullmore and Sporns, 2009, 2012; He and Evans, 2010; Wang et al., 2010; Bullmore and Bassett, 2011; Yu et al., 2012), and have exhibited acceptable test-retest reliability for these metrics (Telesford et al., 2010; Wang et al., 2011). Although promising, little attention has been given to the potential confounding effects of nuisance signals present in R-fMRI studies—in particular, that of motion, which is the primary focus of the present work.

Although the impacts of motion on graph topological measures have not been thoroughly assessed, the demonstrated deleterious effects of motion on community detection provides compelling evidence of their existence (Power et al., 2012). Previous work has found that the assignment of nodes in the connectome to communities (modules) differed notably between children and adults when motion was not considered, but were more similar when motion was accounted for by the removal of affected frames (i.e., scrubbing) (Power et al., 2012). Beyond this demonstration, a key point raised by Power et al., as well as others (Satterthwaite



et al., 2012; Van Dijk et al., 2012) is that short-distance connectivity increases with motion while long-distance connectivity decreases. However, applying this knowledge to topological parameters does not lead to any direct conclusions. In topological space nodes are deemed neighbors if they are directly connected, regardless of the anatomical distance between them. Thus, although motion may decrease the number of long-distance connections in a node's topological neighborhood, and increase the number of short-distance connections, the overall impact to topological parameters such as local and global efficiency is unclear. Equally unclear is the degree to which the effects of motion are reflected in compromises to small-world properties, which reflect the balance of global efficiency with local efficiency (Watts and Strogatz, 1998; Salvador et al., 2005).

Concerns about the impact of motion on topologic parameters are particularly relevant to studies of inter-individual or population-based differences, where systematic relationships can exist between motion and variables of interest (e.g., developmental status, diagnostic status).

In this regard, several recent studies focusing on seed-based correlation and regional R-fMRI measures (Fair et al., 2012; Satterthwaite et al., 2013; Yan et al., 2013a; Power et al., 2014) have provided comprehensive assessments of motion related artifacts and suggested measures for controlling them. These studies generally emphasize that, if attempting to correct for motion at the individual-subject level, (1) higher-order regression models [e.g., Friston 24-parameter model (Friston et al., 1996)] perform better than lower-order models, (2) including scrubbing approaches is superior to regression models alone, and (3) global signal regression controls for head motion more than any approach attempting to explicitly model motion. Importantly, several studies have suggested that despite the best of efforts, motion cannot be fully accounted for at the individual-subject level and argued that motion may be better accounted for at the group-level (i.e., covariate analysis) when possible (Fair et al., 2012; Satterthwaite et al., 2013; Yan et al., 2013a). While some, or all, of these findings may generalize to graph theoretical analyses, this remains an open issue.

Here, we extend our prior work that examines the impact of motion on seed-based correlation analyses and regional R-fMRI measures (Yan et al., 2013a) to include topological properties. Consistent with our prior work, we assess not only the impact of motion on topologic measures and findings of inter-individual and group-related differences, but the ability of previously established motion correction procedures to account for the confounding effects of motion (see **Table 2**). Importantly, when considering graph theoretical analyses, it is essential to appreciate the potential impact of motion on graph construction, prior to the derivation of topologic measures. In order to address this concern, we begin our examination with an analysis of the impact of motion on the density of graphs derived through correlation coefficient thresholding. Additionally, for all topologic measures and procedures examined, we systematically vary density to establish the robustness of our findings.

## **METHODS**

#### **PARTICIPANTS AND IMAGING PROTOCOLS**

We performed our analyses on publicly available imaging data from the 1000 Functional Connectomes Project (FCP; data are available at http://fcon1000*.*projects*.*nitrc*.*org). Consistent with our previous study (Yan et al., 2013a), data of 176 participants (70 males, 20.9 ± 1.9 years) in the Cambridge dataset were used in



#### *Connection estimation-stage:* **motion was corrected during connection estimation**


## **parameters were regressed out from each topological parameter after their calculation**


#### *Both stages:* **the following motion-related parameters were regressed out from each connection before topological parameter calculation, as well as from each topological parameter after their calculation**


*(Continued)*



*FD, framewise displacement; iFC, intrinsic functional connectivity.*

our main analyses. In addition, data of 176 participants (70 males, 21.2 ± 1.9 years) in the Beijing dataset were used to assess the generalizability of our main analyses. The corresponding institutional review boards approved or provided waivers for the inclusion of anonymized data in the FCP. Data were acquired with written informed consent from each participant.

Participants were instructed to simply rest while awake in a 3T scanner, and R-fMRI data were acquired using an echoplanar imaging (EPI) sequence (Cambridge dataset: repeat time *(TR)* = 3 s, echo time *(TE)* = 30 ms, time points = 119, slice number = 47, voxel size = 3 × 3 × 3 mm3, field of view (FOV) = 216 × 216; Beijing dataset: *TR* = 2 s, *TE* = 30 ms, time points = 235, slice number = 33, voxel size = 3*.*12 × 3*.*12 × 3*.*6 mm3, FOV = 200 × 200). A high-resolution T1-weighted magnetization prepared gradient echo image (MPRAGE) was also obtained for each participant to perform spatial normalization and localization.

## **PREPROCESSING**

Unless otherwise stated, all preprocessing was performed using the Data Processing Assistant for Resting-State fMRI (DPARSF, Yan and Zang, 2010, http://www*.*restfmri*.*net), which is based on Statistical Parametric Mapping (SPM8) (http://www*.*fil*.*ion*.* ucl*.*ac*.*uk/spm) and Resting-State fMRI Data Analysis Toolkit (REST, Song et al., 2011; http://www*.*restfmri*.*net), running in Matlab R2012a (Natick, MA). All volume slices were corrected for different signal acquisition times by shifting the signal measured in each slice relative to the acquisition of the slice at the mid-point of each TR. Then, the time series of images for each subject were realigned using a six-parameter (rigid body) linear transformation with a two-pass procedure (registered to the first image and then registered to the mean of the images after the first realignment). Individual structural images (T1-weighted MPRAGE) were co-registered to the mean functional image after realignment using a 6 degrees-of-freedom linear transformation without re-sampling. The transformed structural images were then segmented into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) (Ashburner and Friston, 2005). The Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) tool (Ashburner, 2007) was used to compute transformations from individual native space to MNI space.

#### **HEAD MOTION CORRECTION STRATEGIES (INDIVIDUAL-LEVEL)**

As identified in our previous study (Yan et al., 2013a), the Friston 24-parameter model performed well in addressing head motion effects, which is consistent with other studies that found higher-order models performed better than lower-order models (Satterthwaite et al., 2013; Power et al., 2014). Thus, we compared the following individual-level correction strategies at the preprocessing-stage in the current study (see **Table 2**):


As scrubbing can result in the removal of a large number of time points (Power et al., 2012, 2013; Satterthwaite et al., 2013; Yan et al., 2013a), to obtain reliable results, we removed subjects who had less than 3 min of data remaining after scrubbing, as done in our previous study (Yan et al., 2013a). This resulted in the exclusion of 18 subjects in the Cambridge datasets from the main analyses, leaving 158 subjects for these analyses.

#### **GLOBAL SIGNAL REGRESSION (GSR)**

GSR is a commonly used, yet controversial practice in the R-fMRI field, that yields substantial increases in negative correlations (Murphy et al., 2009; Weissenbacher et al., 2009) and may distort group differences in intrinsic functional connectivity (iFC) (Saad et al., 2012, 2013; Gotts et al., 2013). However, recent studies have found that GSR is more effective in removing relationships between motion and correlation-based R-fMRI metrics across subjects than any correction strategy that explicitly models motion (Yan et al., 2013a; Power et al., 2014). Thus, we evaluated the effects of head motion correction strategies on analyses performed with and without GSR.

Within the nuisance regression step, linear and quadratic trends were included as regressors to account for low-frequency drifts, and signals from WM and CSF were regressed out to reduce respiratory and cardiac effects, in the BOLD signal.

After nuisance regression, the functional data were transformed to MNI space and resampled to 3 × 3 × 3 mm3 voxel size with DARTEL tool (Ashburner, 2007). Spatial smoothing was not performed to avoid mixing signals between different regions (see section Network Construction). Temporal filtering (0.01–0.1 Hz) was then applied to the time series of each voxel to reduce the effect of low-frequency drifts and high-frequency noise.

## **NETWORK CONSTRUCTION**

The connectome graph is composed of distinct brain regions (nodes) and their functional interactions (edges). The whole brain was first parcellated into 90 cortical and subcortical regions of interest (45 for each hemisphere, see **Table A1**) using a prior anatomical automatic labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002). Although the AAL atlas is widely used in brain network topology analysis, Smith et al. (2011) demonstrated the use of functionally inaccurate ROIs is damaging to network estimation, and thus suggests against structural atlases. Here we also evaluated the networks based on two functional atlas for supplementary analyses: Dosenbach's 160 ROIs which were generated based on meta-analysis (Dosenbach et al., 2010), and Craddock's 200 ROIs which were generated based on spatially constrained spectral clustering (Craddock et al., 2012).

The mean time series of each region was extracted by averaging the time series of all voxels within that region. Pearson's correlation coefficients were estimated for each pair of regions and were transformed to Fisher's *z*-score (Fisher, 1915) to create the iFC matrix for each participant. The correlation matrices were further thresholded into binary networks or weighted networks to examine the head motion impact on binarized topology or weighted topology. Two thresholding strategies are widely used: correlation coefficient thresholding and density thresholding; each has its own limitations (Fornito et al., 2013). The correlation coefficient thresholding strategy resulted in networks with densities (the number of existing edges divided by the maximum possible number of edges) that are sensitive to head motion (see results in the section "Head Motion Impact on Graph Construction"); this in turn affects the topological properties. As such, we used the density thresholding strategy to normalize the number of edges among all of the graphs. A wide range of density thresholds (2% ≤ density ≤ 50%, step of 2%) was chosen to allow prominent small-world properties in brain networks to be observed (Watts and Strogatz, 1998) (for details, see the Results section).

While the primary focus of the present work is on graphs derived using full correlation (Pearson's correlation), we also felt that it is important to address potential differences when partial correlation-based graphs are used instead. Partial correlationbased approaches should inherently remove signals present throughout the brain; as such, we predicted that graphs generated from partial correlation should be more robust to motion. Of note, a key limitation for partial correlation approaches is that the covariance matrix is not invertible for most R-fMRI datasets due to the limited number of time points relative to the large number of nodes. This challenge is compounded by additional losses in the number of degrees of freedom produced by temporal filtering. In order to address this, we utilized the graphical lasso method to estimate the sparse inverse matrix through L1 norm (lasso) regularization (Friedman et al., 2008) (http://www-stat.stanford. edu/∼tibs/glasso/). We systematically varied the regularization penalties <sup>1</sup> to acquire matrices with the desired density (2% ≤ density ≤ 50%, step of 2%) for each participant.

#### **NETWORK ANALYSIS**

We investigated both the global and regional topological properties of brain graphs (**Table 1**). At the global level, we investigated local efficiency, global efficiency, clustering coefficient, characteristic path length, normalized clustering coefficient, normalized characteristic path length, small-worldness, assortativity and modularity. At the regional level, we computed degree centrality, nodal efficiency, nodal clustering coefficient, subgraph centrality, betweeness centrality and eigenvector centrality for each node.

All of the topological parameters investigated in the current study are summarized in **Table 1**, and were calculated with the Brain Connectivity Toolbox (Rubinov and Sporns, 2010) (http:// www*.*brain-connectivity-toolbox*.*net). For details about the computation of network parameters, please see (Rubinov and Sporns, 2010).

## **STATISTICAL ANALYSIS**

To examine head motion effects on the topological properties of the connectome graph, we calculated the correlation between head motion and each of the parameters across participants. Head motion was indexed by mean FD derived with Jenkinson's relative root mean square (RMS) algorithm (Jenkinson et al., 2002); mean FD (Jenkinson) was used due to its consideration of voxel-wise differences in motion in its derivation (Yan et al., 2013a).

To investigate the need for group-level motion correction after individual-level correction (Fair et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012; Yan et al., 2013a), we also compared topological parameters derived from subjects in the upper and lower terciles of head motion, as in our prior study (Yan et al., 2013a). The upper and lower motion terciles were created using only females (*n* = 32 / group) to avoid potential confounds associated with sex; age did not differ. Two-sample *t*-tests were performed between the two motion groups to test motion effects with and without group-level correction.

Group-level corrections were performed at two stages: connection-stage and/or topological parameter-stage (**Table 2**). For each stage, two kinds of regressors were regressed out: mean iFC and/or mean FD. The regression of mean iFC is motivated by its ability to address unwanted additive noise as demonstrated in our prior work on standardizing R-fMRI measures (Yan et al., 2013b).

#### **RESULTS**

#### **HEAD MOTION IMPACT ON GRAPH CONSTRUCTION**

Topological parameters derived from graph theoretical analyses are highly sensitive to graph construction. In order to address concerns regarding the potential impact of motion on graph construction, we examined the relationship between mean FD and mean iFC (calculated by averaging the Fisher's *z* value across all connections for an individual). Our findings indicate that mean iFC is highly correlated with motion when GSR is excluded, regardless of the motion correction strategy employed; in contrast, when GSR is applied, mean iFC relationships with motion were more moderate (**Figure 1A**).

We also examined the impact of motion on the density of graphs derived using the correlation coefficient thresholding strategy. As would be expected, the global increase in iFC with motion results in increased density, regardless of the r threshold applied for graph construction (**Figure 1B**). Once again, we found GSR to be a major determinant of our findings, with graph density exhibiting markedly greater relationships (across correlation thresholds) with motion when the data were processed without GSR, rather than with GSR (which diminished nearly all relationships between graph density and motion, regardless of motion correction approaches employed). This is consistent with our prior finding that GSR controls for head motion more than any approach attempting to explicitly model motion (Yan et al., 2013a).

One other consideration that should be noted is the impact of scrubbing on motion-density relationships. Specifically, we found that scrubbing reduces motion-density relationships the most among the individual-level correction strategies when GSR is not used. This benefit was not seen when GSR is used—in fact, the combination of scrubbing and GSR appeared to increase motion relationships relative to GSR alone. This may at first appear to be surprising, but it is important to note that participant data requiring a higher degree of scrubbing will inherently have a higher likelihood of extreme correlation values after scrubbing due to decreases in the number of degrees of freedom; this in turn will increase density (i.e., more edges) (Yan et al., 2013a)—please see an expanded discussion in the section "Reviving or Learning from Global Signal Regression?"

Overall, these results indicate that one should be extremely cautious when using a correlation- or *p*-value-based threshold to construct brain graphs, as the results can be highly confounded by head motion; GSR can alleviate these concerns. Nonetheless, given the impact of head motion on graph construction with correlation-based thresholding, our remaining analyses were carried out using a density thresholding strategy in which the number of graph connections across participants and processing strategies was normalized. We report our main results based on binarized graphs, though our analyses using weighted graphs yielded similar results (see section "Generalizability of Findings").

#### **MOTION-ROBUST SMALL-WORLD PROPERTIES IN THE CONNECTOME GRAPH**

Prior work has demonstrated that human connectome graphs based upon iFC follow a small-world topology (i.e., high clustering and short path lengths linking different nodes) (Salvador et al., 2005; Achard et al., 2006; Achard and Bullmore, 2007; Liao et al., 2011; Yan and He, 2011; Yu et al., 2011). Here, we tested whether the prominent small-world architecture is robust to the various head motion correction strategies, finding that the graphs derived from all the correction strategies retained small-world properties, independent of density level (0.06–0.44) (**Figure 2A**).

<sup>1</sup>In order to achieve densities as close as the desired range, the regularization penalties were varied from 0.0001 to 0.001 in a step of 0.0001, and then from 0.001 to 1 in a step of 0.001, and the penalty resulting in the density closest to the desired value was chosen. Consequently, the selected inverse covariance matrices are very close to the desired densities.

head motion.

When compared with 100 random networks with the same number of nodes, edges, and degree distribution as the observed graph (Maslov and Sneppen, 2002), the brain networks had an almost identical path length (normalized characteristic shortest path length ∼1) but were more locally clustered (normalized clustering coefficient *>*1). Taken together, the current results indicate the previous findings of small-world properties in human functional networks cannot be easily attributed to the presence of head motion. As will be discussed in the following sections, this statement is not intended to imply that head motion does not impact topological parameters.

ROIs of automated anatomical labeling (AAL) atlas. Five preprocessing

We also tested if hub distribution is robust to head motion correction strategies. We first calculated node degree centrality over the range of densities that maintained small-worldness, i.e., 0.06–0.44, and then calculated the area under curve (AUC) for this range. The AUC of degree centrality was averaged across all the participants, and regions with degree *>* mean + one standard deviation (SD) across nodes were identified as hubs (**Figure 2B**). Head motion correction strategies had little impact on the identification of hubs, though once again, the presence of absence of GSR was a major determinant of findings. In the case without GSR, the hubs were predominantly attributed to fronto-parietal network and temporal regions, while shifted into default mode network and insula in the case with GSR. However, there is an important caveat on this finding if one looks at motion-hub distribution relationships for individual density levels, rather than using AUC. The hub distributions are similar between data with and without GSR when the density is low (*<*6%); however, when the density increases, the discrepancy of hub distribution between with and without GSR becomes dominant (**Figure 3A**). This can be explained by the alteration in correlation distribution induced by GSR (**Figure 3B**). The top percentage of connections can be identified either with or without GSR. However, the weaker connections identified will differ as a function of whether or not GSR is applied. In sum, GSR is not only mean-centering the intrinsic connectivities, but can also affect their relative structure as well as hub distribution.

## **HEAD MOTION IMPACT ON GLOBAL TOPOLOGICAL PROPERTIES**

Head motion increased local efficiency while decreasing global efficiency (**Figure 4**). These findings generalized across nearly all densities above 0.1 for global efficiency, but were limited to densities greater than 0.3 for local efficiency. Of note, here the topological properties were derived from graph constructed with density threshold; in other words, relationships with head motion exist in network structure even when the wiring cost (i.e., number of connections) is controlled. When GSR is performed, such head motion relationships are removed.

With regard to small-worldness, we found that motion is negatively associated with small world properties—a finding that generalized across density levels greater than 0.1, and was once again diminished with GSR. To interpret these findings, it is important to understand the impact of motion on the two constituent measures for small-worldness—the normalized clustering coefficient and the normalized characteristic shortest path length. As previously described, higher head motion is associated with an increase in local efficiency (which is equivalent to clustering coefficient) of the constructed graph, and also for degree-matched random networks. The increase in clustering coefficient of the constructed network is less than the increase in degree-matched random networks, leading to a negative correlation between head motion and normalized clustering coefficient. In contrast, the characteristic shortest path length (the inverse of global efficiency) and its normalized version (compared to random networks) were both positively correlated with head motion. Combining the normalized clustering coefficient and normalized characteristic path length, the small-worldness was negatively correlated with head motion. Once again, such an effect is significant in the case without GSR, but almost completely diminished by GSR.

#### **HEAD MOTION IMPACT ON REGIONAL TOPOLOGICAL PROPERTIES**

Next, we evaluated the impact of head motion on regional topological properties; the AUC densities in the range of 0.06–0.44 were used as in section Motion-Robust Small-World Properties in the Connectome Graph. In our prior work, we found degree

centrality was drastically increased with motion, and that relationships with motion were markedly reduced by GSR or Zstandardization (i.e., mean centering + variance normalization) (Yan et al., 2013a). Unlike our previous findings, which were based on a *p*-value-based thresholding strategy (similar to correlation coefficient thresholding), here we found that with density thresholding (i.e., the mean degree was controlled accordingly), both positive and negative relationships with motion were noted for region-wise degree centrality, depending on the specific region examined (**Figure 5**). Interestingly, the degree centralities of precuneus, precentral, fusiform, middle temporal, median cingulate and paracingulate gyri—the hub regions when no GSR is used were positively correlated with head motion. On the other hand, the degree centralities of default mode network regions—medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), angular gyrus, hippocampus and parahippocampal gyrus—were negatively correlated with head motion. Such findings are in line with our prior findings that head motion is positively associated with motor cortex and negatively correlated with the default mode network (Yan et al., 2013a). Of note, head motion associations decreased with scrubbing, but the pattern was similar (i.e., no new regional associations emerged) (**Figure 6**). A key challenge in the interpretation of these findings, which was discussed previously and will be expanded in our discussion, is determining whether or not the motion–BOLD relationships are purely artifactual, or may in part reflect motion-related neural activity or indices of kinetic traits.

Regarding regional topological properties, which reflect local properties, e.g., nodal efficiency and nodal clustering coefficient, we generally found positive relationships with head motion. However, the pattern was reversed for the topological properties that reflect global properties, e.g., betweenness and eigenvector centrality. Subgraph centrality, a measure considered to reflect middle- or meso-scale properties (Zuo et al., 2012), was drastically increased with motion. These findings are consistent with our findings that head motion increased local efficiency while decreased global efficiency (see prior section). Once again, when time points with relatively larger frame-wise displacements were removed via scrubbing, relationships with head motion observed for the various centrality measures were reduced, though the overall patterns remained (**Figure 6**).

When GSR was included in preprocessing, relationships between head motion and regional topological properties were diminished. It is important to note that since we controlled density in our graph construction step, the same amount of highly connected edges were present in the cases of processing with and without GSR—thus removing a major potential confound. The markedly different motion relationships noted with GSR suggest that GSR is not just mean-centering correlation scores, but also

**FIGURE 3 | Impact of density on hub distribution. (A)** Hub distribution across various densities either without GSR (green shaded) or with GSR (pink shaded) derived from the data corrected with Friston 24 model. With stringent density thresholds, the hub distributions are similar between data with and without GSR. When the density increases, the discrepancy of hub

altering the connectivity structure. The manner in which GSR alters this structure remains largely unknown.

### **THE IMPACT OF GRAPHICAL LASSO ON HEAD MOTION RELATIONSHIPS**

When partial correlation (using graphical lasso) was utilized instead of full correlation for estimating connections, we found that topological parameters were insensitive to motion effects at higher density thresholds (e.g., *>*0.25) as compared to those based on full correlation (**Figure 7**). However, head motion effects were more prominent for lower densities (0.05–0.25) when graphical lasso was employed. These results indicate that although graphical lasso removes the variance of other regions when estimating the relationship between two specific regions, it did not remove the "global effect" as addressed by GSR.

Given that we found GSR diminished the relationship between head motion and global topological properties, we tested the effect of GSR on graphical lasso estimates of connectivity using two strategies: (1) the global signal was added as an additional timeseries to the parcellation set; (2) the global signal was regressed out of the fMRI timeseries data prior to performing graphical lasso. In the first case, when the GS timeseries was treated as a signal akin to any ROI's timeseries, the result was identical to those obtained from graphical lasso without the GS timeseries. Of note, this finding did not depend on whether the GS timeseries was calculated by averaging the timeseries across all ROIs, or all voxels. In contrast, regressing out the GS prior to carrying out graphical lasso reduced the effect of head motion as distribution between with and without GSR becomes dominant. **(B)** Scatter plot of Fisher's Z averaged across participants. Most of the top connections can be identified either with or without GSR. However, when the percentage increases, a large portion of connections can be only identified by one procedure but not the other.

previously seen with full correlation. Once again, we found that this did not depend on the specific approach used to calculate the GS; additionally, it did not matter if the GS was regressed before or after filtering. Given that the GS should be theoretically removed by partial correlation or graphical lasso itself, it is not clear why GSR prior to graphical lasso has such an impact.

## **GROUP LEVEL CORRECTION IN ADDRESSING RESIDUAL HEAD MOTION IMPACT**

Previous studies have suggested the necessity of accounting for motion at the group-level when possible (Fair et al., 2012; Van Dijk et al., 2012; Satterthwaite et al., 2013; Yan et al., 2013a). While these reports primarily highlighted the merits of including mean FD as a covariate in group-level analyses, more recent work has suggested additional benefits of correcting each participant's data for global distribution parameters (e.g., the mean R-fMRI for each individual) (Saad et al., 2013; Yan et al., 2013b). Here, we explored the group-level correction targeting two different stages: (1) *the connection*—for each edge, we regressed the correlation scores across subjects on their mean iFC scores and/or motion, and then perform graphical theoretical analysis, (2) *topological parameter*—we added mean iFC and/or mean FD as covariates in group analysis after the topological parameters are calculated. Following the approach of our prior work (Yan et al., 2013a), this was accomplished by comparing a "high"-motion vs. a "low" motion participant group; the upper and lower-motion terciles of females in the publically available Cambridge dataset were used to define these two groups.

In order to carry out group-level correction on global distribution parameters, we first needed to calculate mean iFC. While these values can be calculated from the mean iFC across all ROIs for each participant, as done in the section "Head Motion Impact on Graph Construction", the results can be biased by the atlas used. Here we estimated the mean iFC between all the voxelto-voxel connectivities across the brain (70831 voxels) to avoid such a bias<sup>2</sup> ; as expected, the measure was highly correlated with head motion across subjects (*r* = 0*.*51, *p <* 10<sup>−</sup>11). The following connection-stage corrections were performed and compared: (1) mean iFC regressed; (2) motion (mean FD) regressed; (3) (mean iFC + mean FD) regressed. Consistent with the goal of removing unintended, but systematic, global variations across subjects, mean iFC regression reduced the motion effect when compared to non-correction (**Figure 8**). Directly regressing out head motion from the edges across subjects produced even greater reductions in motion effects. When we regressed out both mean iFC and mean FD the head motion effects were reduced in a similar extent, but this may have the additional benefit of addressing unwanted global variations beyond head motion.

When we performed the group-level correction of topological parameters by including mean iFC and/or mean FD as covariates (topological parameter-stage), significant reductions were noted in the difference between the high motion and low motion terciles. This reduction was significant as compared to uncorrected data, and even compared to the connection-stage group-level correction. We further combined group-level correction at both stages, but without clear benefit as compared to the topological parameter-stage correction.

<sup>2</sup>We calculated the all voxel-to-voxel mean iFC as follows: (1) normalize the time courses of all the voxels to zero mean and unit variance; (2) calculate the mean signal across the brain ("global signal"); (3) calculate correlation between this "global signal" and all the other voxels (a simple dot product and then divided by *n* − 1); (4) calculate the mean value of the correlation coefficients across brain. This mean correlation coefficient is equivalent to the mean of all voxel-to-voxel correlations. This calculation is similar to the L<sup>2</sup> norm method recently proposed by Saad et al. (2013), but in a more intuitive form. To improve the normality of such a value for the purpose of standardization, we converted the mean iFC into Fisher's *z* value.

**FIGURE 5 | Correlations between head motion and regional topological properties were plotted in matrix (A) and on brain surface (B).** The layout of panel **(A)** is the same as **Figure 4** except that each column represents one of the AAL regions. The regional properties were characterized by the area

under the curve (AUC) of each measure integrated within density range of 0.06–0.44 and the head motion correlation with these AUCs was demonstrated in panel **(B)**. The size of spheres denotes the

*(Continued)*

#### **FIGURE 5 | Continued**

strength of correlation, red spheres denote positive correlations, blue spheres denote negative correlations, and green spheres denote

insignificant correlations (*p >* 0*.*05, |*r*|*<* 0.16). "-" in panel **(A)** indicates that the node correlation demonstrated in panel **(B)** is derived from Friston 24 model.

correlations (*p >* 0*.*05, |*r*|*<* 0.16).

#### **GENERALIZABILITY OF FINDINGS**

Finally, we addressed possible concerns regarding the generalizability of our findings to other studies by varying several factors (**Figure 9**): (1) brain parcellation approach; (2) connection type (binary vs. weighted); (3) dataset (Cambridge vs. Beijing). First, we examined the effect of parcellation approach on our findings by repeating our analyses with brain graphs constructed from Dosenbach's 160 spherical ROIs that were generated based on a meta-analysis (Dosenbach et al., 2010) (**Figure 9A**), and Craddock's 200 ROIs that were generated based on spatially constrained spectral clustering (Craddock et al., 2012) (**Figure 9B**). Similar to our findings with AAL, for these two parcellations, we found head motion effects on the global topological parameters in the case without GSR; such relationships were diminished when GSR was employed. Next, we examined the impact of connection type, by repeating our analyses using weighted connections, finding the effect of head motion on the global topological parameters were once again significant without GSR, and diminished when GSR was employed (**Figure 9C**). Finally, we repeated our analyses using the Beijing dataset; the findings generalized well from the Cambridge dataset, further increasing our confidence in them (**Figure 9D**).

each measure integrated within density range of 0.06–0.44. The head motion

#### **DISCUSSION**

The present work provides a comprehensive examination of the relationship between inter-individual differences in commonly used topological parameters and motion, yielding multiple important findings. First, we found that head motion increases iFC throughout the brain, and as such, confounds graph construction when correlation (*p*-value) based thresholds are employed to determine the presence of edges. Density thresholding was used as a means of avoiding this potential confound in the present work. As expected, small-world properties were related to the presence of head-motion, though could not be attributed to motion alone (i.e., small world properties persist after motion correction). Consistent with our prior work, global signal regression proved beneficial with respect to its ability to mitigate relationships between topological properties of the connectome graph and head motion. Consistent with its ability to remove globally present signals, using partial correlation to estimate graph connections also reduced the influences of motion on topological parameters, although not to the degree observed with GSR. Finally, it is worth noting that, consistent with our prior work, group-level corrections were effective in reducing motion relationships for topologic parameters, although they were more effective when applied after graph topological parameter calculation (i.e., as covariates in group level analyses for topological parameters). Importantly, we found that our findings generalized across parcellation sets, connection types (binary, weighted) and datasets.

#### **MOTION-DEPENDENCIES IN GRAPH CONSTRUCTION**

Motion poses a distinct challenge for graph theoretical R-fMRI measures, as it confounds construction of the graph upon which

the parameters are based by inflating the number of edges. The increased wiring cost associated with motion in turn biases topological parameters, regardless of whether they are global or regional. Central to any effort to minimize the relationship between topological parameters and motion, is the minimization of its impact on graph construction. In this regard, we found density thresholding to be superior to correlation or *p*value thresholding as it fixes the number of connections in the brain across participants. This avoids motion-related variation in the number of connections from one participant to the next, which are present when correlation thresholding strategies are employed due to increases in correlation levels throughout the brain inherently produced by motion. However, density thresholding has its own limitations. First, it results in a loss to the biological validity of the analysis, as it is highly unlikely that all individuals have the same number of connections in their brain. Second, the specific correlation threshold making the top n% connections varies across subjects (Fornito et al., 2013) and can be affected by motion and preprocessing strategy decisions—particularly when higher density threshold are employed.

The present work draws attention to group-level correction as a means of accounting for the influences of motion on graph construction and topological parameters. Such approaches can be applied to individual connections prior to graph construction, or to topological parameters calculated after graph construction. Regressing mean iFC and mean FD from each connection prior to graph construction can effectively remove motion-density relationships with respect to correlation and *p*-value thresholding (the correlation between mean FD and density across *r* thresholds are within −0.02 to 0.05), while allowing the density to vary across participants. Although potentially less obvious, our analyses suggest that graph construction with density thresholding is affected by motion as well, and can benefit group-level correction of individual connections prior to graph construction. An interesting finding of the present work is that connection-level

corrections cannot entirely remove motion dependencies for topologic parameters, necessitating group-level covariate analysis for topologic parameters.

#### **REVIVING OR LEARNING FROM GLOBAL SIGNAL REGRESSION?**

Consistent with prior work (Yan et al., 2013a), the most robust finding of the present work was the ability of GSR to remove motion-relationships for R-fMRI metrics. This may at first seem to be a vindication of GSR, or at least an argument for resurgence of usage of GSR, which has decreased in the small world literature in recent years without replacement by an alternative technique for handling motion.

Unfortunately, the picture for GSR is not that simple. Prior demonstrations of the potential for GSR to artifactually exaggerate or introduce negative correlation coefficients (Murphy et al., 2009; Weissenbacher et al., 2009), as well as artifactually alter the covariate structure in group-level analyses (Saad et al., 2012, 2013; Gotts et al., 2013), cannot go unheeded. Nor can concerns about potential difficulties in interpretation of findings with GSR as it's actually GM signal regression (Yan et al., 2013b). In the present work, we found that GSR can do more than just mean-centering data, as the specific connections surviving density thresholding can change with the presence of GSR (regardless of whether aggressive motion corrections, such as scrubbing, were applied) in turn producing drastic changes in topological parameters as well as hub distributions (**Figure 10**). Our findings suggest that one way to obtain topological properties and hub distributions that are robust to preprocessing strategy, is to adopt a more stringent density threshold (e.g., *<*6%) at which only the top connections survive—as these are the same with or without GSR. One notable caveat in this suggestion is that given our lack of knowledge concerning the true wiring cost of the brain, stringent thresholding may or may not compromise biological validity and/or sensitivity.

The present work explored the interaction of GSR with a number of other approaches thought to remove the impact of nuisance

**FIGURE 9 | Generalizability of the current findings of head motion dependencies.** We addressed possible concerns regarding the generalizability of our findings to other studies by computing the topological parameters based on parcellation set (**A**: Dosenbach's 160 sphere ROIs, **B**: Craddock's 200 ROIs), weighted graph based on AAL atlas **(C)**, and Beijing dataset based on AAL atlas **(D)**. Results without GSR were shaded in green and with GSR were shaded in pink.

signals, including motion—namely, scrubbing, partial correlation and standardization. In the case where data is processed without GSR, we found that scrubbing reduced the impact of motion more than any of the other individual-level correction strategies, though still appeared to be less effective than GSR alone (e.g., the mean correlation between motion and small-worldness across densities 6–44% for Friston 24 + scrubbing: *r* = −0*.*20; while for Friston 24 + GSR: *r* = 0*.*03). Importantly, scrubbing did not produce any of the alterations in hub ranking or other topological parameters that were seen with GSR at higher densities. Thus, the effect of scrubbing is qualitatively different from GSR. Furthermore, when scrubbing was combining with GSR, as recently recommended by Power et al. (2014), the decreases in motion-density relationships produced through GSR alone, were less profound—suggesting a performance decrease. This may be explained in part by the introduction of more extreme correlation values through scrubbing (**Figure 11**). As shown in our prior

work (Yan et al., 2013a), this is to be expected, as scrubbing inherently decreases the degrees of freedom, and systematic differences can be introduced across subjects as a function of the number of frames scrubbed. As suggested by Power et al. (2014), one can try to balance the impact of scrubbing in group comparisons by balancing the number of frames scrubbed between groups, but this cannot be easily accomplished in the study of inter-individual differences.

Under no condition was partial correlation (using graphical lasso) able to remove motion relationships to the extent that GSR was able to. This was surprising, as a signal present throughout the brain due to motion should be accounted for by partial correlation. One possibility that will be discussed in the next section is that residual relationships with motion may reflect the neural correlates of head motion, which would be expected to survive correction with partial correlation. Regarding standardization approaches, we found that regression of mean iFC and

(red). Bottom: The difference in distribution between with and without scrubbing was averaged across the 25 subjects. Scrubbing reduces the correlation coefficients overall in the case of without GSR, i.e., left shifting the distribution. However, in the case of with GSR, the mean-centered distribution (GSR's property) is widened by scrubbing, i.e., scrubbing increased the possibility of extreme correlation values.

mean FD at the connection and topologic parameter stages was effective in removing the majority of relationships with motion, but once again, not as completely as GSR. In sum, GSR appears to possess a unique property that clearly merits future understanding, even if the approach itself is not well justified for continued use by the literature.

the 25 subjects for either without scrubbing (blue) or with scrubbing

#### **MOTION ARTIFACT vs. MOTION-RELATED NEURAL ACTIVITY**

Our prior work raised a key concern in the interpretation of motion-relationships with the BOLD signal and its derivatives namely the possibility that they may in part be driven by the neural origins of motion, or reflect kinetic traits, rather than being solely the product of intensity fluctuations induced by motion (Yan et al., 2013a). In our prior work, this notion was supported by findings that low and high motion framewise displacements had differential effects on the BOLD signal. For individuals with a high frequency of framewise displacements greater than 0.2 mm, we found negative motion-BOLD relationships in the prefrontal areas, where displacements resulting from head motions are greatest; for individuals with particularly high amounts of motion (e.g., children), these negative relationships were even more widespread throughout the brain. Scrubbing largely removed these negative relationships. In contrast, positive motion-BOLD relationships were primarily present in motorrelated cortices (e.g., primary motor, supplementary motor) and were relatively unaffected by scrubbing procedures—suggesting against origins in imaging artifact. One other theme of note arose from our analysis of relationships between differences in motion and differences in R-fMRI metrics across participants. In these analyses, we found that individuals with higher motion tended to have higher scores for a number of R-fMRI measures in motor-related cortices, and lower in default mode regions.

In the present work, we note that individuals with higher motion appeared to be characterized by higher centrality in dorsal parietal and dorsal frontal areas, and lower centrality in the default network—a finding that remains after motion correction approaches, including scrubbing. While this could still be a reflection of problematic effects of the low degree of motion present in the data, we find this highly unlikely. Instead, we posit, that our findings may in fact reflect either a trait marker of individual with higher kinetic traits, or at least higher kinetic states during the scan session. The unique ability of GSR to remove motion relationships is interesting, as it demotes the centrality of those regions that appear to be most associated with motion (even in scrubbed data) and increase the centrality of regions least associated with motion (**Figure 10**). Given that the global signal is known to have neural components (Scholvinck et al., 2010), a link may exist. Nonetheless, future efforts may benefit from working to find novel (and likely multimodal) ways of differentiating between image artifacts resulting from head motion and motion-related neural activity.

## **EMERGING RECOMMENDATIONS FOR OPTIMIZING PROCESSING FOR GRAPH THEORETICAL ANALYSIS: WHAT TO OR NOT TO DO**

While a growing number of studies have begun to revisit the challenges of motion-correction for the purposes of R-fMRI, significant empirical and analytic work is needed before developing guidelines for addressing motion. Nonetheless, the present work has yielded multiple insights to help guide researchers as follows:


#### **LIMITATIONS**

Several limitations in the current work merit consideration. First, the head motion parameters were estimated from the fMRI data themselves, and limited to between-volume motions (i.e., motion occurring within the period of a single scan volume cannot be accounted for). Future studies require objective external measurement of motion to obtain a true gold standard of head motion. Second, simultaneously recorded cardiac and respiratory signals were not available for the dataset used in the current study, which prevented the definitive separation of head motion effects from physiological noise sources as well as meaningful neural signals. Third, the current methods explored graphical lasso as a statistical method to evaluate partial correlation; although effective and generally accepted, alternative approaches exist (e.g., ridge and elastic net) and should be considered for further exploration. Fourth, in order to facilitate group comparisons, we created two groups of participants using mean FD (high motion vs. low motion) for our two-sample *t*-test based analyses; however, mean FD is not all encompassing other aspect of motion attributes can vary across participants and groups in an uncontrolled manner. Additionally, while we controlled sex and age between the two groups, other uncontrolled traits (e.g., IQ, social economical status, extraverts vs. introverts) may differ between the two groups. Future studies may consider the creation of within-subject designs for comparison of motion states, i.e., high motion vs. low motion scans for each subject. Fifth, for the group-level mean FD correction, we only take mean FD itself but not the interaction term (mean FD <sup>∗</sup> Group) into account. If the interaction term is modeled and significant, interpretation of findings related to the main group effect can be difficult. In such a case, methods such as the Johnson-Neyman procedure can be carried out to determine within which range of covariates the main group effect is significant, and which range is not (D'Alonzo, 2004). Finally, in our previous work on standardization (Yan et al., 2013b), we standardized global SD beyond global mean (e.g., method of mean regression + SD division). In the current work, SD division for each individual had no effects on the graph construction, as it doesn't change the relative order of connections for a given participant. Further studies focusing on addressing the multiplicative effects might be helpful in mitigating head motion effects.

## **CONCLUSIONS**

While graph theoretical measures, including local and global topological parameters, possess significant promise for the advancement of our quantification and understanding of interindividual differences in human brain function, they can be profoundly confounded by the presence of motion if not properly accounted for. The present work explored various options to individual-level correction approaches, generating a set of recommendations for future work and demonstrating the continued necessity for using ANCOVA-based corrections at the group-level. A key challenge for the field as it moves forward is to develop empirical and analytic approaches that are capable of differentiating associations with motion between reflective of artifact and reflective of neural signals underlying motion in the scanner, or trait markers.

## **ACKNOWLEDGMENTS**

We thank Dr. F. Xavier Castellanos for his insights and advice in the preparation of this work, as well as his financial support of Chao-Gan Yan. This work was supported by grants from the National Institute of Mental Health (BRAINS R01MH094639 to Michael P. Milham; R01MH081218 to Michael P. Milham; 5R33MH086952 to F.X.C.), the Stavros Niarchos Foundation (Michael P. Milham), Brain and Behavior Research Foundation (R. Cameron Craddock), the National Science Fund for Distinguished Young Scholars (81225012 to Yong He) and the Natural Science Foundation of China (81030028 to Yong He). Additional support provided by a gift from Joseph P. Healey to the Child Mind Institute (Michael P. Milham).

## **REFERENCES**


functional connectivity MRI: a quantitative comparison of preprocessing strategies. *Neuroimage* 47, 1408–1416. doi: 10.1016/j.neuroimage.2009.05.005


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 01 June 2013; accepted: 12 December 2013; published online: 26 December 2013.*

*Citation: Yan C-G, Craddock RC, He Y and Milham MP (2013) Addressing head motion dependencies for small-world topologies in functional connectomics. Front. Hum. Neurosci. 7:910. doi: 10.3389/fnhum.2013.00910*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Yan, Craddock, He and Milham. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## **APPENDIX**

#### **Table A1 | Abbreviations for the regions in the AAL-atlas.**


## A systematic investigation of the invariance of resting-state network patterns: is resting-state fMRI ready for pre-surgical planning?

#### *K. Kollndorfer 1, F. Ph. S. Fischmeister 2, G. Kasprian1, D. Prayer <sup>1</sup> and V. Schöpf <sup>1</sup> \**

*<sup>1</sup> Department of Radiology, Division of Neuro- and Musculoskeletal Radiology, Medical University of Vienna, Vienna, Austria <sup>2</sup> Department of Neurology, Study Group Clinical fMRI, Medical University of Vienna, Vienna, Austria*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

## *Reviewed by:*

*Yong He, Beijing Normal University, China*

*Krzysztof Gorgolewski, Max Planck Institute for Human Cognitive and Brain Sciences, Germany*

#### *\*Correspondence:*

*V. Schöpf, Department of Radiology, Division of Neuro- and Musculoskeletal Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria. e-mail: veronika.schoepf@ meduniwien.ac.at*

**Objectives:** Measurements of resting-state networks (RSNs) have been used to investigate a wide range of diseases, such as dementia or epilepsy. This raises the question whether this method could also serve as a pre-surgical planning tool. Generating reliable functional connectivity patterns is of crucial importance, particularly for pre-surgical planning, as these patterns may directly affect the outcome.

**Methods:** This study investigated the reproducibility of four commonly used resting-state conditions: fixation of a black crosshair on a white screen; fixation of the center of a black screen; eyes-closed and fixation of the words "Entspann dich!" (Engl., "relax"). Ten healthy, right-handed male subjects (mean age, 25 years; SD 2) participated in the experiment. The spatial overlap for different RSNs across the four conditions was calculated.

**Results:** The spatial overlap across all four conditions was calculated for each seed region on a single subject and at the group level. Activation maps at the single-subject and group levels were highly stable, especially for the reading network (RNW). The lowest consistency measures were found for the visual network (VIN). At the single-subject level spatial overlap values ranged from 0.31 (VIN) to 0.45 (RNW).

**Conclusion:** These findings suggest that RSN measurements are a reliable tool to assess language-related networks in clinical settings. Generally, resting-state conditions showed comparable activation patterns, therefore no specific conditions appears to be preferable.

**Keywords: functional connectivity, resting-state network (RSN), resting-state, fMRI, default mode network, reproducibility**

## **INTRODUCTION**

The possibility of performing functional magnetic resonance imaging (fMRI) without stimulation, as an easy way to obtain insight into the spatiotemporal distribution of resting-state networks (RSNs), has revolutionized neuroscience research. It has been demonstrated that RSNs are organized as specific functional networks across the brain, demonstrating characteristic spatial and temporal changes independent of condition (sleep, task performance, rest, anesthesia) or age (fetuses, preterms, infants, adults) (see, e.g., Schöpf et al., 2012a,b). In particular, in patient groups that show a lack of task cooperation, as, for example, in patients with neurodegenerative or neuropsychiatric diseases (Auer, 2008), resting-state fMRI has become quite popular as a method by which to gain new insights into these diseases (Fox and Greicius, 2010). RSNs are typically characterized by spontaneous low-frequency fluctuations (*<*0.1 Hz) and are observed throughout the whole brain.

In several previous studies, state-dependent differences were observed in the functional connectivity of resting-state networks (fcRSN) (Fransson, 2006; Newton et al., 2007; Bianciardi et al., 2009; Yan et al., 2009; Van Dijk et al., 2010). Some of these studies (Yan et al., 2009; Van Dijk et al., 2010) compared the differences between frequently presented resting-state conditions: eyes-open with fixation; eyes-open without fixation; and eyesclosed. Independent of the resting-state condition, subjects or patients are usually instructed to relax, not to think of anything in particular, and not to fall asleep during the scanning session. These studies have shown that the eyes-open conditions evoked higher functional connectivity of the default mode network (DMN) than the eyes-closed condition. Apart from lower-vigilance states, such as mind wandering, day dreaming, or musing about the recent past (Mason et al., 2007; Buckner et al., 2008), the DMN is associated with monitoring the functions of sensory input (Gilbert et al., 2007; Hahn et al., 2007). Therefore, activities within the DMN seem to be attenuated when the eyes are closed. However, Fox et al. (2005) found no differences in the DMN between resting-state conditions for the eyes-closed condition, the eyes-open without fixation in low-level illumination condition, and the eyes-open condition with fixation of a crosshair.

A recently published study on the interpretation of deactivations in neuroimaging studies (Hayes and Huxtable, 2012) suggested resting-state fMRI measurements before and after taskor stimulus-related fMRI experiments would enable better interpretation of activity in the task itself. In this context, the stability of RSN patterns, independent of the resting-state condition, is of major importance.

The variations in the results of fcRSN measurements raise questions concerning the reproducibility, accuracy, and specificity of resting-state fMRI not only for basic neuroscience research. Resting-state connectivity measures are also proposed as a promising practical tool in a wide range of clinical applications, especially for pre-surgical planning (e.g., epilepsy) (see, e.g., Lui et al., 2008; Bettus et al., 2009). Hence, generating reliable functional connectivity measurements is of particular importance. The reproducibility of functional connectivity measurements within one subject and across subjects has been discussed critically. A recent study (Chou et al., 2012) obtained high reproducibility within one subject, but substantial variation in fcRSNs between subjects. However, other investigations found a high degree of reproducibility for functional connectivity in the DMN (Meindl et al., 2010) and in the motor network (Amann et al., 2009) across subjects, as well as a significant correspondence between different statistical methods (Rosazza et al., 2012). In most clinical resting-state studies, factors like differences in disease duration, incidence of precipitating factors, cognitive dysfunction, and surgical outcome, are considered, but the rest-task itself and how patients were instructed to perform in the resting-state has been given less attention.

In this study, we investigated the reproducibility of RSNs in a homogenous group of 10 healthy subjects, who underwent four different resting-state sessions, while factors that could potentially influence reproducibility were held constant. These four resting-state conditions have been commonly used in previous studies: (1) crosshair (rest\_cross): participants had to fixate on a black crosshair on a white screen; (2) black screen (rest\_black): subjects were instructed to focus on the center of a black screen; (3) eyes-closed (rest\_eyes\_closed): participants had to rest with their eyes-closed; and finally (4) subjects were asked to fixate on the presented words "Entspann dich!" (Engl., "relax") written in black letters on a white screen (rest\_relax).

The prospective use of resting-state fMRI measures in a clinical set-up for the purpose of enabling pre-surgical planning, it is of crucial importance that the designs generate a stable and reliable signal. As fMRI reproducibility characteristics can be strongly dependent on the chosen paradigm designs (Bennett and Miller, 2010), it is important to formally examine reproducibility measures for specific conditions. Therefore, we analyzed differences in functional connectivity patterns induced by the four restingstate conditions (1–4) in distinct networks: the DMN; the visual network (VIN); the sensorimotor network (SMN); the reading network (RNW); and the auditory network (AUD).

## **MATERIALS AND METHODS**

## **SUBJECTS**

Ten healthy, right-handed male subjects (mean age, 25 years; SD 2) were included in the study. All participants had normal or corrected-to-normal vision. All subjects were informed about the aim of the study and gave their written, informed consent prior to inclusion. The study was approved by the Ethics Committee of the Medical University of Vienna. Measurements were performed at approximately the same time of the day, between 5:00 and 9:00 p.m.

## **IMAGING METHODS**

Measurements were performed on a 3 Tesla TIM Trio system (Siemens Medical Solution, Erlangen, Germany) using singleshot gradient-recalled echo-planar imaging (EPI). Twenty slices (1 mm gap, 4 mm thickness) with an FOV of 210 × 210 mm and TE/TR 42/2000 ms were acquired. Slices were aligned with the connection line between the anterior and posterior commissure. Each subject underwent four resting-state conditions, each lasting for 5 min. The light in the scanning room was turned off throughout the whole measurement period.

### **RESTING-STATE CONDITIONS**

Across all four conditions, participants were instructed not to think of anything in particular and not to fall asleep.


Eye movement and fixation of the target were monitored using an MR-compatible eye tracker (ViewPoint EyeTracker, Arrington Research, Scottsdale, AZ) at all times. The order of the runs was identical for all subjects.

#### **DATA ANALYSIS**

Image preprocessing for all four runs was performed separately with SPM8 (http://www*.*fil*.*ion*.*ucl*.*ac*.*uk/spm/), including slice-timing and motion correction, normalization to an MNI template, and smoothing. Correlation maps were generated by computing the cross-correlation coefficient on a single-voxel basis for different regions of interest (ROIs; see **Figure 1**). Seed regions were chosen according to previously published literature and specified on the standard brain using the WFU PickAtlas

(Maldjian et al., 2003, 2004) and processed using MarsBaR v0.43 (Brett et al., 2002). Chosen seed regions comprised the precuneus (DMN see, e.g., Cole et al., 2010), the right primary visual cortex (BA17; VIN see, e.g., Bianciardi et al., 2009), the right primary motor cortex (BA 4; SMN see, e.g., Biswal et al., 1995), the left reading areas (BA 22, BA 44, BA 45; RNW see, e.g., Koyama et al., 2010), and the left auditory cortex (BA 41, BA 42; AUD see, e.g., Cordes et al., 2000). Correlation maps were converted to *z*-values using Fisher's r-to-z transformation, as implemented in Matlab (Matlab 7.8.0, Release 2009 Mathworks Inc., Sherborn, MA, USA) to enable parametric statistical comparison. Only positive correlations were mapped for this investigation. Single-subject analysis for all four conditions (rest\_cross, rest\_black, rest\_eyes\_closed, rest\_relax) was conducted for all seed regions. A within-subject ANOVA factoring the four conditions was performed separately for all seed regions, followed by T-contrasts to compare all conditions in pairs. All resulting statistical parametric maps were thresholded at *p <* 0*.*001 (uncorrected) using a cluster extent threshold of 10 contiguous voxels.

cortex (BA 4; SMN); **(D)** left reading areas (RNW); and **(E)** left auditory cortex (BA 41, BA 42; AUD).

As a measure of consistency of the spatial patterns, an overlap of activation maps for all four conditions was calculated at the single-subject level and group level [see Equation (1)]. For comparison of consistency across all four conditions and across all possible combinations of three conditions, an overlap was calculated at the single-subject and at the group level.

The spatial overlap for each condition and each seed voxel (Rombouts et al., 1997, 1998) was calculated by

$$R\_{\rm OVERAP}(i) = n \times \frac{A\_{\rm OVERAP}(i)}{\sum\_{j} A\_{j(i)}} \tag{1}$$

where *i* represents the different seed regions, *j* the different resting-state conditions, *n* the number of conditions, *Aj(i)* the quantity of activated voxels for seed region *i* in condition *j*, and *A*OVERLAP(*i*) the quantity of identical supra-threshold voxels for all conditions for seed region *i*. *A*OVERLAP is a measure to describe the quantity of voxels that are activated across all conditions. *R*OVERLAP ranges from 0 (no spatial overlap) to 1 (exact overlap) and can be expressed as a percentage. This measure represents the consistency of the spatial extent of the functional connectivity, independent from the way, resting-state was induced. The application of this whole brain consistency measure was motivated by the fact that we did not want to restrict our measurements to a predefined ROI (e.g., Raemaekers et al., 2007; Caceres et al., 2009).

## **RESULTS**

After successfully completing all four fMRI runs, subjects were asked which of the four resting-state conditions created the greatest state of relaxation. The majority of subjects (6/10) rated rest\_relax, the last condition, to be the easiest condition in which to stay relaxed during the scan. Three subjects declared the rest\_eyes\_closed condition, and one subject the rest\_cross condition, to induce the greatest state of relaxation.

A complete description and visualization of all contrasts, conditions, and seed regions can be found in **Figure A1** and **Table A1** (both in Appendix). Results of the calculated linear T-contrast uncovered significant differences within network-specific areas for most comparisons across all resting-state conditions and all networks analyzed.

#### **SINGLE-SUBJECT ANALYSIS**

Results revealed a high consistency for all investigated RSN across the four conditions within one subject, but considerable differences between subjects (see **Figure 2**). Spatial overlaps of any two conditions within one subject ranged from 0.58 to 0.66 (exemplarily calculated for subject 3 for the DMN; for visualization see **Figure 2**), whereas overlaps of one condition between two subjects ranged from 0.39 to 0.57 (exemplarily calculated for subject 2 and 3 for the DMN; for visualization see **Figure 2**). The highest overlap across all conditions was found for the RNW (0.45), and the lowest overlap was seen in the VIN (0.31; see **Table 1** and **Figure 3**). Results further revealed that all overlaps calculated for any combination of three conditions led to comparable spatial overlap.

#### **GROUP ANALYSIS**

Although group activation maps showed high consistency across all four conditions, significant variations were found in all investigated networks. For the DMN, these differences involved the precuneus, the medial temporal and frontal gyrus, the pre- and postcentral gyrus, the inferior parietal gyrus, and the

**Table 1 | Mean overlap at the single-subject level for all four conditions compared to the overlap of any possible combination of three conditions.**


eyes-closed condition **(E/K)**.

paracentral lobule. Significant changes in the network-specific areas were obtained in the calcarine gyrus, the fusiform gyrus, the lingual gyrus, and the superior and medial occipital cortex. The rest\_cross condition resulted in the highest functional connectivity in the right precuneus, whereas, in the resting-state condition, rest\_relax functional connectivity in the precuneus was found on

Results revealed a substantial consistency across all four conditions **(F/L)** in

left side. The least precuneus functional connectivity was induced by the rest\_black condition.

Significant differences in the VIN have been found across the four resting-state conditions in the calcarine gyrus, the fusiform gyrus, the lingual gyrus, and the superior occipital cortex. All of these brain areas are associated with the **single-subject level.**

**Table 2 | Overlap at the group level for all four conditions compared to the overlap of any possible combination of three conditions.**


VIN. The rest\_cross condition, compared to all other conditions (rest\_black, rest\_eyes\_closed, rest\_relax), induced the highest functional connectivity in the left lingual gyrus and the right fusiform gyrus. The rest\_eyes\_closed condition evoked less functional connectivity in the superior occipital cortex. Significant differences within the SMN involved areas such as the precentral gyrus, the supplementary motor area (SMA), and the postcentral gyrus. A decreased functional connectivity in the left postcentral gyrus was found in the rest\_black condition. For the RNW, differences were found in the left medial temporal gyrus and the left superior temporal gyrus. Results revealed significant differences in the AUD for the superior temporal gyrus, the insular cortex, the postcentral gyrus, and the supramarginal gyrus.

The spatial consistency analysis of the group maps across resting-state conditions revealed substantial overlap in suprathreshold voxels (*p <* 0*.*001, FWE corrected) for all ROIs (see **Table 2**).

The spatial overlap of any possible combination of three conditions was calculated for detecting one specific condition that induces most variability. The results show that all overlaps of three conditions revealed comparable overlaps, leading to the conclusion that no specific conditions appears to be preferable at group level. Therefore, no specific conditions appears to induce more variance in general.

## **DISCUSSION**

fMRI is a validated and frequently used tool in pre-surgical planning for the mapping of language or motor areas (Roessler et al., 2005). Difficulties with this method may arise when patients have restricted motor or language abilities, as active participation is necessary in most fMRI paradigms. In contrast, resting-state fMRI is a method that does not require active task performance. Therefore, it is a promising tool to overcome the challenge of active participation in clinical practice. RSN measurements have experimentally been used in pre-surgical planning (Böttger et al., 2011). To date, initial experience has been gained by comparing fcRSN with functional connectivity patterns generated by a finger tapping paradigm (Zhang et al., 2009). The results of that study revealed high consistency of fcRSN patterns compared to task-based mapping of functional connectivity patterns. Less attention has been given to the impact of the resting-state condition.

In this study, we were able to demonstrate stable networks across different resting-state conditions. An analysis of the robustness of the group-mean response was performed for all restingstate conditions. Differences across the four conditions occurred in all networks investigated. However, we found that the grouplevel activation maps were highly stable across conditions. As previous studies have shown considerable between-subject variations (Chou et al., 2012), the analysis of consistency across the conditions was also calculated on a single-subject level. We were able to replicate these findings as single-subject overlaps for all four conditions differed across subjects within a range from 0.14 to 0.65. According to Gorgolewski et al. (2013), a reliable task may be characterized by a significantly higher within- than betweensubject overlap. Results of our study revealed higher overlaps across different conditions within one subject (overlaps of any two conditions ranging from 0.58 to 0.66) than overlaps for two different subjects for the same conditions (overlaps ranging from 0.39 to 0.57).

Motion related artifacts are one of the most challenging problems in fMRI used for pre-surgical planning (Bullmore et al., 1999; Seto et al., 2001) and at very high field, as parallel imaging reconstruction artifacts, eddy currents and B0 changes due to motion increase (Beisteiner et al., 2011; Robinson et al., in revision). Known problems are head motion in slice selecting direction during one TR causing spin history effects and between image volumes causing signal changes near the ventricles and at the edge of the brain (Friston et al., 1996). Head motion artifacts are particularly severe in patient studies. Gorgolewski et al. (2013) reported stimulus related motion to be the confounding factor in explaining reliability between sessions. Therefore, resting-state fMRI could serve as a supplementary technique in pre-surgical planning not presenting with stimulus correlated motion artifacts.

In addition to the DMN, we extended our analysis to other networks, such as the VIN, the SMN, the AUD, and the RNW. These networks are of increasing importance in clinical and presurgical mapping studies. Thus, our results may be highly relevant for the planning and task specification of future clinical RSN trials.

The results of our single-subject analysis revealed a stable reproducibility for all investigated networks on a single-subject level, but considerable differences between subjects. These findings are in line with the results of Chou et al. (2012), who obtained high reliability of the DMN within one subject, but substantial variations across different subjects by calculating intraclass correlation coefficients (ICC) and coefficients of variance (COV). The highest overlap across all four conditions was found in the RNW (0.45), and the lowest overlap in the VIN (0.31). The poorer overlap in the VIN might have been the result of different visual input. Therefore, a spatial overlap was calculated for any possible combination of three conditions to evaluate if any condition significantly decreases variability. Exclusion of any condition led to a significant increase in spatial overlap, but no difference between any combinations of three conditions could be found. Therefore, no specific resting-state condition appears to be preferable.

A detailed discussion of our data in relation to the published literature is only possible on the topic of the DMN, as the other networks were not part of extensive investigations concerning differences across resting-state conditions. In the following paragraphs, all investigated networks will be discussed in detail.

### **DEFAULT MODE NETWORK (DMN)**

The DMN includes parts of the medial temporal lobe, the medial prefrontal cortex, the posterior cingulated cortex, the precuneus, and the medial, lateral, and inferior parietal cortex (Buckner et al., 2008).

In this study, we found that all four resting-state conditions induced differences in functional connectivity networks within areas that are typically part of the DMN. Yan et al. (Yan et al., 2009) found differences among the three restingstate conditions of eyes-closed, eyes-open, and eyes-open with fixation. The eyes-closed condition evoked less functional connectivity, assuming that more evaluation of sensory information is needed with opened eyes, and therefore, an increased functional connectivity was found for the DMN. However, in our study, we even found differences between the three resting-state conditions with opened eyes. This variability in fcRSN activation patterns might be explained by distinctive eye movements across the four resting-state conditions (Morisita and Yagi, 2001).

For the DMN, we found a stable overlap of *R*OVERLAP = 0*.*71 across all conditions at a group level, representing a consistent spatial extent of functional connectivity independent of restingstate condition.

#### **VISUAL NETWORK (VIN)**

The VIN can be characterized by areas typically involved in processing visual stimuli, such as the mesial visual areas (e.g., striate cortex, lingual gyrus) and lateral visual areas, such as the occipital pole and the occipito-temporal regions (Rosazza and Minati, 2011). The eyes\_closed condition induced less functional connectivity in the superior occipital cortex, which might be explained by the lack of visual input. These findings are in line with a study by Yang et al. (Yang et al., 2007).

The poorest overlap across the four conditions was found for the VIN. This finding could be elucidated by the use of different visually induced fcRSN conditions, which influence the functional connectivity patterns of the VIN.

#### **SENSORIMOTOR NETWORK (SMN)**

In the first report of a RSN (Biswal et al., 1995), spontaneous BOLD fluctuations were found in the sensorimotor cortex, the SMA, and in premotor areas during rest. In this investigation, significant differences across the four resting-state conditions were found in all areas representing the SMN. The finding of decreased functional connectivity in the rest\_black condition is in line with a study by McAvoy et al. (2008), which reported a dissimilarity in networks induced by the conditions eyes-open and eyes-closed, where the first may reflect greater neuronal activity than the latter.

With an overlap of 70% across all four conditions, the SMN was found to be very robust. This high spatial reproducibility makes the SMN an interesting candidate for pre-surgical planning, e.g., to evaluate motor-related areas in patients with restricted motor abilities.

## **READING NETWORK (RNW)**

Language-related areas are commonly examined for pre-surgical planning, using fMRI activation patterns during verb generation tasks, see for example (Holland et al., 2001; Eaton et al., 2008; Szaflarski et al., 2008; Tillema et al., 2008; Karunanayaka et al., 2010). Individual definition of language- and motor-related areas is of high importance, as it is widely accepted that there are no typical language or motor "centers." These functions are rather spread across wide cortical and subcortical networks. As patients with brain tumors or epilepsy often show restricted functioning, resting-state fMRI may act as an outstanding method for pre-surgical planning.

The highest consistency across all four resting-state conditions was obtained for the RNW. With an overlap of 0.82 at the group level, the spontaneous fluctuations across all conditions was extremely stable, hence fcRSN measurements seem to be an appropriate tool for the assessment of a language network in group studies. For any 3-fold combination a minimal spatial overlap of 0.84 was found ranging from 0.84 (excluding the condition rest\_cross) to 0.87 (excluding the condition rest\_eyes\_closed). Thus, all four resting-state conditions examined in this study appear to show robust and reliable functional connectivity in the RNW and are thus appropriate for investigations of the RNW.

#### **AUDITORY NETWORK (AUD)**

The AUD involves the superior temporal gyrus, Heschl's gyrus, the insula, and the postcentral gyrus. The primary auditory cortex is known to interact with the angular gyrus, the supramarginal gyrus, Broca's area, and Wernicke's area.

Our results indicate that, among all conditions, rest\_eyes\_closed and rest\_relax evoked the most differences concerning functional connectivity networks compared to the other conditions. With an overlap of *R*OVERLAP = 0*.*54 in the AUD, the four resting-state conditions induced larger variability compared to the other networks, except the VIN. Yet, the underlying source of this moderate overlap remains unclear.

#### **RATING OF RELAXATION**

The majority of subjects rated the condition, rest\_relax, as most relaxing. In this rest-task, however, less precuneus activation was found in the DMN. DMN activity is generally characterized by the absence of an active task. Without focusing attention on a task, thoughts tend to wander, participants imagine future events, or think about the recent past. In fact, participants perform an active task in the resting-state condition, rest\_relax, by focusing their attention on relaxation. Subjects felt more relaxed, but mind wandering and daydreaming tendencies decreased, as the functional connectivity of the DMN is decreased compared to rest\_cross or rest\_black.

#### **APPLICATION TO CLINICAL PRACTICE**

Resting-state measurements are not only of interest for basic neuroscience research. These measurements have recently been shown to allow promising insights in the epileptic brain in clinical practice (e.g., Lui et al., 2008; Negishi et al., 2011; Morgan et al., 2012).

The results of a study by Lui et al. (2008) revealed a difference in the interictal activity of the precuneus in epilepsy patients with generalized rather than partial seizures, suggesting that the lack of precuneus activation in patients with generalized seizures may contribute to the more severe interictal deficits in cognitive functions. Morgan et al. (2012) also identified resting-state fMRI as a supporting tool for pre-surgical assessment.

According to Negishi et al. (2011), RSN measurements are also an appropriate technique to predict surgical outcome in epilepsy patients. A common treatment method for intractable epilepsy is surgery, which can considerably improve epileptic symptoms. However, surgical therapy is not risk-free, and postsurgical complications can lead to cognitive dysfunction. Therefore, it is necessary to assess the possible benefit of surgical treatment. A recently published investigation by Negishi et al. (2011) showed that preoperative measurements of RSNs may serve as a predictor of surgical outcome, as patients with recurrent seizures after surgery differed in their functional connectivity compared to seizure-free patients.

Seed-based resting-state fMRI must be discussed critically in clinical practice, as mapping of a seed region may cause complications. In pathologically altered brains, an activated region may be located differently, therefore additional variance, based on different locations of the seed regions, must be recognized. Moreover, the performance of seed-based resting-state fMRI is impossible if the seed region is occupied by a tumor or a lesion.

Alterations of RSN connectivity, especially in the DMN, have been shown in neurological and neurodegenerative disorders, such as Alzheimer's disease (Auer, 2008). Initial changes can be observed even in the preclinical stages. For a reliable interpretation of the results, especially in a clinical setting, the stability of generated networks is of crucial importance, and the way the resting-state is induced should be considered. Generally, restingstate conditions with eyes-opened are preferable, as they usually evoke higher functional connectivity. According to our findings, patients who undergo fMRI for investigation of speech- and reading-related areas may also be investigated with eyes-closed conditions, considering patient comfort due to disease-related impairments. For examination of the DMN, resting-state conditions in which subjects or patients are instructed to relax by visual presentation of a word or a sentence (e.g., "relax") should be avoided because of the lowered activity levels in the precuneus. Study protocols for fMRI resting-state measurements should be clearly defined for reliability of functional connectivity patterns and to complete information in reports. In pre-surgical planning, motion artifacts may cause considerable difficulties, as head motion artifacts are particularly severe in patients. Gorgolewski et al. (2013) identified motion as the main confounding factor in task-based fMRI. Therefore, resting-state fMRI may be a useful tool to overcome stimulus correlated motion-artifacts in pre-surgical planning.

## **LIMITATIONS**

A possible limitation of this study is the identical sequence of resting-state conditions, i.e., there was no randomization of the different conditions across subjects. However, it is known from several studies using the Stroop test that reading is an automatic process (see, e.g., MacLeod, 1991), which does not require the subjects' focused attention. Therefore, to prevent possible influence and carry-over effects induced by the rest\_relax condition on the resting-state conditions, the run order was kept identical for all subjects.

Another limitation is eye movements, which have been suggested to possibly influence activity patterns according to Ramot et al. (2011). However, an eye-tracking device in this study was used only to verify that the subject obeyed the instructions and had his/her eyes-opened or closed. Thus, we did not record eye movements or blinking during the scanning session. Such data should be collected in further investigations to determine the exact influence of eye movements on RSNs.

## **CONCLUSION**

We were able to show that the degree of network pattern modulation induced by resting-state conditions varied across the investigated networks. The most consistent results were obtained for the RNW. Our results indicate that RSN patterns were not affected by the resting-state conditions within one subject but a considerable difference of overlap across subjects was obtained. Furthermore, we were able to show that fcRSN were highly stable at the group level, especially for the language-related network. As the overlap is comparable in any combination of three conditions, no specific condition seems to be preferable at single-subject or at group level.

#### **ACKNOWLEDGMENTS**

The authors thank the reviewers for their helpful comments on the manuscript, the subjects for their participation, and D. Prayer, AC, and GT for their special support. V. Schöpf and K. Kollndorfer are supported by the FWF (P23205-B09).

## **REFERENCES**


C. (2013). Single subject fMRI testretest reliability metrics and confounding factors. *Neuroimage* 69, 231–243.


Comprehensive presurgical functional MRI language evaluation in adult patients with epilepsy. *Epilepsy Behav.* 12, 74–83.


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 25 October 2012; accepted: 07 March 2013; published online: 26 March 2013.*

*Citation: Kollndorfer K, Fischmeister FPhS, Kasprian G, Prayer D and Schöpf V (2013) A systematic investigation of the invariance of resting-state network patterns: is resting-state fMRI ready for pre-surgical planning? Front. Hum. Neurosci. 7:95. doi: 10.3389/fnhum. 2013.00095*

*Copyright © 2013 Kollndorfer, Fischmeister, Kasprian, Prayer and Schöpf. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## **APPENDIX**


**FIGURE A1 | Axial mean anatomical images overlaid by brain activation resulting from contrasts for all conditions and networks (***p <* **0***.***001 uncorrected for whole-brain volume analysis).**


**Table A1 | Detailed listing of all condition contrasts analyzed for all networks.**


**Frontiers in Human Neuroscience www.frontiersin.org** March 2013 | Volume 7 | Article 95 | **131**




l med front g (85; 3.67)

l insula (29; 3.87)

> r precentral g (34; 3.56)

 (121; 4.50)

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## PANDA: a pipeline toolbox for analyzing brain diffusion images

## *Zaixu Cui , Suyu Zhong, Pengfei Xu, Yong He and Gaolang Gong\**

*State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China*

#### *Edited by:*

*Hauke R. Heekeren, Freie Universität Berlin, Germany*

#### *Reviewed by:*

*R. Matthew Hutchison, Western University, Canada Christopher J. Steele, Max Planck Institute for Human Cognitive and Brain Sciences, Germany*

#### *\*Correspondence:*

*Gaolang Gong, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, #19 Xinjiekouwai Street, Beijing 100875, China. e-mail: gaolang.gong@bnu.edu.cn*

Diffusion magnetic resonance imaging (dMRI) is widely used in both scientific research and clinical practice in *in-vivo* studies of the human brain. While a number of post-processing packages have been developed, fully automated processing of dMRI datasets remains challenging. Here, we developed a MATLAB toolbox named "Pipeline for Analyzing braiN Diffusion imAges" (PANDA) for fully automated processing of brain diffusion images. The processing modules of a few established packages, including FMRIB Software Library (FSL), Pipeline System for Octave and Matlab (PSOM), Diffusion Toolkit and MRIcron, were employed in PANDA. Using any number of raw dMRI datasets from different subjects, in either DICOM or NIfTI format, PANDA can automatically perform a series of steps to process DICOM/NIfTI to diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that are ready for statistical analysis at the voxel-level, the atlas-level and the Tract-Based Spatial Statistics (TBSS)-level and can finish the construction of anatomical brain networks for all subjects. In particular, PANDA can process different subjects in parallel, using multiple cores either in a single computer or in a distributed computing environment, thus greatly reducing the time cost when dealing with a large number of datasets. In addition, PANDA has a friendly graphical user interface (GUI), allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, PANDA is freely available at http:// www.nitrc.org/projects/panda/. This novel toolbox is expected to substantially simplify the image processing of dMRI datasets and facilitate human structural connectome studies.

**Keywords: PANDA, diffusion MRI, DTI, pipeline, diffusion metrics, structural connectivity, network, connectome**

## **INTRODUCTION**

Diffusion magnetic resonance imaging (dMRI) has become one of the most popular MRI techniques for brain research. dMRI can be used to quantify white matter (WM) property and to virtually reconstruct WM pathways in the living brain (Le Bihan, 2003). Given its unique merits, dMRI has been extensively applied to the study of WM connectivity in both normal and abnormal conditions, leading to a substantial enhancement in our understanding of the role of WM, particularly in brain diseases (Johansen-Berg and Rushworth, 2009).

One popular application of dMRI is to extract various diffusion metrics [e.g., fractional anisotropy (FA) and mean diffusivity (MD)] that putatively reflect WM integrity (Basser and Pierpaoli, 1996; Pierpaoli and Basser, 1996; Beaulieu, 2002). These metrics can be further applied to identify differences in WM integrity across subjects. To perform this type of analysis, multiple sequential image-processing steps (e.g., eddy-current correction, tensor calculation, metric calculation, and normalization) are required. Currently, a number of packages, such as FMRIB Software Library (FSL) (Smith et al., 2004) and DTI-Studio (Jiang et al., 2006), provide a set of functions that can carry out these jobs. However, these packages typically perform the processing step-by-step and subject-by-subject. Obviously, this processing pattern is inefficient, as users have to wait until the preceding steps or until each subject is completely finished before initiating the next step or subject. In addition, this pattern requires a large amount of manual operation, which potentially increases the possibility of processing errors caused by manual mistakes. To date, a toolbox supporting fully automated processing of raw dMRI datasets to diffusion metrics that are ready for statistical analysis is still lacking.

Another popular application of dMRI is to virtually reconstruct WM tracts, referred to as diffusion tractography (Mori et al., 1999; Behrens et al., 2007). Previous studies using diffusion tractography mainly focus on a few specific WM tracts. Recently, accurately constructed entire brain anatomical networks (i.e., the connectome) based on diffusion tractography have attracted a lot of attention (Behrens and Sporns, 2012) and are the key target of the ongoing human connectome project (http:// humanconnectome*.*org/). While the framework for constructing anatomical networks of the human brain (i.e., definition of network nodes and edges) has been established (Hagmann et al., 2008; Gong et al., 2009a,b), it is mainly implemented in-house. The community is in urgent need of a fully automated public tool that can construct anatomical brain networks using dMRI datasets.

Currently, there have been a few packages such as MIPAV (McAuliffe et al., 2001), JIST (Lucas et al., 2010), Nipype (Gorgolewski et al., 2011), and LONI (Dinov et al., 2009), which aim to facilitate automated processing of neuroimaging dataset. Essentially, these packages provide environments for constructing analysis workflows with a number of pre-included processing modules from existing tools (e.g., Camino, FSL, AFNI, FreeSurfer, and SPM), and therefore various automated processing pipelines (e.g., a dMRI processing pipeline) can be developed within these environments. In order to construct pipelines with these packages, users need to choose processing modules and define dependencies and parameters themselves. It is noted that, if particular processing modules are not encapsulated [e.g., JIST does not include Tract-Based Spatial Statistics (TBSS) analysis], users have to develop their own modules and further incorporate them into the environment. While these powerful and sophisticated packages make it possible to generate a dMRI processing pipeline, they are favored by developers, and not end users without programming skills. A ready-for-use pipeline tool for dMRI processing is highly desired, particularly for end users.

Here, we present a MATLAB toolbox named PANDA (a Pipeline for Analyzing braiN Diffusion imAges) for a comprehensive pipeline processing of dMRI dataset, aiming to facilitate image processing for the across-subject analysis of diffusion metrics and brain network constructions. Of note, the processing pipelines in this toolbox have been completely set up, allowing the end-users of dMRI to process the data immediately. Moreover, the processing procedures within this pipeline were carefully designed to follow the recommended practice as possible (Jones et al., 2012). After the user sets the input/output and processing parameters through the friendly graphical user interface (GUI), PANDA fully automates all processing steps for datasets of any number of subjects, and results in data pertaining to many diffusion metrics that are ready for statistical analysis at three levels (Voxellevel, ROI-level, and TBSS-level). Additionally, anatomical brain networks can be automatically generated using either deterministic or probabilistic tractography techniques. Particularly, PANDA can run processing jobs in parallel with multiple cores either in a single computer or within a distributed computing environment using a Sun Grid Engine (SGE) system, thus allowing for maximum usage of the available computing resources.

To assess the usability and validity of PANDA, we apply PANDA to study the age effect (i.e., old vs. young) on the diffusion metrics of WM as well as the topological properties of the WM network. According to previous findings, decreased WM anisotropy and weakened network efficiency are expected in old individuals.

## **MATERIALS AND METHODS**

PANDA was developed by using MATLAB under an Ubuntu Operating System. A number of processing functions from FSL (Smith et al., 2004), Pipeline System for Octave and Matlab (PSOM) (Bellec et al., 2012), Diffusion Toolkit (Wang et al., 2007), and MRIcron (http://www*.*mccauslandcenter*.* sc*.*edu/mricro/mricron/) were called by PANDA. Here, we will describe the procedures of pipeline processing in PANDA, followed by an introduction to the realization of pipelines.

### **PANDA PROCESSING PROCEDURES**

The main procedure of PANDA is shown in **Figure 1** and includes three steps: (1) preprocessing; (2) producing diffusion metrics (ready for statistical analysis); and (3) constructing networks.

### *Preprocessing*

*Converting DICOM files into NIfTI images.* The input files of PANDA can be in either DICOM or NIfTI format. If the input files are in NIfTI format, this conversion step will be skipped. Otherwise, DICOM files will be converted into NIfTI format during this step. The *dcm2nii* tool embedded in MRIcron accomplished this task.

*Estimating the brain mask.* This step yields the brain mask by using the *bet* command of FSL (Smith, 2002). The brain mask is required for the subsequent processing steps. Here, the b0 image without diffusion weighting was used for the estimation.

*Cropping the raw images.* To reduce the memory cost and speed up the processing in subsequent steps we cut off the non-brain space in the raw images, leading to a reduced image size. The acquired brain mask was used to determine the borders of the brain along the three dimensions. The *fslroi* command of FSL was then applied to remove the non-brain spaces.

*Correcting for the eddy-current effect.* Eddy-current induced distortion of diffusion weighted images (DWI), as well as simple head-motion during scanning, can be corrected by registering the DW images to the b0 image with an affine transformation. To achieve this, the *flirt* command of FSL was used. Notably, this registering procedure was applied to all images, with the b0 image of first acquisition used as the target if multiple DWI acquisitions were scanned. It is worth mentioning that while the *eddy\_correct* command of FSL is not called here, the result of this step is exactly the same as the output of *eddy\_correct*. Basically, PANDA just splits the 4D file (the input file of *eddy\_correct*) into a number of 3D files and then performed the affine-registration exactly like *eddy\_correct*. The purpose of this implementation is to avoid the large memory demand when the 4D file size is huge. Finally, the gradient direction of each DWI volume was rotated according to the resultant affine transformations (Leemans and Jones, 2009).

*Averaging multiple acquisitions.* This step will be skipped if there is only one DWI acquisition. Otherwise, after eddy-current correction, the aligned multiple DWI was averaged by calling the *fslmaths* command of FSL.

*Calculating diffusion tensor (DT) metrics.* This step involves a voxel-wise calculation of the tensor matrix and the DT metrics, including FA, MD, axial diffusivity (AD), and radial diffusivity (RD) (Pierpaoli and Basser, 1996; Song et al., 2002). The *dtifit* command of FSL was applied.

#### *Producing diffusion metrics that are ready for statistical analysis*

*Normalizing.* To allow for comparison across subjects, location correspondence has to be established. To end this, registration of all the individual images to a standardized template is always applied. Here, PANDA non-linearly registered individual

FA images of native space to the FA template in the MNI space by calling the *fnirt* command of FSL. The resultant warping transformations were then used to resample the images of the diffusion metrics (i.e., FA, MD, AD, and RD) into the MNI space with a customized spatial resolution (e.g., 1 × 1 × 1 mm or 2 × 2 × 2 mm). This resampling step was implemented by the *applywarp* command of FSL.

*Output for voxel-based analysis.* The resultant images of the diffusion metrics in the standard space are ready for voxel-based statistical analysis. However, in the framework of voxel-based analysis, these images are typically smoothed to some degree, which can reduce the effect of image noise and misalignment between subjects. Accordingly, PANDA smoothed the images with a given Gaussian kernel, which was realized by calling the *fslmaths* command of FSL. The smoothed diffusion metric images can then be directly used for voxel-based statistical analysis with any preferred tools, e.g., FSL (http://www*.*fmrib*.*ox*.*ac*.*uk/fsl/), SPM (http://www*.*fil*.*ion*.*ucl*.*ac*.*uk/spm/), or AFNI (http://afni*.*nimh*.* nih*.*gov/afni/).

*Output for atlas-based analysis.* In addition to the popular voxel-based method of analysis, diffusion metrics can be analyzed at the level of region of interests (ROI), which may provide better statistical sensitivity in some cases (Faria et al., 2010). Recently, a few WM atlases (e.g., the ICBM-DTI-81 WM labels atlas and the JHU WM tractography atlas) have been proposed (Mori et al., 2008). These WM atlases in the standard space allow for parcellation of the entire WM into multiple ROIs, each representing a labeled region in the atlas. To support ROIbased analysis, PANDA calculates the regional diffusion metrics (i.e., FA, MD, AD, and RD) by averaging the values within each region of the WM atlases. These resultant ROI-based data (saved as text files) can be statistically analyzed with SPSS (http:// www-01*.*ibm*.*com/software/analytics/spss/) and other statistical packages.

*Output for TBSS-based analysis.* The TBSS framework avoids the necessity of choosing a spatial smoothing procedure during voxel-based analysis and may provide better sensitivity and interpretability when it is applied to multi-subjects dMRI datasets (Smith et al., 2006). To support this type of analysis, PANDA follows the standard TBSS framework. Firstly, the mean of all the aligned FA images was created and skeletonized, resulting in a mean FA skeleton. Secondly, the diffusion metric data from individual subjects were projected onto the skeleton. Finally, individual images with data on the skeleton were created. The resultant images can be directly used for voxel-wise statistical analysis on the skeleton. Here, the *fslmaths* and *tbss\_skeleton* commands of FSL were employed.

## *Constructing networks*

Two basic elements are required for a network: a node and a connection. Thus, the central tasks for constructing brain networks are: (1) defining network nodes and (2) defining connections between nodes. The schematic flowchart of network construction is demonstrated in **Figure 2**.

*Defining network nodes.* Typically, the entire brain is divided into multiple regions using a prior gray matter (GM) atlas, where each region represents a network node (Bullmore and Sporns, 2009). However, the prior atlases are generally defined in the standard space and need to be transformed to the native dMRI space of each individual. To address this, PANDA uses the framework

reconstructed using deterministic tractography. **(B)** Parcellation of gray matter in diffusion space. Each color represents a node in a brain

number, averaged length, and averaged FA. **(E)** The network matrix

weighted by connectivity probability.

proposed by Gong et al. (2009a). Specifically, the individual FA image in native space was co-registered to its corresponding structural image (i.e., T1-weighted) using an affine transformation. The individual structural image was then non-linearly registered to the ICBM152 template. Based on the resultant transformations in these two steps, an inverse warping transformation from the standard space to the native dMRI space can be obtained. Prior atlases in the standard space were then inversely warped back to individual native space by applying this inverse transformation. Currently, PANDA provides two well-defined atlases: the Automated Anatomical Labeling (AAL) (Tzourio-Mazoyer et al., 2002) atlas and the Harvard-Oxford atlas (HOA) (http://www*.* cma*.*mgh*.*harvard*.*edu/fslatlas*.*html). Notably, users can import customized atlases into PANDA to define the network nodes. During this step, the *flirt*, *fnirt*, *inwarp,* and *applywarp* commands of FSL were used.

*Constructing networks using deterministic tractography.* In general, deterministic tractography assumes a deterministic fiber orientation at every location during tracking, typically ending up with 3D trajectories for reconstructed WM tracts. Here, the *dti\_recon* and *dti\_tracker* commands of the Diffusion Toolkit (http://trackvis*.*org/dtk/) were called to reconstruct all possible fibers within the brain by seeding from all the WM voxels. For every pair of brain nodes/regions defined above, fibers with two end-points located in their respective masks were considered to link the two nodes. Based on the linking fibers, PANDA calculated three basic weighted matrices: *number-weighted matrix* (M*N*), *FA-weighted matrix* (MFA), and *length-weighted matrix* (M*L*). In the matrices, each row or column represents a brain region/node. The values of the elements M(*i, j*)*N*, M(*i, j*)FA, and M(*i, j*)*<sup>L</sup>* represent the number, averaged FA and averaged length of linking fibers between node *i* and node *j*, respectively. The resultant matrices were saved as a MATLAB data file and can be directly used for topological analysis with graph theoretic approaches (Bullmore and Sporns, 2009; Bullmore and Bassett, 2011).

*Constructing networks using probabilistic tractography.* In contrast, probabilistic tractography typically runs the tracking procedure many times, and fiber orientation is determined probabilistically. This type of tractography may improve tracking sensitivity, particularly for non-dominant fibers. The probabilistic tractography proposed by Behrens et al. (2003, 2007) has been implemented in FSL and is called by PANDA for network construction. This process involves two steps as follows:

*BedpostX*. Using the Markov Chain Monte Carlo sampling technique, this module estimated the local probability distribution of fiber direction at each voxel, a prerequisite for running subsequent probabilistic tractography (Behrens et al., 2003). In PANDA, bedpostX was realized by calling the *xfibres* command of FSL.

*Probabilistic Tractography and Network Construction*. Network construction using FSL-based probabilistic tractography has been previously described (Gong et al., 2009b). Briefly, for each defined brain region/node, probabilistic tractography was performed by seeding from all voxels of this region. For each voxel, 5000 fibers were sampled. To achieve this, the *probtrackx* command of FSL was called. The connectivity probability from the seed region *i* to another region *j* was defined by the number of fibers passing through region *j* divided by the total number of fibers sampled from region *i*. The connectivity probability of each node to the other nodes within the brain network can be calculated by repeating the tractography procedure for all nodes. This leads to an individual-specific weighted matrix, whose rows and columns represent the brain nodes and whose elements represent the connectivity probability between nodes. This matrix can also be directly used for various network analyses.

## **REALIZATION OF PIPELINES**

PSOM is a flexible framework for the implementation of pipelines in the form of Octave or Matlab scripts (Bellec et al., 2012), and was employed to build up the processing pipeline in our study. Here, a pipeline refers to a collection of jobs with a well identified set of options that use files for inputs and outputs. The entire processing flow of PANDA includes 41 steps, each of which is a job within the PANDA pipeline. Notably, more steps can be added if new functions or processing steps are included. The workflow of the current PANDA pipeline showing all the jobs and their associated dependencies is illustrated in Appendix A.

In particular, PANDA was designed to allow for jobs running in parallel either on a single computer with multiple cores or on a computing cluster. Notably, the PANDA processing steps are parallelizable at multiple levels. For example, the same processing steps (i.e., preprocessing) for a group of subjects can be parallelized, since the steps are independent across subjects. In addition, for the same subject, different processing steps without between-dependency such as *producing diffusion metrics* and *brain parcellation* can be parallelized as well. Finally, a few very time-consuming steps (i.e., *BedpostX* and *Probabilistic Tractography and Network Construction*) have been internally parallelized. The parallelizing strategies in PANDA are demonstrated in **Figure 3**.

## **TESTING THE AGE EFFECT ON WM CONNECTIVITY BY USING PANDA** *Subjects*

The test included data from 23 young adults (males, 11; females, 12; age, 17–24 years) and 17 elderly individuals (males, 8; females, 9; age, 54–77 years). All subjects were recruited from the campus and the local community. Subjects with a history of neurological or psychiatric disorders were excluded from this study. Written informed consent was obtained from each subject, and the protocol was approved by the Ethics Committee of the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University.

## *MRI acquisition*

All scans were performed using the 3-T Siemens Tim Trio MRI scanner in the Imaging Center for Brain Research, Beijing Normal University. Diffusion MRI was acquired using a single-shot echoplanar imaging-based sequence with following parameters: coverage of the whole brain; slice thickness, 2 mm; no gap; 68 axial slices; repetition time (TR), 9000 ms; echo time (TE), 92 ms; flip angle, 90◦; 66 non-linear diffusion weighting directions with

*b* = 1000 s/mm2 and one image without diffusion weighting (i.e., *b* = 0 s/mm2); 4 repetitive acquisitions; acquisition matrix, 128 × 124; field of view (FOV), 256 × 248 mm2; resolution, 2 × 2 × 2 mm. Three-dimensional T1-weighted images with high resolution were obtained using a three-dimensional magnetization prepared rapid gradient echo (MP-RAGE) sequence with the following parameters: 1 mm slice thickness without gap; 176 sagittal slices; TR, 1900 ms; TE, 3.44 ms; flip angle, 9◦; acquisition matrix, 256 × 256; FOV, 256 × 256 mm2; resolution, 1 × 1 × 1 mm.

Independent processing steps from the same subject or across subjects in

#### *Image processing*

The whole pipeline procedure of PANDA was run on all dMRI datasets with an in-house computing cluster of 6 nodes, each with 30GB of memory and 12 Intel Xeon E5649 2.53 GHz cores. For each pipeline step, default parameters were chosen.

#### *Network topology*

Graph theoretical approaches have been applied to characterize the topology of brain networks that are derived from neuroimaging data (Bullmore and Sporns, 2009). Here, we focus on two indicated by orange boxes.

topological network parameters: global efficiency and local efficiency. Global efficiency was defined as the average of the inverse of the "harmonic mean" of the characteristic path length, which represents global information transferring ability within the network (Latora and Marchiori, 2001). Local efficiency quantifies the ability of the network fault tolerant, corresponding to the efficiency of the information flow between nodal neighbors. Specifically, local efficiency was defined as the average of nodal local efficiency that is computed as the global efficiency of the subgraph composed by its nearest neighbors (Latora and Marchiori, 2001).

#### *Statistical analysis*

For diffusion metric, we tested the group difference on FA across the entire WM. Specifically, normalized and smoothed (6 mm Gaussian kernel) FA images produced by PANDA were employed for this voxel-based analysis. A general linear model (GLM) with gender being taken as a covariate was applied to each WM voxel. For multiple comparison correction, false discovery ratio (FDR) was applied, and *p <* 0*.*01 was considered as significant.

For each subject, the FA-weighted matrix generated from PANDA was selected for topological analysis. Each matrix is 78 × 78 and represents the WM network of cerebral cortex. Each row or column of the matrix represents a cortical region of the AAL template (Gong et al., 2009a,b). The global efficiency and local efficiency were then calculated. To test the group effect on the global and local efficiency, a GLM with gender and brain size as covariates was applied, and *p <* 0*.*05 was chosen as the significant level.

#### **RESULTS**

#### **AN INTEGRATED MATLAB TOOLBOX: PANDA**

An integrated MATLAB toolbox named PANDA has been developed for fully automated processing of dMRI datasets, which is an open-source package and is freely available at http://www*.* nitrc*.*org/projects/panda. An online discussion forum (http:// www*.*nitrc*.*org/forum/forum*.*php?forumid=2731) and a mailing list (http://www*.*nitrc*.*org/mailman/listinfo/panda-commits) have been registered for PANDA, and technical supports and updates will be constantly provided by the developers. Notably, PANDA has been packaged with PSOM, MRIcron, and Diffusion Toolkit. Only FSL is required to be installed separately.

Specifically, PANDA includes a main function and a set of separate modules/utilities. Using the main function, PANDA can run pipeline processing for any number of subjects, after raw dMRI datasets are loaded into the program. This running mode will finish all processing steps and end up with all outputs as described in "Materials and Methods." In contrast, the utilities can be used separately for specific processing steps (e.g., DICOM conversion, TBSS, and brain parcellation). Particularly, PANDA has a very friendly GUI (**Figure 4**), with which users can perform various interactions with the embedded functions, e.g., setting inputs or outputs and configuring the processing parameters. In addition, PANDA can provide the status of the ongoing pipeline processing in real-time, allowing users to monitor progress through the GUI. The detailed descriptions for GUIs of PANDA are included in Appendix B.

As provided by PSOM (Bellec et al., 2012), PANDA has a number of advantages as follows: (1) it can run jobs in parallel either in a single computer with multiple cores or in a computing cluster; (2) it can generate log files and keep track of the pipeline execution; (3) if the program terminates before finishing, users can load a configuration file, click "RUN," and PANDA will restart from the termination point; (4) if users re-run the pipelines after changing some options, PANDA will only restart the procedures related to these options; and (5) the jobs will run in the background and PANDA & MATLAB can be closed after clicking the "RUN" button.

#### **RESULTANT FILES OF PANDA**

For each subject, PANDA generates six folders containing resultant files, as listed in **Table 1**. Specifically, the *native\_space* folder consists of all images and files in the native space. The files in the *quality\_control* folder include 2D snapshot pictures of FA, T1, normalized FA, and normalized T1, which can be quickly viewed to check the quality of the data and related registrations (**Figure 5**). All files of the diffusion metrics that are ready for statistical analysis are stored in the folder named *standard\_space*. The *trackvis* folder consists of resultant files generated by the "Diffusion Toolkit" for deterministic tractography, which can be opened with Trackvis. The *native\_space.bedpostx* folder contains the resultant files of bedpostX that are required for FSL probabilistic tractography. Finally, the MATLAB files containing the network matrices with different weighting (i.e., fiber number, averaged FA, averaged length, and connectivity probability) are stored in the folder named *network*.

#### **TIME COST**

To provide information about the time cost of PANDA procedures, a few baseline running-time tests were conducted. Specifically, two dMRI datasets with different acquisition schemes (dataset I: 64 directions, 4 repetitive acquisitions, resolution: 2 × 2 × 2 mm; dataset II: 30 directions, 2 repetitive acquisitions, resolution: 2.2 × 2.2 × 2.2 mm) were tested under four conditions (one subject with four cores; one subject with eight cores; two subjects with four cores; two subjects with eight cores). The results are listed in **Table 2**.

Obviously, the running time depends on dMRI scanning schemes. More DWI directions and more repetitive acquisitions will result in longer running time of *preprocessing* and *bedpostX*. Our results further demonstrated that the running-time

### **Table 1 | Folders produced by PANDA.**


for multiple subjects with multiple cores in PANDA can be effectively saved, due to the parallelized processing. For example, finishing the pre-processing steps for two subjects costs almost the same time as for one subject (**Table 2**). In addition, since the *bedpostX* has been parallelized internally, finishing *bedpostX* with eight cores cost only half of time as cost with four cores (**Table 2**).

#### **THE AGE EFFECT ON WM CONNECTIVITY USING PANDA**

As expected, voxel-based comparison revealed a distributed FA decreases (*p <* 0*.*01, FDR corrected) throughout the brain in the old group. Specifically, FA was mainly affected in the bilateral superior longitudinal fasciculus, uncinate fasciculus, internal capsules, external capsules, fornices, and corpus callosum (**Figure 6**).

Moreover, we observed group differences in topological efficiencies of WM network of cerebral cortex. As demonstrated in **Figure 7**, the global efficiency of the WM network showed a significant reduction in the old group (*p* = 0*.*03) after controlling for gender and brain size, and the local efficiency exhibited only a trend of reduction (*p* = 0*.*16).

## **DISCUSSION**

In this study, we have developed a MATLAB toolbox named PANDA for comprehensively processing dMRI datasets. The key advantage of PANDA is that it fully automates all the processing steps of dMRI datasets for any number of subjects. PANDA can yield diffusion metric data that is ready for statistical analysis at three levels (voxel-level, atlas-level, and TBSSlevel), and can generate anatomical networks/matrices of the entire brain using either deterministic or probabilistic diffusion tractography.

**FIGURE 5 | Snapshot pictures for quality control of FA normalization.** The normalized FA is overlaid with image edges that were derived from the FA template. These pictures can be quickly viewed to check the quality of normalization.



*The processing was performed using a local workstation with 30 GB of memory and Intel Xeon E5649 2.53 GHz cores. Four conditions were tested: one subject with four cores; two subjects with four cores; one subject with eight cores; two subjects with eight cores.*

**FIGURE 6 | The statistical map showing significant FA decreases in old group (***p <* **0***.***01, FDR corrected).** The hot color represents *t* values for the age effect.

A fully automated pipeline naturally makes the data processing efficient, at the same time reducing potential mistakes by avoiding manual processing of individual steps. While constructing a dMRI processing pipeline with MIPAV (McAuliffe et al., 2001), JIST (Lucas et al., 2010), Nipype (Gorgolewski et al., 2011), or LONI (Dinov et al., 2009) is possible, it requires prior knowledge on pipeline design and programming skills related to these packages. In addition, knowledge on the details of all steps for processing dMRI dataset is required, which might be another challenge for end users. To provide a ready-for-use pipeline tool for end users, PANDA was developed, making it possible to process dMRI datasets immediately with established pipelines.

Notably, there exist differences in the processing procedures across existing dMRI packages, and some important processing steps might be overlooked (Jones et al., 2012). These issues have been well discussed by a few recent articles (Jones and Cercignani, 2010; Jones et al., 2012). The processing pipelines of PANDA have tried to follow the best practice as possible. For example, the adjustment of diffusion gradient directions after eddy-current correction, which has been frequently missed (Leemans and Jones, 2009; Jones et al., 2012), has been included in the PANDA pipeline. In future versions, PANDA will keep being updated to include processing steps of the best practice at the moment.

Another advantage of PANDA is that both sequential and parallel processing modes are supported, which makes it possible to take full advantage of available computing resources. The parallel environment can be either a single computer with multiple cores or a computing cluster, which increasingly enters into labs around the world. As shown in **Figure 3**, the PANDA processing have been parallelized as much as possible, and can thus reduce the time cost substantially under a parallel processing mode. For instance, the running time for pre-processing two subjects is almost the same as for one subject by using a workstation with four cores.

Finally, PANDA has a very friendly GUI (**Figure 4**), allowing the advanced users to select the desired options for each processing step. Depending on the datasets, users may change the options of some processing steps to optimize the processing quality. The reference data, e.g., image templates for normalization or prior atlases for node definition, can also be replaced by customized data, making it possible for processing dMRI data of non-human (e.g., primate) brains.

In the present study, we applied PANDA to produce results for testing the age effect on WM diffusion metrics as well as topological properties of the WM network. Significant FA reductions during aging were found in the bilateral uncinate fasciculus,

#### **REFERENCES**


Behrens, T. E. J., and Sporns, O. (2012). Human connectomics. *Curr. Opin. Neurobiol.* 22, 144–153.

Behrens, T., Berg, H. J., Jbabdi, S., Rushworth, M., and Woolrich, M. (2007). Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?

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superior longitudinal fasciculus, external capsules, fornices and corpus callosum, which are highly consistent with previous findings (Bennett et al., 2010; Michielse et al., 2010). In addition, significant reduction of global efficiency and a trend of reduction of local efficiency were observed in the old group. These topological changes are largely compatible with our previous results that are based on a larger dataset (Gong et al., 2009b). The declined WM connectivity and topology may underlie various patterns of cognitive decline during normal aging. The results for this specific study prove the usability and validity of the PANDA processing.

PANDA is of great applicability in the area of connectivity neuroscience. For example, this tool can be applied to dMRI datasets that are collected to study various connectivity hypotheses. Also, the effects of dMRI processing parameters or steps on the final connectivity results can be easily tested by using PANDA. Recently, the term "connectome" has been proposed to advocate efforts for comprehensively mapping and analyzing brain connectivity and networks (Sporns et al., 2005), and dMRI has been taken as a primary technique for structural macroconnectome (Behrens and Sporns, 2012). This will lead to a large number of dMRI datasets in the foreseeable future (http:// humanconnectome*.*org/). To process these connectome dataset, PANDA has unique advantages, as it can handle the large number of datasets very efficiently because of its parallelizing strategies. Meanwhile, it can automatically provide important metrics of interest (e.g., diffusion metrics of brain connectivity and brain network matrices) for connectome studies. Therefore, PANDA can potentially make contributions to the study of the human connectome in the near future.

In summary, PANDA can substantially facilitate/simplify image processing in a dMRI-related study, and can provide measures for WM connectivity and network analysis. It has an extendable design framework, and new functions or utilities can and will be added in the future.

## **ACKNOWLEDGMENTS**

The authors sincerely thank all the developers of FSL, PSOM, Diffusion Toolkit, and MRIcron, whose functions are called by PANDA. In addition, the authors thank Dr. Pierre Bellec for his support when implementing PSOM to PANDA, and thank Dr. Yanchao Bi for English editing. This work was supported by the National Science Foundation of China (No. 31000499, 81271649, 81030028), the Beijing Nova Program (No. Z121110002512032), the 973 program (No. 2013CB837300), and Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning.

R., Clare, S., et al. (2003). Characterization and propagation of uncertainty in diffusion-weighted MR imaging. *Magn. Reson. Med.* 50, 1077–1088.

Bellec, P., Lavoie-Courchesne, S., Dickinson, P., Lerch, J. P., Zijdenbos, A. P., and Evans, A. C. (2012). The pipeline system for Octave and Matlab (PSOM): a lightweight scripting framework and execution engine for scientific workflows. *Front. Neuroinform.* 6:7. doi: 10.3389/fninf.2012.00007

Bennett, I. J., Madden, D. J., Vaidya, C. J., Howard, D. V., and Howard, J. H. Jr. (2010). Age-related differences in multiple measures of white matter integrity: a diffusion tensor imaging study of healthy aging. *Hum. Brain Mapp.* 31, 378–390.


structural core of human cerebral cortex. *PLoS Biol.* 6:e159. doi: 10.1371/journal.pbio.0060159


image processing, analysis and visualization in clinical research," in *Proceedings of the 14th IEEE Symposium on Computer-Based Medical Systems*, (Bethesda, USA), 381–386.


radial (but unchanged axial) diffusion of water. *Neuroimage* 17, 1429–1436.


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 11 September 2012; accepted: 04 February 2013; published online: 21 February 2013.*

*Citation: Cui Z, Zhong S, Xu P, He Y and Gong G (2013) PANDA: a pipeline toolbox for analyzing brain diffusion images. Front. Hum. Neurosci. 7:42. doi: 10.3389/fnhum.2013.00042*

*Copyright © 2013 Cui, Zhong, Xu, He and Gong. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## **APPENDICES APPENDIX A**

## **APPENDIX B: GUIs OF PANDA**

## *Main function*

The main GUI of PANDA is shown in **Figure B1**. Users are required to set up inputs and configure outputs through this GUI. Specifically, the data inputs are folders, each containing files in either DICOM or NIfTI format, for each subject. The output configuration includes: (1) a main output folder that contains subject-specific subfolders of results; (2) digital subject IDs; and (3) a prefix. The IDs and prefixes are used to name the resultant subfolder or files for each subject. In addition, users may change the pipeline options (**Figure B2A**), diffusion options (**Figure B2B**), and tracking options (**Figure B2C**). The default setting for these options will be used if no changes are made.

Once all required settings are established, users simply click the "RUN" button to start the processing. PANDA will automatically finish all the sequential jobs and yield files containing


diffusion metrics and anatomical brain networks, as described in the "Materials and Methods." During processing, the status of jobs can be checked in the monitor table of the GUI (**Figure B1**).

#### *Separate utilities*

*TBSS.* As shown in **Figure B3A**, this utility is for separate TBSS procedures, which require all images of FA and other diffusion metrics to be aligned in the MNI space. With correct input settings, this module will automatically generate individual images with data on the skeleton for all subjects. Statistical analyses can be directly applied to the resultant images.

*Brain parcellation (node definition).* This utility is used to separately define the brain network nodes. The sub-GUI is shown in **Figure B3B**. This module requires FA images of native space and skull-stripped T1 images as inputs. A prior atlas in the MNI space should also be specified. The results of this utility are individual atlas images in the dMRI native space for all subjects. These images can be directly loaded by the utility "Tracking & Network." *Bedpostx.* As shown in **Figure B4A**, this utility allows for the estimation of voxel-wise local probability distributions of fiber orientation for a set of subjects, which is typically very timeconsuming. The input for each subject should be a folder containing four files as listed: (1) a 4D image named *data.nii.gz* containing diffusion-weighted volumes and volumes without diffusion weighting; (2) a 3D binary brain mask volume named *nodif\_brain\_mask.nii.gz*; (3) a text file named *bvecs* containing gradient directions for diffusion weighted volumes; and (4) a text file named *bvals* containing the b-values that were applied to each volume acquisition. This module will generate a separate folder containing all the files that are required for subsequent probabilistic tractography.

*Tracking* **&** *Network.* This utility can separately construct anatomical brain networks based on tractography. The sub-GUI is shown in **Figure B4B**. For a deterministic tractographybased network, a folder with four files described in the section "Bedpostx" together with an individual-specific atlas image generated by the utility "Brain Parcellation" are required. For a probabilistic tractography-based network, the resultant folder of the utility "Bedpostx" and the individual-specific atlas image should be the inputs. As described in the "Materials and Methods," this module will generate network matrices that are saved in a MATLAB data file.

*DICOM sorter.* This handy utility, as shown in **Figure B5A**, can automatically sort multiple DICOM files in the same folder into sequence-specific or subject-specific sub-folders, based on the header information of the DICOM files. This is particularly useful when the DICOM files from different sequences or subjects are saved in the same folder, which happens very often.

*Image converter.* The NIfTI format can be a pair of files (hdr/img), a single file (nii), or a compressed file (nii.gz). A NIfTI file may be required in a certain file type, e.g., \*.nii or \*.hdr/img. As shown in **Figure B5B**, this utility can convert NIfTI pair format (hdr/img), NIfTI format (nii), and NIfTI GZ format (nii.gz) file types.

*File copier.* This utility can copy a large number of files located in different source folders into the same target folder. The sub-GUI is shown in **Figure B5C**. After PANDA processing, each subject will have unique folders containing the resultant files. "File Copier" can easily copy the same types of resultant files (e.g., aligned FA images) of all the subjects to one target folder, which might be helpful for further statistical analysis or other purposes.

## Task vs. rest—different network configurations between the coactivation and the resting-state brain networks

## *Xin Di , Suril Gohel , Eun H. Kim and Bharat B. Biswal\**

*Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Christian Windischberger, Medical University of Vienna, Austria Qihong Zou, Peking University, China*

#### *\*Correspondence:*

*Bharat B. Biswal, Department of Biomedical Engineering, New Jersey Institute of Technology, 607 Fenster Hall, University Heights, Newark, NJ 07102, USA e-mail: bbiswal@yahoo.com*

There is a growing interest in studies of human brain networks using resting-state functional magnetic resonance imaging (fMRI). However, it is unclear whether and how brain networks measured during the resting-state exhibit comparable properties to brain networks during task performance. In the present study, we investigated meta-analytic coactivation patterns among brain regions based upon published neuroimaging studies, and compared the coactivation network configurations with those in the resting-state network. The strength of resting-state functional connectivity between two regions were strongly correlated with the coactivation strength. However, the coactivation network showed greater global efficiency, smaller mean clustering coefficient, and lower modularity compared with the resting-state network, which suggest a more efficient global information transmission and between system integrations during task performing. Hub shifts were also observed within the thalamus and the left inferior temporal cortex. The thalamus and the left inferior temporal cortex exhibited higher and lower degrees, respectively in the coactivation network compared with the resting-state network. These results shed light regarding the reconfiguration of the brain networks between task and resting-state conditions, and highlight the role of the thalamus in change of network configurations in task vs. rest.

**Keywords: brain network, coactivation, hub shift, meta-analysis, modularity, resting-state, small world, thalamus**

## **INTRODUCTION**

The human brain exhibits organized spontaneous fluctuations in the resting-state (Biswal et al., 1995), enabling researchers to study large-scale brain segregations and integrations (Bullmore and Sporns, 2009, 2012; Menon and Uddin, 2010). The spontaneous fluctuations reveal high synchronization between brain regions in the same brain system (Cordes et al., 2000; Greicius et al., 2003), and are relatively independent between different brain systems (Beckmann et al., 2005; Biswal et al., 2010). The whole brain segregation and integration can also be studied using graph theory based analysis (Bullmore and Sporns, 2009; Wang et al., 2010). For example, the brain network in the resting-state revealed modular structures, small world and scale free properties (Salvador et al., 2005; Achard et al., 2006; Achard and Bullmore, 2007; Nakamura et al., 2009; Yan and He, 2011).

Despite the growing popularity of resting-state fMRI to study brain functions, studies have yet to address a fundamental question regarding whether the brain at resting-state is comparable to the brain during task performing. Given that the evoked cerebral blood flow by different tasks account for less than 5% of the resting-state cerebral blood flow (Raichle, 2010), the restingstate brain already represents a large proportion of hemodynamic information which may reflect brain maintenance. Studies have also shown that task-related coactivation patterns correspond well with the brain systems that are measured during the restingstate (Toro et al., 2008; Smith et al., 2009). However, based on the economic theory of brain network organization, the brain network should be in an energy saving mode during the resting-state, while exhibiting dynamic network reconfiguration in the presence of a task demand to facilitate global and between systems information transmissions (Bullmore and Sporns, 2012). We predict that even though the connectivity in task conditions and the resting-state may be similar, substantial differences of network configurations may take place to support different task demands.

Changes in connectivity modulated by task are important to understand brain integration (Friston, 2011). Specific connections have been shown to be modulated by specific tasks (McLntosh and Gonzalez-Lima, 1994; McIntosh et al., 1994; Büchel and Friston, 1997; Rao et al., 2008). However, the modulations of connectivity are task specific, and it is difficult to modulate the whole brain network using a specific task. Thus, we adopted the same approach as Toro et al., and Smith et al. to examine task activations or group differences and their corresponding coactivation pattern across the whole brain (Toro et al., 2008; Smith et al., 2009). Specifically, we constructed brain networks comprised of 140 regions of interest (ROIs) from the whole brain based on both meta-analytic coactivation patterns (Yarkoni et al., 2011) and resting-state correlations of fMRI signals (Biswal et al., 2010). The online database Neurosynth (http:// old*.*neurosynth*.*org/) was used to extract coactivation information, which contained 47,493 activations from 4393 studies (Yarkoni et al., 2011). We first asked whether the strength of coactivation between a pair of ROIs was correlated with their restingstate correlations. We then compared different network properties based on graph theory between the two brain networks (Bullmore and Sporns, 2009), including the small-worldness (Watts and Strogatz, 1998), modularity (Newman, 2006), and hub distributions. We hypothesized that the brain when performing tasks will be more integrated and thus exhibit higher global efficiency and reduced modularity compared with the resting-state brain. In addition, we hypothesized that the brain hubs may shift from the default mode network (DMN) (Raichle et al., 2001; Greicius et al., 2003) regions to other brain regions that are critical during task executions.

## **METHODS**

#### **REGIONS OF INTEREST**

One hundred and sixty functionally defined ROIs from Dosenbach et al. were adopted in the present analysis (Dosenbach et al., 2010). Twenty-four ROIs were removed because they were outside the Neurosynth mask. We included four more ROIs that were not represented within the 136 ROIs (Sabatinelli et al., 2011): the right amygdala (Montreal Neurological Institute, MNI, coordinates: 20, −4, −15), the left amygdala (−20, −6, −15), the right parahippocampus (14, −33, −7), and the left parahippocampus (−20, −33, −4). A total of 140 ROIs were used in the present study to construct brain networks (supplementary Table S1).

## **COACTIVATION NETWORK**

The online database, Neurosynth, was used to construct the coactivation network (Yarkoni et al., 2011). The database search was conducted in November, 2012 when the database had 4393 studies and 47,493 activations. For each of the 140 ROIs, Neurosynth identified all the papers in the database that reported coordinates within 10 mm from the ROI center, and exported a whole brain z-score map representing the likelihood that a voxel coactivated with the given ROI (Yarkoni et al., 2011). The images were thresholded using a false discovery rate (FDR) criterion of *p <* 0*.*05. Thus, the Neurosynth search of all the ROIs resulted in 140 coactivation maps.

One hundred and forty spherical ROIs were defined using radii of 10 mm. The coactivation values of 140 ROIs were extracted from 140 coactivation maps, which resulted in a 140 × 140 matrix. Each row of the matrix represented the coactivation values of a given ROI with the other ROIs. Because the number of papers that was returned by each ROI inquiry was different, the coactivation values from different ROI inquiry may be biased. Therefore, we normalized each row by dividing the value from the ROI corresponding to that row, so that the diagonal values of the matrix were equaled to one. In addition, since the distribution of the coactivation values are skewed, all the values of the matrix were added by one, and were logarithmically transformed to facilitate a normal distribution. Finally, because the coactivation likelihood of region A with region B and the coactivation likelihood of region B with region A are generally similar but have slightly different values, the matrix was transposed, and averaged with the original matrix to create a symmetrical coactivation matrix.

#### **RESTING-STATE NETWORK**

We analyzed a resting-state fMRI data set to construct a restingstate network to compare with the coactivation network. The Oulu dataset from the 1000 Functional Connectomes Project was used (Biswal et al., 2010). This dataset originally contains 103 subjects. One subject's data was discarded because of large head motion (greater than 3 mm). Thus 102 subjects' data were included in the current analysis (36M/66F). The mean age was 21.5 years (range from 20 to 23 years). Two hundred and forty-five resting-state functional images were acquired for each subject (*TR* = 1*.*8 s, 28 slices). High resolution anatomical image was also acquired for each subject using MPRAGE sequence (Magnetization Prepared Rapid Acquisition Gradient Echo). More information for the data can be found at http:// fcon\_1000.projects.nitrc.org/fcpClassic/FcpTable.html. To rule out the possibility that the current results are due to sample bias of the resting-state dataset, we have conducted a separate analysis using another resting-state dataset, i.e., the Nathan Kline Institute (NKI) / Rockland Sample (http://fcon\_1000.projects. nitrc.org/indi/pro/nki.html). Data analyses were identical to the Oulu dataset. Detailed methods and results of NKI dataset are reported in the supplementary material section.

Functional MRI images were processed using the SPM8 toolbox (http://www*.*fil*.*ion*.*ucl*.*ac*.*uk/spm/) under the MATLAB7.7 environment (www*.*mathworks*.*com). First, the MPRAGE anatomical image for each subject was segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using the new segment routine in SPM8. The deformation field maps were also obtained in this step to later normalize the functional images. For each subject, the first five images of the fMRI images were discarded, resulting in 240 images per subject. The functional images were then motion corrected using the realign function. One subject's data were discarded after this step because the head motion was greater than 3 mm, resulting in 102 subjects in total. Next, the functional images were coregistered to the subjects' own anatomical images. Then, the deformation field map obtained from new segmentation step was applied to the functional images to normalize them into the standard MNI space.

One hundred and forty times series from the corresponding ROIs were extracted for each of the subjects. Six head motion parameters and their first order derivatives, first five eigenvectors from signals within WM masks, and first five eigenvectors from signals within CSF masks were regressed out using linear regression (Chai et al., 2012). No global signal regression was applied. Next, the time series were temporally filtered using a band-pass filter of 0.01–0.1 Hz. For each subject, a 140 × 140 correlation matrix was calculated using Kendall's rank correlation to minimize spurs correlations due to noises. The correlation matrices were transformed into Fisher's z, and averaged across subjects. Finally, the mean Fishers' z matrix was transformed back to correlation matrix using Fisher's inverse transform.

## **NETWORK ANALYSIS**

Because the values in the coactivation matrix and the restingstate correlation matrix are essentially different, network sparsity thresholds were used to keep the number of edges of the two networks the same when comparing the two networks. The sparsity range was set between 6 and 40% with an increment of 1%. This range was used because typical sparsity of human neuron network is between this range, and the large scale brain networks revealed small world properties within this range (Achard and Bullmore, 2007; He et al., 2008). After thresholding, all the networks were binary (unweighted) undirected networks.

We first compared the two networks in terms of small world properties (global efficiency and mean clustering coefficient) and modularity. The global efficiency characterizes how efficient the whole brain network integrates information, and the mean clustering coefficient characterizes how efficient the information flows around local nodes (Watts and Strogatz, 1998). Modularity, also known as Newman's Q, characterizes the extent the whole brain network can be divided into sub-communities (Newman, 2006). The global efficiency, mean clustering coefficient, and modularity were calculated for the two networks at each sparsity level using the brain connectivity toolbox (Rubinov and Sporns, 2010). As a reference, random networks were generated 1000 times at each sparsity level. The three parameters were also calculated for the random networks, and were averaged across the 1000 random networks.

To determine the statistical significance, we created a null distribution of network differences by randomly shuffling the two networks 1000 times and calculating their differences of network properties for 1000 random networks. Specifically, at each sparsity level, we first identified edges that were different between the two networks. Next, we randomly assigned 50% of these different edges from the coactivation network to the resting-state network, and vice versa, resulting in two new mixed networks. We then calculated the three network parameters, i.e., the global efficiency, mean clustering coefficient and modularity, for the two mixed networks and obtained their differences between the two networks. The randomizations were performed 1000 times for each sparsity level to obtain a difference distribution. The difference of the three parameters between the coactivation network and the resting-state network were then compared with the randomized distribution to determine statistical significances. A critical threshold of *p <* 0*.*001 was used.

To demonstrate modular structures of the coactivation and the resting-state network, we thresholded the two networks at a sparsity level of 20%, and entered the two unweighted undirected networks into Gephi (https://gephi*.*org/) to determine their modular structures using the algorithm by Blondel et al. (2008). The two networks and their modular structures were rendered into a 2D surface using the Fruchterman–Reingold Algorithm (Fruchterman and Reingold, 1991).

We then examined whether the two networks displayed similar hub distribution (Achard et al., 2006). In the present study, we simply defined the importance of each node by calculating the number of edges connected to this node (also known as degree). We calculated the degrees for each node for the two networks at each sparsity level. Next, at each sparsity level, correlations of node degrees between the coactivation network and the restingstate network were calculated at the sparsity range of 6–40%. The correlations reflected the similarity of hub distributions of the two networks. There were only small correlations of degrees between the two networks (see Results below), i.e., a high degree node in the resting-state network was not necessarily a high degree node in the coactivation network. Hence, we subtracted the degrees between the two networks for each node at the sparsity level of 10, 20, and 30%. At each of the three sparsity levels, we sorted the degree differences. The two networks were also randomized using the method mentioned above, and the sorted degree differences were calculated. The randomization was conducted 1000 times, and the distribution of the sorted degree differences was obtained. Then, the original sorted degree differences between the coactivation network and resting-state network was compared with the distribution. Next, at each sparsity level, five nodes that had the largest degree differences and five nodes that had the least degree differences between the coactivation network and the resting-state network were identified. These nodes were rendered on a brain surface model using the BrainNet Viewer (http://www*.* nitrc*.*org/projects/bnv/).

## **RESULTS**

### **COACTIVATION AND RESTING-STATE NETWORKS**

The pattern of the coactivation and the resting-state correlation matrix were comparable (**Figures 1A,B**). Because the ROIs were arranged according to their network affiliations as reported by Dosenbach et al. (2010), square like structures along the diagonal were observed in both networks (see supplementary Table S1 for the network affiliations of the ROIs). In addition, the coactivation strengths and the resting-state correlation strengths among the 9730 (140 × 139*/*2) pairs of ROIs showed a strong linear relationship (**Figure 1C**), i.e., if two regions had higher correlation in the resting-state, they also had higher coactivation strength, and vice versa. The Pearson correlation between the coactivation strengths and connectivity strengths was 0.72 (*r*<sup>2</sup> <sup>=</sup> <sup>0</sup>*.*51).

#### **SMALL WORLD AND MODULARITY**

Both the coactivation network and the resting-state network revealed smaller global efficiency and larger clustering coefficient compared with the reference random networks, which characterizes the small world network properties (**Figure 2**). Direct comparison between the coactivation network and the restingstate network revealed greater global efficiency and smaller mean clustering coefficient for the coactivation network compared with the resting-state network at selected sparsity levels (highlighted by shading in **Figure 2**). Thresholding at a significance level of *p <* 0*.*001, greater global efficiency for the coactivation network were present at almost all the sparsity levels that were tested between 6 and 40% (except for 23%), while smaller mean clustering coefficient for the coactivation network were only present at the sparsity level between 28 and 40%.

Both the coactivation network and the resting-state network revealed higher modularity compared with the random networks (**Figure 3A**). The coactivation network generally revealed lower modularity than the resting-state network at sparsity level between 17 and 40% at the significance level of *p <* 0*.*001. **Figures 3B,C** demonstrated the modular structures of the coactivation network and the resting-state network at sparsity level of 20%. For the resting-state network, four modules were clearly

#### **HUB SHIFTS**

At all sparsity levels between 6 and 40%, the correlations between node degrees of the coactivation network and the resting-state network were small (range from 0.17 to 0.38) (**Figure 4A**). We then plotted the node degrees of the coactivation network against the node degrees of the resting-state network at 10, 20, and 30% sparsity levels (**Figures 4B–D**). We observed that there were several nodes in the upper right corner or lower right corner of the scatter plots, which indicates that these nodes had higher degrees in one network but not in the other network.

Additional analysis showed that the distribution of degree differences between the coactivation network and the resting-state network were outside the distribution of sorted degree differences

between the coactivation and resting-state networks at *p <* 0*.*001 based on 1000 permutations.

coactivation and resting-state networks at *p <* 0*.*001 based on 1000

**FIGURE 4 | (A)** Correlations between nodes' degree of the coactivation network and the resting-state network as a function of connectivity sparsity. Panels **(B–D)** show the scatter plots of node degrees between the two networks at sparsity level of 10% **(B)**, 20% **(C)**, and 30% **(D)**, respectively. The lines in the scatter plots represent the linear fit.

modules.

of randomized 1000 permutations (**Figures 5A–C**), indicating that the degree differences between the two networks are not likely due to random noises. We then subtracted the degrees in the activation network by the degrees in the resting-state network for all 140 nodes at sparsity levels of 10, 20, and 30%, respectively. The top five nodes that had the greatest degree differences between the two networks are illustrated in **Figures 5D–F** and **Table 1**. Across the three sparsity levels, the bilateral thalamus demonstrated higher degrees in the coactivation network compared with the resting-state network. Other regions, including the basal ganglia, inferior parietal lobule (IPL), posterior parietal cortex, medial frontal cortex (mFC), and anterior insula, also showed higher degrees in the coactivation network at various sparsity levels. In contrast, a node in the inferior temporal cortex revealed consistently higher degree in the resting-state network compared with the coactivation network. Other regions, including the precuneus, angular gyrus, inferior parietal sulcus (IPS), temporoparietal junction (TPJ), superior frontal cortex, parahippocampal gyrus, and inferior cerebellum, also showed higher degrees in the resting-state network at various sparsity levels. The connectivity of the thalamus and the left inferior cortex for the two networks at sparsity level of 20% are illustrated in **Figure 6**.

## **DISCUSSION**

The current study compared the whole brain network configurations between the coactivation network and the resting-state network. We first observed a high correlation between the coactivation strength and the resting-state correlation across all pairs of ROIs. In other words, if a pair of brain regions has greater functional connectivity in the resting-state, they are more likely to have greater coactivation, and vice versa. This is in line with previous findings that the coactivation patterns correspond well with the resting-state connectivity and networks (Toro et al., 2008; Smith et al., 2009). However, further analysis revealed substantial differences in network configuration between the two networks. Specifically, the coactivation network revealed higher global efficiency, lower mean clustering coefficient, and lower modularity as compared with the resting-state network. Shifts in hub regions were also observed where the thalamus had greater degrees in the coactivation network than in the resting-state network, and a region in the left inferior temporal cortex had greater degrees in the resting-state network than in the coactivation network. These results were similar when using NKI-dataset (see supplementary materials).

The brain network exhibits a so-called "small-world" property (Watts and Strogatz, 1998) that the network has greater mean local efficiency but smaller global efficiency than random network. Small world properties have been initially shown in nonhuman primates (Sporns, 2000; Stephan et al., 2000) and later in human brain network using both the resting-state fMRI (Salvador et al., 2005; van den Heuvel et al., 2009) and diffusion weighted imaging (Hagmann et al., 2007; Gong et al., 2009). The current results revealed greater global efficiency and smaller mean local efficiency for the coactivation network as compared with the resting-state network, suggesting that the whole brain is connected more efficiently to support global information flow during task performing. These results are in line with the findings that the brain exhibits higher global efficiency as task difficulty increases (Kitzbichler et al., 2011), and in the awake state compared with the stage 1 sleep (Uehara et al., 2013).

The current study also revealed smaller modularity in the coactivation network as compared with the resting-state network. These results suggest that the whole brain is less segregated as independent modules when performing tasks as compared with the resting-state. In other words, there are more between module connections and less within module connections when performing tasks, while more within module connections and less between module connections exist in the resting-state. These results are in line with the economy theory of brain network that long range between system connections are more costly, so that dynamic connectivity between brain systems is only present upon task demands (Bullmore and Sporns, 2012). Consistent with this notion, brain network modularity reduces when the task demand

**Table 1 | Top five regions that have greater or smaller degree in the coactivation network as compared with in the resting-state network for the sparsity of 10, 20, and 30%, respectively.**


*Regions highlighted in bold represent the regions show consistent differences between the two networks across the three sparsity levels.*

increases (Kitzbichler et al., 2011), and in awake state than during non-rapid eye movement sleep (Boly et al., 2012).

In addition to the whole brain network properties, the current study also identified hub regions by calculating degrees (number of connections) for each ROI. In contrast to the high correlation of network strengths between the coactivation network and the resting-state network, the correlations of node degrees between the two networks are small (around 0.3). This suggests a hub shift between task performance and resting-state (Fransson et al., 2011; Achard et al., 2012), which may reflect the adaptive brain reorganization that support the execution of tasks. However, the low correlations of degrees are inconsistent with a previous study showing a high correlation of degrees between a passive fixation condition and a continuous semantic classification task condition (Buckner et al., 2009). The differences may be due to the methodological differences used by Buckner et al. (voxel-wise analysis); the voxel-wise degree distributions are likely to be affected by the underlying brain anatomy, and the high correlation between the two degree maps may partially reflect the anatomical information. In addition, the differences may also be explained by the task adopted by Buckner et al., which is different from the current coactivation approach. Further studies are needed to investigate shifts in hubs elicited by different tasks.

The thalamus regions showed consistently higher degrees (the number of connections with other regions) in the coactivation network as compared with the resting-state network. The thalamus relays visual and auditory information gathered from the eyes and ears to the cerebral cortex (Hotta and Kameda, 1963). Different parts of the thalamus have intensive connections to wide spread cortical regions (Zhang et al., 2008; Eckert et al., 2012). In addition, the thalamus as a relay is important for corticocortical communication, and thus is suggested to be a potential hub for the brain function (Guillery, 1995). Previous resting-state fMRI studies occasionally identified the thalamus as a hub region (van den Heuvel et al., 2008), however, most of studies did not support this view (Achard et al., 2006; Buckner et al., 2009; Yan and He, 2011; Zuo et al., 2012). This may be because the centrality (as measured by degree or eigenvector centrality) of the thalamus is context specific (Lohmann et al., 2010; Gili et al., 2013). This is in line with the present result, and suggests that the thalamus mediates corticocortical communication during task, but this mediation is weakened in the resting-state.

In contrast, the left inferior temporal cortex region revealed higher degree in the resting-state network as compared with the coactivation network. This region is part of the DMN (Raichle et al., 2001; Buckner et al., 2008), which is generally deactivated during tasks (Shulman et al., 1997). Regions that are connected with the left inferior temporal cortex mostly constitute the DMN (**Figure 6D**). Consistent with previous studies of brain centrality (Achard et al., 2006; Buckner et al., 2009), the left inferior temporal cortex showed high centrality in resting-state. But, the current analysis also revealed that the degree is significantly less in the coactivation network. This may reflect the less involvement of this region during tasks as compared with the resting-state (Shulman et al., 1997).

By comparing network configurations of the coactivation network with the resting-state network, the current analysis provides insight on the different brain modes during task and restingstate. The brain during task exhibits greater small-worldness that facilitates global information transmission, and smaller modularity that facilitate information transmission between different systems. These results motivate future studies to investigate brain network configurations in different task conditions. In addition, the current analysis identified the thalamus as a hub region only in the coactivation network but not the resting-state network, suggesting that the role of thalamus in the brain network may be overlooked when studying the resting-state brain network.

A difficulty of studying thalamus connectivity is that the thalamus is spatially heterogeneous, so that different substructures connect to different brain regions (Zhang et al., 2008; Eckert et al., 2012). Future studies may need to use fine spatial scales to investigate the thalamus and its effect on network configurations (Wang et al., 2009; Hayasaka and Laurienti, 2010).

Recently, several efforts have been made to study brain networks using inter-individual covariance from different imaging modalities, for example brain structures (Mechelli et al., 2005; Chen et al., 2008), brain metabolisms (Horwitz et al., 1984; Di et al., 2012), and resting-state brain parameters (Zhang et al., 2011; Taylor et al., 2012). Although these studies provide information on brain integration, the lack of theoretical basis causes difficulty in combining results from different imaging modalities. The current study may provide a theoretical framework

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## **ACKNOWLEDGMENTS**

This study was supported by a National Institute of Health grant 5R01AG032088.

## **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/Human\_Neuroscience/10.3389/ fnhum.2013.00493/abstract


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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 16 May 2013; accepted: 03 August 2013; published online: September 2013. 17* *Citation: Di X, Gohel S, Kim EH and Biswal BB (2013) Task vs. rest—different network configurations between the coactivation and the resting-state brain networks. Front. Hum. Neurosci. 7:493. doi: 10.3389/fnhum.2013.00493*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Di, Gohel, Kim and Biswal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## The envirome and the connectome: exploring the structural noise in the human brain associated with socioeconomic deprivation

#### *Rajeev Krishnadas <sup>1</sup> \*, Jongrae Kim2, John McLean1, G. David Batty3,4, Jennifer S. McLean5, Keith Millar 1, Chris J. Packard6 and Jonathan Cavanagh1*

*<sup>1</sup> Sackler Institute of Psychobiological Research, Institute of Health and Wellbeing, University of Glasgow, Gartnavel Royal Hospital, Glasgow, UK*

*<sup>2</sup> Department of Biomedical Engineering, School of Engineering, University of Glasgow, Glasgow, UK*

*<sup>3</sup> Medical Research Council Social and Public Health Sciences Unit, Glasgow, UK*

*<sup>4</sup> Clinical Epidemiology Group, Department of Epidemiology and Public Health, University College London, London, UK*

*<sup>5</sup> Glasgow Centre for Population Health, Glasgow, UK*

*<sup>6</sup> Glasgow Clinical Research Facility, Glasgow, UK*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Sebastian J. Lipina, Unidad de Neurobiología Aplicada (UNA, CEMIC-CONICET), Argentina Boris Bernhardt, Max Planck Institute for Human Cognitive and Brain Sciences, Germany*

#### *\*Correspondence:*

*Rajeev Krishnadas, Institute of Mental Health and Wellbeing, Sackler Institute of Psychobiological Research, University Corridor, Southern General Hospital, Room - 25, Ground-Floor, Neurology building, 1345 Govan Rd., Glasgow, Lanarkshire G51 4TF, UK e-mail: rajeev.krishnadas@ glasgow.ac.uk*

Complex cognitive functions are widely recognized to be the result of a number of brain regions working together as large-scale networks. Recently, complex network analysis has been used to characterize various structural properties of the large-scale network organization of the brain. For example, the human brain has been found to have a modular architecture i.e., regions within the network form communities (modules) with more connections between regions within the community compared to regions outside it. The aim of this study was to examine the modular and overlapping modular architecture of the brain networks using complex network analysis. We also examined the association between neighborhood level deprivation and brain network structure—modularity and gray nodes. We compared network structure derived from anatomical MRI scans of 42 middle-aged neurologically healthy men from the least (LD) and the most deprived (MD) neighborhoods of Glasgow with their corresponding random networks. Cortical morphological covariance networks were constructed from the cortical thickness derived from the MRI scans of the brain. For a given modularity threshold, networks derived from the MD group showed similar number of modules compared to their corresponding random networks, while networks derived from the LD group had more modules compared to their corresponding random networks. The MD group also had fewer gray nodes—a measure of overlapping modular structure. These results suggest that apparent structural difference in brain networks may be driven by differences in cortical thicknesses between groups. This demonstrates a structural organization that is consistent with a system that is less robust and less efficient in information processing. These findings provide some evidence of the relationship between socioeconomic deprivation and brain network topology.

**Keywords: socioeconomic status, neighborhood deprivation, gray nodes, modularity, graph theory, cortical thickness**

### **INTRODUCTION**

Overlapping large-scale networks that are organized across the cortex form the anatomical and functional foundations of complex cognitive processes (Bressler and Menon, 2010). Complex network analysis based on graph theory has been recently used on neuroimaging data (MRI, MEG, and EEG) to explore different properties of these large-scale cortical network organization (Sporns, 2011). These studies have shown that human brain networks are optimally functioning systems that demonstrate small world properties, and a modular architecture (He et al., 2007; Bassett et al., 2008; Chen et al., 2008; Bullmore and Sporns, 2012). Modularity is an index of community structure within a large-scale network (Newman, 2006). That is, these networks have a tendency to form modules or communities with more connections between nodes within the module than between modules. Structurally, modules represent discrete entities whose functions are separable from those of other modules (Hartwell et al., 1999).

While modularity is usually associated with robustness of the network in biological systems, complex cognitive processes (an index of performance of the network) are unlikely to occur optimally within isolated modules (Hintze and Adami, 2008). Rather, they are likely to be dependent on the coordinated activity between several modules within the large-scale network. Indeed, most biological networks that survive in nature are those that achieve some balance between robustness and performance. Intuitively, it would be beneficial if the human brain network demonstrated modularity—increasing its robustness—but also had an architecture that facilitates efficient information transfer between modules—thereby improving performance. Therefore, while maintaining the advantages of having a modular architecture, we propose that the human brain will also demonstrate an overlapping modular architecture, where certain nodes (we call gray nodes) are included in many modules at the same time (**Figure 1**) (Zhao et al., 2011). Within an information processing system, such architecture, will improve information transfer between modules thereby increasing efficiency and performance of the network in terms of having lesser number of edges and shorter average path lengths. In short, while modularity represents the community architecture within a network, gray nodes represents an index of overlapping communities.

Survival in adverse environments may be associated with changes in network structure that make them less robust and reduce their performance. Neighborhood level socioeconomic status (SES) is associated with adversity and the presence of risk factors for reduced physical and neurocognitive health (Diez Roux and Mair, 2010; Srireddy et al., 2012). If indeed, cognitive functions are dependent on optimal functioning (and hence structure and topology) of large-scale brain networks, it is possible that SES is associated with changes in large-scale network structure. A small number of neuroimaging studies have shown SES to be associated with variations in individual brain anatomy and functional connectivity in adults (Gianaros et al., 2007, 2008). While network structure and topology have been found to be disrupted in a number of mental illnesses, no study has examined the relationship between neighborhood socioeconomic deprivation and brain network structure in humans.

The aim of the present study was to apply complex network analysis to examine the structural characteristics modularity and gray nodes—of cortical networks derived from cortical morphology correlation (**Figure 1**). We also examined

**FIGURE 1 | Shows the modular architecture (A) and gray nodes (B).** Gray nodes: Consider two fully connected networks **(B)**, with four nodes each and are fully connected. The two networks can be connected in two different ways. If they are connected as the first left in the bottom, then one additional edge is used. On the other hand, if they share the two nodes depicted in gray, then the combined module saves resources, i.e., there are two nodes and two edges less than the first combination. In addition, the average path lengths are shortened than the one with the non-sharing combination.

these structural characteristics in relation to socioeconomic deprivation. There is growing evidence that cortical morphology covariation is an indicator of connectivity between different regions of the brain (Worsley et al., 2005; Lerch et al., 2006; He et al., 2007; Bassett et al., 2008; Zalesky et al., 2010; Alexander-Bloch et al., 2013). Graph-theoretical network analyses based on morphological correlations have been used to examine brain network structure in healthy and clinical samples (He et al., 2007, 2009; Bassett et al., 2008).

Using complex network analysis of magnetic resonance imaging (MRI) surface-based morphometry we investigated the topological features of whole cortical anatomical networks in 42 neurologically healthy men from the most deprived (MD) and least deprived (LD) neighborhoods of Glasgow (Sporns, 2011). The connectivity matrices in the present study were derived from region-wise cortical thickness correlations between 68 anatomical parcellations and subjected to complex network analyses. We propose that the brain networks derived thus will show an overlapping modular architecture—by the presence of modules and gray nodes. We also examined to determine if these structural properties differed significantly between neurologically healthy people living in the most deprived (with higher risk of reduced mental health cognitive functioning) and the least deprived regions of Glasgow. Throughout the paper, "structural" refers to the network structure (e.g., modularity or proportion of gray nodes). We have used the term "anatomical" to refer to brain anatomy.

## **MATERIALS AND METHODS PARTICIPANTS**

Participants were recruited as part of a larger study (Psychological, social and biological determinants of ill health (pSoBid). Details of the design of pSoBid have been described elsewhere (Velupillai et al., 2008; Deans et al., 2009; Knox et al., 2012; McGuinness et al., 2012; McLean et al., 2012). Selection of participants was based on the Scottish Index of Multiple Deprivation 2004 (SIMD), which ranks small areas on the basis of multiple deprivation indicators across six domains, namely: income; employment; health; education, skills, and training; geographic access and telecommunications; and housing. Sampling was stratified to achieve an approximately equal distribution of the 666 participants across males and females and age groups (35–44, 45–54, and 55–64 years) within the most (bottom 5% of SIMD score) and LD areas (top 20% of SIMD score). Participants could opt-in for the neuroimaging component of the study. This paper presents the analysis from 42 male individuals who were randomly selected. This included 21 people from the most deprived regions and 21 from the least deprived regions, who were age matched.

## **IMAGE ACQUISITION**

All MR imaging were performed using GE Medical systems, 3T Signa Excite HD system (Milwaukee, USA) using an eight channel phased array (receive only) head coil. An axial 3D T1-weighted IR-FSPGR was acquired with TR = 6.8 ms; TE = 1.5 ms, Inversion Preparation time = 500 ms; Flip angle = 12◦; FOV = 26 cm; Phase FOV = 70%; matrix: 320 × 320; 160 slices; Bandwidth 31.25 kHz; Slab thickness = 1 mm. The acquisition time for this scan was 8 min 54 s.

### *Cortical thickness measurements and parcellations*

Cortical reconstruction was performed with the FreeSurfer image analysis suite, which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). (Dale et al., 1999; Fischl et al., 1999; Fischl and Dale, 2000) Briefly, following skull-stripping and correction of inhomogeneity artifact, constrained region growing was used to create a unitary white matter volume for each hemisphere. The gray-matter/white-matter boundary for each cortical hemisphere was determined using tissue intensity and neighborhood constraints. The white matter surface was tessellated by assigning two triangles to the square face of each surface voxel. This process yielded approximately 160000 vertices per hemisphere. The white matter surfaces were deformed toward the gray matter/pial boundary, with a point to point correspondence at each vertex. Cortical thickness was computed as the distance between the white and the pial surfaces at each vertex. Cross-subject registration of hemispheric cortical surfaces was performed by projecting them onto the spherical representations. The maps produced are not restricted to the voxel resolution of the original images and are thus capable of detecting sub-millimeter differences between groups. The parcellations were obtained using the Desikan sulcogyral-based atlas, which follows the anatomical conventions of Duvernoy. The FS imageprocessing pipeline was visually inspected and corrected at critical points in order to avoid errors permeating through the subsequent analyses. Procedures for the measurement of cortical thickness have been validated against histological analysis and manual measurements. The Desikan Killiany atlas produces 68 parcellations based on gyri and sulci (Desikan et al., 2006). In addition to the Desikan Killiany atlas parcellation scheme, we also used fine-grained parcellation schemes based on anatomical sulcogyral boundaries including the Destrieux atlas, (148 parcellations) and fine-grained parcellation schemes (200, and 1000 parcellations) that did not follow anatomical conventions described in Echtermeyer et al. (Destrieux et al., 2010; Echtermeyer et al., 2011). The pipeline of the analysis and the parcellation are shown in **Figure 2**.

## **CORTICAL THICKNESS—BETWEEN GROUP COMPARISON**

Statistical comparisons of global data and surface maps were generated by computing a general linear model (GLM) of the effect of neighborhood deprivation (independent variable) on thickness (dependent variable) at each vertex in the cortical mantle, using the Query, Design, Estimate, Contrast (QDEC) interface of FreeSurfer. Age was used as nuisance covariate in the model. QDEC is a single-binary application included in the FreeSurfer distribution that is used to perform group averaging and inference on the cortical morphometric data produced by the FreeSurfer processing stream. (http://surfer*.*nmr*.*mgh*.*harvard*.* edu/fswiki/Qdec). Maps were created using statistical thresholds of *p* = 0*.*05 and were smoothed to a full width half maximum (FWHM) level of 20 mm. Since this analysis involved performing a GLM analysis at 160000 vertices, these maps were corrected for multiple comparisons by means of a cluster-wise procedure using the Monte Carlo Null-Z simulation method adapted for cortical surface analysis and incorporated into the QDEC processing stream. For these analyses, a total of 10,000 iterations of simulation were performed for each comparison, using a threshold of *p* = 0*.*05.

#### **NETWORK CONSTRUCTION**

Network construction was based on parcellations of cortical thickness as described by He et al. (2007). We defined an anatomical connection (edge) as statistical associations in cortical thickness between cortical parcellations based on the Desikan Killiany

atlas included in the FreeSurfer pipeline (nodes). The statistical similarity in cortical thickness between 2 regions was measured by computing the Pearson's correlation coefficient across subjects to create an interregional correlation matrix (*N* × *N*, where *N* is the number of brain regions based on Desikan cortical parcellation atlas, here *N* = 68). In order to keep the analysis as close as possible to previous reports, prior to the correlation analysis, a linear regression was performed at every region to remove the effects of age, and mean overall cortical thickness; the residuals of this regression were then substituted for the raw cortical thickness values (He et al., 2007; Chen et al., 2008). In order to be consistent with the cortical thickness group difference analysis presented above, the complex network analyses were repeated without mean overall cortical thickness in the model, but the results of our analysis did not differ significantly (results not shown). A separate matrix was produced for the MD (21 subjects) and the LD (21 subjects). As a first step, all negative correlations were discarded. As the correlation analysis was performed for all 68 × 68*/*2 = 1431 pairs of regions, we performed a multiple comparisons correction to test the significance of these correlations.

We applied the false discovery rate (FDR) procedure separately to each matrix in order to correct the multiple comparisons at a *q* value of 0.2 (this was chosen as at 0.05, both matrices were very sparse). (Genovese et al., 2002) Using this threshold, we constructed a symmetric connection matrix (**Figures 5**, **6**), whose element was 1 if the cortical thickness correlation between 2 regions was statistically significant and 0 otherwise. This binarized connection matrix captures the underlying anatomical connection patterns of the human brain common to the population sample under study. We repeated all the analyses on matrices derived from the fine grained parcellation schemes described above, in order to validate our findings using multiple parcellation schemes.

#### **MODULARITY**

All the modularity metrics were calculated on the above two adjacency matrices separately and compared to corresponding random networks. Modularity is an intuitional concept and there are variations in the mathematical definitions, where each has its own advantages and disadvantages. One common property among the various ways of defining modularity, however, is accounting for the agreed intuition about modularity, i.e., a module is a subset of nodes in a graph, whose connections among the elements within the subset are much denser than the ones to nodes outside the subset. Newman suggested the following modularity measure,*Q*:

$$Q = \max\_{s \in \mathcal{S}} \frac{1}{4m} s^T B s,$$

where *s* is a column vector and element of the set *S*, *S* is the set of all column vectors whose dimension are equal to the number of nodes in the graph, *n*, and each component of the vector is either −1 or +1, *(*·*) <sup>T</sup>* is the transpose. *B* is equal to *A* − *kkT/ (*2*m)*, *A* is the adjacency matrix, whose dimension is *n* × *n*, and the *i*-th column (or row) and *j*-th row (or column) element is 1 (or 0) if *i*-th and *j*-th nodes are connected by an edge (or if there is no edge), *k* is a column vector whose element is the number of edges connected for each node, i.e., the degree of node, and *m* is the total number of edges. Roughly speaking, *B* quantifies the difference between the number of edges found in a subset of the given network structure, i.e., *A*, and the expected average from the random graphs, whose nodes degree is the same as the one of the given graph, i.e., *kkT/ (*2*m)*. Hence, positive *Q* values imply that there are more edges found than the expected and it is, therefore, a module.

By obtaining *s* that maximizes the modularity, *Q*, the nodes are divided into two groups, i.e., modules, depending on the corresponding values in the maximizing vector, *s*. The maximization problem, however, is the integer quadratic programming problem, which is NP-hard. It is even computationally very difficult to obtain the true solution, which gives the global maximum value of *Q*. Note that *Q* is always less than or equal to 1. If the condition for *s* is relaxed so that it can take any real numbers, then the problem becomes finding maximum eigenvalue and the corresponding eigenvector of the matrix, *B*. This can be solved efficiently using the power-iteration, i.e., choosing an arbitrary initial vector, *s*0, and recursively updating the vector using *sk*<sup>+</sup><sup>1</sup> = *Bsk* until it converges. Then, *s* maximizing *Q* is calculated simply by taking the sign of converged *sk*. To increase the chance of finding the global solution, these procedures are repeated a number of times with a different random initial vector, *s*0. If the calculated maximum value, *Q*, is positive (or negative), then the graph is divided (or declared indivisible).

Once the graph is divided into two modules, then each module is inspected whether it can be further divided by solving the following the maximization problem:

$$
\Delta Q = \max\_{r \in \mathbb{S}^\sharp} \frac{1}{4m} r^T B^\mathfrak{g} \, r,
$$

where *r* is an element of the set *s <sup>g</sup>* , *s <sup>g</sup>* is the set of *ng* -dimension column vectors whose element is either +1 or −1, *ng* is the number of nodes in the module, which is found in the previous step, *B<sup>g</sup>* is equal to *Bij* − diag [*k<sup>g</sup>* ], *Bij* is a matrix constructed by a part of *B*, where the rows and columns belong to the module, *kg* is the degree of each nodes only concerning *Bg* , and diag [·] is the diagonal matrix, where the diagonal terms are given by the vector in the argument and the other elements are zero. Again, if *-Q >* 0(or *-Q* ≤ 0), then the module is divided into two smaller modules (or declared indivisible). The above procedures are repeated on every module recursively until all modules are declared indivisible. By definition, the divisibility of a module is determined based on whether the modularity measure is positive or not. Very often, it is, hard to justify whether some subgroups of a graph are modules if the modularity contribution, i.e., *Q* or *-Q*, is very close to zero. As the mathematically possible maximum value is 1, the modular structure is much clearer if the modularity is closer to 1. Hence, the number of modules is calculated for various *Q*-threshold, which decides when modules are declared as indivisible.

#### **GRAY NODES**

A network, in general, is not a simple collection of modules but a combination of complicated overlapped modular structures, i.e., it demonstrates a hierarchical modular architecture. The overlapped modular structures are hard to decipher into elementary modules that pertain to the whole network. There are several methods to unravel the overlapping modular structure. In order to use a consistent measure with the modular calculation, an extended modularity (*Qe*) is defined as follows:

$$Q\_{\varepsilon} = \max\_{s\_{\varepsilon} \in \mathcal{S}\_{\varepsilon}} \frac{1}{4m} s\_{\varepsilon}^T B s\_{\varepsilon},$$

where *se* is an element of the set, *Se*, and the set *Se* is the collection of vector, *se*, whose dimension is again, *n*, i.e., the number of nodes, and its element is either -1, +1, or 0. Compare to the vector *s* in *S*, *se* has one more degree of freedom in possible values (Zhao et al., 2011). The nodes corresponding to zero are called gray nodes, which are included in multiple modules at the same time or are not included in any module. *-Qe* is defined in the similar manner. Gray node is a similar concept to that of connector hub and hierarchical or overlapping modular structure. While connector hubs are defined as nodes with greater than average degree of the network and distributed between both local and long range connections, gray nodes are defined as nodes that are shared by modules. It is an index of overlapping modular architecture of the network. Previous literature has described such overlapping architecture based on a prior definition of modularity by Newman and Girvan (Newman and Girvan, 2004; Nicosia

et al., 2009; Lazar et al., 2010; Wang et al., 2012). On the other hand, "gray nodes" are a unified way to define the structure in the more recent modularity definition by Newman (Newman, 2006). This provides an advantage that we measure modular architecture, and the overlapping architecture using a consistent measure without requiring significant changes in the algorithm (Newman, 2006).

All calculations presented in this paper are based on Monte-Carlo simulations performed 1000 times. The distributions of all calculations are confirmed to be similar to Gaussian distributions (data not shown). Hence, there is no danger that the analyses based on the mean and the variance may give any false interpretations of the true distribution of the data. All graphs were compared to random graphs (with the same number of nodes and degree distribution as the corresponding brain networks).

## **RESULTS**

Demographic details, differences in risk factors and performance on cognitive tests of the participants are shown in **Table 1**. In general, participants in the MD group had higher inflammatory and metabolic risk markers, poorer GHQ scores and performed poorly on a number of cognitive tests. Supplemental file shows the details of how early life and current individual level SES were derived. **Table A1** shows that individual level SES covaried significantly with the neighborhood level deprivation status, and hence were not included in our data analysis.

#### **Table 1 | Demographic and clinical characteristics of study participants.**


*T, unpaired t-test; BMI, body mass index; CRP, C-reactive protein; IL-6, interleukin-6; ICAM-1, intercellular adhesion molecule.*

### **CORTICAL THICKNESS DIFFERENCES BETWEEN GROUPS**

Initial analysis of cortical thickness across groups showed that those from the most deprived population had significant cortical thinning pertaining to bilateral perisylvian cortices. (**Figure 3**).

#### **NETWORK ANALYSIS**

We conducted all analyses on binarised matrices derived from interregional correlations of cortical thickness. Initial examination of number of isolated modules showed that for a given correlation threshold, the least deprived group had greater number of isolated groups compared to the deprived group (**Figure 4**). The raw networks and FDR filtered networks are shown in **Figures 5**, **6**. The distribution of the groups' correlation coefficients is shown in **Figure 7**. A direct comparison of the networks derived from the above populations, was not possible, as for a given correlation threshold, the sparsity (density) of the two networks were significantly different (**Figure 8**). In addition, the FDR procedure thresholded the two networks significantly differently. This method of thresholding resulted in different number of edges—k—(sparsity) in the networks of the two groups because of differences in their inter-regional cortical thickness correlations. We therefore compared the network structure derived from the groups to their corresponding random networks. The results of this analysis are shown in **Figures 9**, **10**.

#### *Modularity and grey nodes*

Firstly, the networks derived from both groups showed a modular architecture, and the presence of gray nodes. Toward a modularity of 0.3 (strong modularity), the least deprived network had more modules, compared to its corresponding random network. However, the most deprived network, showed no difference from its random counterpart.

With regards the gray nodes, for a given a modularity toward 0.3, the least deprived network showed significantly greater number of gray nodes compared to the corresponding random network. However, the most deprived network showed significantly

**most deprived and the least deprived groups.** Red regions pertain to regions where the most deprived group showed cortical thinning. Covariates in the model—Age and alcohol use.

smaller proportion of gray nodes compared to its random counterpart. While the differences between groups were maintained in the Destreaux atlas (148 parcels) that followed the sulcogyral boundaries, these differences were not seen with the finer grain parcellations of 200 and 1000 parcels that did not follow the sulcogyral scheme. (**Figures 11A–C**).

## **DISCUSSION**

We have shown here that brain networks derived from cortical morphological correlations show a modular organization, and indeed an overlapping modular architecture as demonstrated by the presence of gray nodes. We have also shown that neurologically healthy subjects from the MD regions of Glasgow differ significantly in their brain network structure from those from the LD regions in comparison to their corresponding random networks on relatively coarse parcellations schemes that followed the

**FIGURE 5 | The raw correlation matrix for each group shows that two groups have almost equal number of non-zero components in the matrix.** The correlation matrix for each group is given by a 68 × 68 matrix, where each value in the matrix is calculated from the cortical thickness correlation measured in 21 individuals. Affluent: Least deprived; Deprived: Most deprived.

**FIGURE 6 | In the correlation matrix for each group, all values below the FDR threshold are set to zero, where.** About three-times more edges survived the FDR procedure in the most deprived than the least deprived group. Affluent: Least deprived; Deprived: Most deprived.

sulcogyral boundaries. Brain networks in the MD group showed same number of modules and smaller proportion of gray nodes compared to their corresponding random network. These differences, however, disappeared at fine-grained parcellation schemes that did not follow the sulcogyral schemes.

A number of recent studies have shown that human brain network structure derived from anatomical covariance demonstrates a modular architecture (Chen et al., 2008, 2011). There are a number of advantages in having a modular architecture. Kaiser et al. suggest that this feature allows for low wiring costs; are time scale separable; allows for the coexistence of integration and segregation within a network; transient chimera states of resynchronization and synchronization; and also allows for rapid and robust assembly (Kaiser, 2007). In addition, a modular architecture is robust against random attacks on the network and helps to contain the effects of these attacks to the module, rather than spreading through the network.

We compared the brain network graphs with random graphs that had similar degree to the corresponding brain network. For

**FIGURE 8 | Correlation and sparsity (Number of zeros divided by Maximum possible number of edges) relations in cortical thickness network.** The most deprived have more edges (denser network) than the least deprived for a fixed correlation threshold. On the other hand the least deprived would have more false positive edges than the deprived and/or the deprived would have more false negative edges than the least deprived for a fixed sparsity. Affluent: Least deprived; Deprived: Most deprived.

**FIGURE 9 | Number of modules and the corresponding random graphs [indicated by "(R)"] with respect to various modularity (***Q***) threshold.** Error bars represent the 1σ-bound for each case. In the module calculation algorithm, if the module contribution, *Q* or *-Q*, is less than the threshold, it was declared indivisible. Higher thresholds imply strong modules. Affluent: Least deprived; Deprived: Most deprived.

both the LD and MD groups, at lower modularity thresholds, the brain network graphs had fewer modules compared to their corresponding random graphs. However, this phenomenon was reversed at higher thresholds. This is possibly because within the constraints of fixed resources (nodes/edges), brain networks enhance a few specific modules by rewiring and sacrificing unwanted modules.

In our study, for a given number of modules, the brain networks in the LD group showed stronger modular organization than their corresponding random graphs. In other words, the networks derived from the most deprived group had more edges between modules, which weakened the modular architecture. Previous work by Chen et al. using a similar technique showed

**FIGURE 10 | Shows the proportion of gray nodes with respect to the corresponding Modularity threshold.** Error bars represent the 1σ-bound for each case. In the module calculation algorithm, if the module contribution, *Q* or *-Q*, is less than the threshold, it was declared indivisible. Higher thresholds imply strong modules. Gray nodes have two implications in the network structure: (i) efficient usage of resources and (ii) shorter average distance between nodes. Recycling existing nodes and edges to combine multiple modules saves limited resources to construct the network. It is believed that reducing wiring resources is one of the major selection pressure on the brain network evolution. Affluent: Least deprived; Deprived: Most deprived.

that modules derived using correlations of cortical thickness, broadly gave out six functionally relevant modules (Chen et al., 2008). Using the same number (six modules) as Chen et al., the modules were functionally more relevant in the LD population (data not shown). For example, all anatomical regions pertaining to language function were integrated together within a given module. However, this was not the case with the MD. Anatomical regions pertaining to similar function were distributed across several modules, consistent with poor functional modular organization at a given threshold. While these modularity differences may be due to anatomical differences between groups that we have shown, these may have functional implications, as anatomical networks have been found to overlap with functional networks (Alexander-Bloch et al., 2013). If we consider these networks as information processing systems, then such a difference in network structure could contribute to greater noise and less efficient information processing within the system. However, a direct interpolation of the results of our study is not possible due to the static nature of our data.

We describe a new metric—gray node—as a measure of overlapping modular organization. While modularity improves the robustness within a system, it is unlikely that our brain network achieves optimal performance by operating as a number of different isolated modules. As stated previously, cognitive processes are likely to be the result of a number of modules interacting with each other in a fast and efficient way. The overlapping modular architecture—represented here by the presence of gray nodes—is beneficial in that given a fixed number of resources it provides the best modular architecture, maximizing the communication between modules thereby achieving a balance between robustness and optimal performance. Gray nodes have two implications in the network structure: i) efficient usage of resources and ii) shorter average distance between nodes. Recycling existing nodes and edges to combine multiple modules saves limited resources to construct an efficient network. It is believed that reducing wiring resources is one of the major selection pressures on the brain network evolution. Our results suggest that the networks derived from the MD group show much lower efficiency compared to their corresponding random network (Achard and Bullmore, 2007; Bullmore and Sporns, 2009). While metrics describing overlapping modules have been outlined previously, gray nodes have the advantage that it was derived from Newman (2006) and integrates well with the given modularity metric (Newman, 2006).

While the structural differences may be driven by the difference in cortical thickness between the two groups, the reason for the anatomical difference between the two groups is not clear. It should be noted that the groups differed on a number of variables that could potentially explain the observed difference. For example, those from the most deprived had poorer mental health and also had higher levels of inflammation. (See **Table 1**) We have previously shown inflammatory markers to be associated with cortical thickness (Krishnadas et al., 2013). We were, however, underpowered to explore the role of potential mediators that could explain the difference between groups in structural properties. Previous studies have demonstrated age related changes to modularity (Chen et al., 2011). Our groups were matched for age. Similarly, mental illnesses have shown to be associated with disruption to the modular architecture. A few studies have also examined this property in medical conditions like MS and epilepsy (He et al., 2009; Vaessen et al., 2012). A number of studies have shown an association between socioeconomic deprivation and brain anatomy and function in both children and adults, though none have examined the association with network structure (Gianaros et al., 2011; Hanson et al., 2011; Jednorog et al., 2012). A key question that remains is how these anatomical differences could contribute to poorer cognitive functioning and mental health. Interestingly, the MD group performed poorly on all cognitive tests, including NART (National adult reading test)—a test that is relatively stable through age, and often considered a test of measure of the peak achieved intellectual functioning. We did not examine if less modularity was directly associated with poorer cognitive functioning as utilizing correlation coefficients to construct the matrix meant that indices of modularity could not be calculated at an individual level. However, change in network structure is a potential mechanism by which regional anatomical brain deficits may contribute to global network topology, thereby resulting in poorer cognitive function. Previous studies have examined the relationship between intelligence quotient (IQ) and network properties. For example Li et al. found a significant positive correlation between number of edges and IQ. They also found that those with greater IQ had shorter path lengths, greater clustering coefficient (similar to our findings) and in general greater global efficiency of structural networks in the brain (Li et al., 2009). Similarly using resting state fMRI to examine the overall organization of the brain network using graph analysis, van den Heuvel et al. showed a strong negative association between characteristic path length of the resting-state brain network and IQ (Van Den Heuvel et al., 2009). They suggest that human intellectual performance is likely to be related to how efficiently the brain integrates information between various brain regions.

#### **NEIGHBORHOOD LEVEL vs. INDIVIDUAL LEVEL SES.**

Socio-economic status (SES) refers to a multidimensional construct that is usually measured using a number of economic (e.g., income) and non-economic (e.g., education) indicators (Hackman et al., 2010). SES can be measured at an individual/household or at a neighborhood level. Regardless of the level of measurement (individual/neighborhood), SES has been associated with significant health disparities (Diez Roux and Mair, 2010). Most of the studies previously mentioned have examined the association between individual level SES and brain morphology. But individual level explanations for poor health do not capture significant social and structural determinants of ill health (Diez Roux and Mair, 2010). It is well-established that social circumstances have direct biological consequences, as well as impact on health behaviors (see Diez Roux and Mair for a detailed review on neighborhood deprivation). However, relatively small number of studies have explored the contributions of individual level SES indicators with neighborhood level indicators to health inequalities. Neighborhood level deprivation has been associated with poor health outcomes due to inequalities in resource distribution. These neighborhoods have physical (e.g., access to food) and social (e.g., violence) attributes that are contributors to health outcomes. However, individual and neighborhood deprivation are likely to interact significantly. For example, Stafford and Marmot found that living in a deprived neighborhood has the most adverse impacts on poorer individuals possibly because they are more dependent on collective resources of the neighborhood (Stafford and Marmot, 2003). In our study, individual level SES covaried significantly with neighborhood level SES. (For details of this analysis see **Table A1** in Appendix) Due to the nature of the sampling technique, people from the most deprived neighborhoods also had poorer individual SES. This is partly because neighborhood deprivation scores (SIMD) are derived from data pertaining to individuals in the area. Since our groups differed inherently in their individual SES, it was deemed inappropriate to co-vary for the effects of individual SES (Miller and Chapman, 2001). Our relatively small sample size was also not sufficiently powered to examine if individual SES contributed significant variance over and above that explained by neighborhood SES or vice versa. The extreme group sampling technique prevented us from examining any dose-response effect of either individual or neighborhood level deprivation in our sample.

### **EFFECT OF PARCELLATION SCHEME ON NETWORK STRUCTURE**

Zalesky et al. have previously shown that network topology vary considerably as a function of the spatial scale of the atlas used (Zalesky et al., 2010). Previous reports that have examined cortical thickness covariance network structure in clinical and non-clinical populations have used the same parcellation scheme (Desikan-Killiany atlas) used in our study (Raj et al., 2010; Hanggi et al., 2011; Romero-Garcia et al., 2012; Yang et al., 2012). Of note, Romero-Garcia et al. in order to examine the effect of network resolution on topological properties, compared the Desikan-Killiany atlas based parcellation with finer parcellation schemes of up to 1494 parcellations (Romero-Garcia et al., 2012). Interestingly they found that highly grained cortical scales showed enhanced local connectivity (clustering coefficient), and local efficiency, but increased path length and decreased global efficiency. Our findings resonate that of Romero-Garcia et al., in that, at different parcellation schemes, the network topologies differed (Romero-Garcia et al., 2012). For fine-grained parcellation schemes that did not follow sulcogyral boundaries, the LD brain network, and MD brain network were similar. At a modularity threshold of around 0.3, both network structural properties looked similar to their random counterparts (suggesting a decrease in global properties at more fine grained schemes) (**Figures 1A,B**).

Anatomically, since cortical thickness is a continuous measure, regions that lie close to each other will show very similar cortical thickness and hence high correlation. Here, a fine parcellation schemes, may uncover local connection (or a forking-U fiber connection), while a coarse may not (see Figure 1 in Zalesky et al) (Zalesky et al., 2010). In addition, regions close to each other are likely to be anatomically connected by the tangential neurons and dendrites. It is possible that in our case, the group differences disappeared when geometrically close connections were exposed at the finer parcellation schemes. In addition, at finer parcellation, where the number of parcels far exceed the number of subjects in the study, the study may have been significantly underpowered to show significant differences between groups (Zalesky et al., 2010).

It is also possible that network structure derived from relatively coarse parcellations are more representative of large scale cortical networks, while the networks derived from the fine-grained parcellations also include the meso/microscale connections representing regional/local connections. Whatever the case, it is clear that the granularity of chosen parcellations may affect the results of the network analysis. Our data suggest that when exploring connectivity, choosing the right granularity that is best suited to answer the question of interest is vital. However, clear cut guidelines pertaining to this are absent. One suggestion is that in order to answer clinical questions, anatomically relevant atlases like AAL or the sulcogyral parcellations (FreeSurfer) as used in our study may be more relevant. Interestingly for a finer (than Desikan atlas) parcellation that follows the sulcogyral boundaries (the Destreaux atlas—149 parcellations), the difference between the brain and random networks in the most deprived group disappear at around a modularity threshold of around 0.2 (**Figure 11A**).

#### **SPARSITY (DENSITY) AND MODULARITY**

Although we found significant differences between the networks and their corresponding random graphs, we did not perform a direct comparison of the network structure between the two groups, as the thresholds imposed by the FDR correction led to matrices that were significantly different in their sparsity (density). Thresholding a matrix is a problem when comparing networks that have different sparsity for a given correlation coefficient (Van Wijk et al., 2010). While the reason for the sparsity difference between the groups is not known, revealing topological differences gives deeper insights into the difference in networks than just revealing the sparsity difference. One recommended way to solve this problem is by fixing the sparsity (density) of a matrix, and comparing the networks at the same fixed sparsity threshold (Hanggi et al., 2011). This approach will, however, increase the false negative or false positive correlations at a given threshold. For instance, in our case, at more than 90% of correlation thresholds, the LD network was more sparse (less edges—k) than the MD. i.e., for a given correlation threshold, the networks from both the groups were different in their size (the number of edges). The difference in modularity between groups may therefore be k dependent. This difference in correlation threshold may have arisen from anatomical difference in the bilateral perisylvian cortical thickness we found between groups. While these morphological differences could have led to a reduction in correlation between regions that are actually connected, this could also have led to an increase in the number of spurious correlations (false positive), between regions that are not biologically connected, thereby contributing to noise within the network. Therefore, introducing false edges by fixing the sparsity was not thought to be meaningful.

## **CORTICAL THICKNESS CORRELATION AS A MEASURE OF CONNECTIVITY**

While the biological meaning of structural covariance is not clear, structural covariance networks have been found to be genetically heritable, associated with cognitive function, recapitulate functional networks, and change over the life span. See Alexander-Bloch et al. (2013) for a detailed recent review of this literature (Alexander-Bloch et al., 2013). Cortical volume is a construct that is derived from two distinct properties of the cortical sheet: cortical thickness and surface area and have distinct cellular and genetic basis. Rakic's (2007, 2009) radial unit hypothesis proposes that symmetrical cell division within the neural stem cell pool in the ventricular zone causes an exponential increase in the number of radial columns—that result in surface area (SA) expansion. This is independent of asymmetrical cell division in the founder cells that is responsible for a linear increase in the number of neurons within a radial column, contributing to cortical thickness (CT) (Rakic, 2007). Complex network analysis using graph theory using cortical structural covariance networks derived from CT and cortical SA shows different structural properties, suggesting that they contribute to different properties within cortical networks (Sanabria-Diaz et al., 2010). Cortical gray matter volume is almost entirely driven by differences in the cortical SA rather than CT. (Im et al., 2006) Secondly, recent large scale studies have shown that these two parameters—CT and SA—have independent genetic basis (Panizzon et al., 2009). Thirdly, life course trajectories of these cortical parameters seem to be different. While gyrification—a ratio of total SA to pial SA remains fairly stable post childhood through to early adulthood, CT changes dynamically through this period (Rathbone et al., 2011; Raznahan et al., 2011; Salinas et al., 2012). However, more recent studies suggest that the relation between age and cortical parameters in adulthood, are complex (Hogstrom et al., 2012). CT in addition appears to be highly susceptible to various environmental influences over the life course such as smoking, alcohol dependence, and marijuana use while SA appears to be influenced by various unique developmental factors (Kuhn et al., 2010; Lopez-Larson et al., 2011; Momenan et al., 2012). This highlights the importance of studying volume and thickness independently in morphometric studies (Winkler et al., 2010). Surface area appears to be influenced by various unique developmental factors and is less susceptible to age-related differences in later life (ref). These and other findings suggest that while cortical surface areas increase significantly prenatally and remain fairly stable post childhood, cortical thickness changes dynamically across the lifespan (Raznahan et al., 2011; Salinas et al., 2012; Shaw et al., 2012). We restricted our analysis to cortical thickness as we were examining the association between what an environmental variable (deprivation) and a cortical parameter (cortical thickness) that has previously shown to be influenced by environmental factors. Further analysis using other parameters may reveal differences in structural properties that are contributed by factors that may be influenced early in life.

#### **LIMITATIONS**

While the positive features of this study include a wellcharacterized community based cohort, there are limitations to be acknowledged: the cross-sectional design limits our ability to attribute causation and there is some selection bias in that the participants opted in. We did not include any sub-cortical regions particularly those that are relevant to physiological stress response. Smaller sample size meant that there was a potential for type 2 error, especially with regards the fine grain parcellations. We excluded female subjects in order to reduce variance in cortical morphology pertaining to gender. Further work would involve replication of the study in a larger population, including younger population, targeting critical periods of brain growth. Finally, future work to develop a clearer biological framework of a more comprehensive investigation of metabolic and inflammatory markers may be more informative.

In summary, people from the MD population show less modular and overlapping modular architecture of the brain networks derived from cortical morphology compared to their corresponding random graphs at a coarse sulcogyral parcellation scheme. At fine grained parcellation scheme that did not follow sulcogyral boundaries, this difference disappeared. While the difference in network structure at the coarse level may be the result of anatomical differences at a large scale level, the exact etiopathogenesis and the consequence of this difference is not clear. Taken together we propose that brain networks associated with MD group may be less efficient in information and signal processing at a large scale level. Future studies should look at longitudinal functional and effective connectivity studies using MRI and EEG/MEG to explore the effect of socioeconomic status on development.

## **AUTHOR CONTRIBUTIONS**

Rajeev Krishnadas and Jongrae Kim are joint first authors who contributed equally to this work. Rajeev Krishnadas and Jongrae Kim analyzed data and wrote the paper. John McLean created the MRI protocol and analyzed the data. All other researchers were involved in designing, performing the research and discussing the paper.

## **ACKNOWLEDGMENTS**

We would like to acknowledge Dr Mortimer and Theresa Sackler for their continuing support. We would also like to thank Prof Cheol Han for his help with fine grained parcellation schemes. Research Professor at Korea University, Seoul Korea.

#### **FUNDING**

This work was funded by the Glasgow Center for Population Health, a partnership between NHS Greater Glasgow and Clyde, Glasgow City Council and the University of Glasgow, supported by the Scottish Government. The funders had a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

## **REFERENCES**


sclerosis associated with white matter lesion load. *Brain* 132, 3366–3379. doi: 10.1093/brain/awp089


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 01 June 2013; accepted: 11 October 2013; published online: 12 November 2013.*

*Citation: Krishnadas R, Kim J, McLean J, Batty GD, McLean JS, Millar K, Packard CJ and Cavanagh J (2013) The envirome and the connectome: exploring the structural noise in the human brain associated with socioeconomic deprivation. Front. Hum. Neurosci. 7:722. doi: 10.3389/fnhum.2013.00722*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Krishnadas, Kim, McLean, Batty, McLean, Millar, Packard and Cavanagh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## **APPENDIX**

## **EARLY LIFE AND CURRENT SOCIOECONOMIC STATUS (SES)**

Correspondence analysis was used to explore the factor structure of early and late SES. This is similar to factor analysis for categorical data. These analyses confirmed that markers of early and late SES are well-represented by single factors, and determined the corresponding weight associated with each level of each marker. By taking levels with positive and negative weights as representing relative deprivation or affluence, the following cut-offs were used to derive early and late SES scores: Early life SES (ESES) consisted of the following items: number of siblings (*>* 3 = 0), people per room (*>* 1 = 0), paternal social class (IIIM or below = 0), parental housing tenure (Not owner = 0), use of a car by the family (no car = 0). The current SES (CSES) score was derived from current income (*<* 25k = 0); current social class (III or lower = 0); current housing tenure (not owner = 0). For each variable, those deemed to be least deprived scored 1 and those deemed to be most deprived scored 0. These scores were then summed for each, giving total score (0–5 for ESES, 0–3 for CSES), higher scores suggesting more affluence. The components are shown in the **Table A1**.

#### **Table A1 | Individual level SES.**


*\*Fishers exact test.*

## Extraversion and neuroticism relate to topological properties of resting-state brain networks

#### *Qing Gao1 \*, Qiang Xu2, Xujun Duan2, Wei Liao2, Jurong Ding2, Zhiqiang Zhang3, Yuan Li 4, Guangming Lu3 and Huafu Chen2 \**

*<sup>1</sup> School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China*

*<sup>2</sup> Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China*

*<sup>3</sup> Department of Medical Imaging, Jinling Hospital, Clinical School, Medical College, Nanjing University, Nanjing, Jiangsu, China*

*<sup>4</sup> School of Politics and Public Managements, University of Electronic Science and Technology of China, Chengdu, China*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Henrik Walter, Charité Universitätsmedizin, Germany R. Matthew Hutchison, Western University, Canada*

#### *\*Correspondence:*

*Qing Gao, School of Mathematical Sciences, University of Electronic Science and Technology of China, No. 4, Section 2, N. Jianshe Rd., Chengdu 610054, China e-mail: qingqing.gao@gmail.com; Huafu Chen, Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section 2, N. Jianshe Rd., Chengdu 610054, China e-mail: chenhf@uestc.edu.cn*

With the advent and development of modern neuroimaging techniques, there is an increasing interest in linking extraversion and neuroticism to anatomical and functional brain markers. Here, we aimed to test the theoretically derived biological personality model as proposed by Eysenck using graph theoretical analyses. Specifically, the association between the topological organization of whole-brain functional networks and extraversion/neuroticism was explored. To construct functional brain networks, functional connectivity among 90 brain regions was measured by temporal correlation using resting-state functional magnetic resonance imaging (fMRI) data of 71 healthy subjects. Graph theoretical analysis revealed a positive association of extraversion scores and normalized clustering coefficient values. These results suggested a more clustered configuration in brain networks of individuals high in extraversion, which could imply a higher arousal threshold and higher levels of arousal tolerance in the cortex of extraverts. On a local network level, we observed that a specific nodal measure, i.e., betweenness centrality (BC), was positively associated with neuroticism scores in the right precentral gyrus (PreCG), right caudate nucleus, right olfactory cortex, and bilateral amygdala. For individuals high in neuroticism, these results suggested a more frequent participation of these specific regions in information transition within the brain network and, in turn, may partly explain greater regional activation levels and lower arousal thresholds in these regions. In contrast, extraversion scores were positively correlated with BC in the right insula, while negatively correlated with BC in the bilateral middle temporal gyrus (MTG), indicating that the relationship between extraversion and regional arousal is not as simple as proposed by Eysenck.

**Keywords: resting-state, functional magnetic resonance imaging, graph topological properties, extraversion, neuroticism**

## **INTRODUCTION**

In Eysenck's personality theory, he proposed three fundamental dimensions of personality: extraversion, neuroticism, and psychoticism (Eysenck, 1967; Eysenck and Eysenck, 1985). It is now acknowledged that extraversion and neuroticism have their biological bases (Matthews and Gilliland, 1999), while the neuropsychology of the third dimension, psychoticism, has not been worked out in detail. The arousal theory of Eysenck (1967) related extraversion to arousability of the reticulocortical circuit and proposed a higher arousal threshold in cortex and higher levels of arousal tolerance in extraverts (Eysenck, 1967; Eysenck and Eysenck, 1985; Fischer et al., 1998). Neuroticism, on the other hand, is associated with arousability of the limbic circuit, such that individuals with higher neuroticism scores have greater activation levels and lower thresholds within subcortical structures (Eysenck, 1990; Wei et al., 2012).

With the advent and development of modern neuroimaging techniques, there is increasing interest in exploring neuroanatomical or neurofunctional correlates of extraversion and neuroticism, to test the theoretically proposed biological explanation of the two fundamental dimensions. Neuroanatomical studies have found extraversion was associated with structural/anatomic variations in the middle and inferior frontal regions, fusiform gyrus, and insula (INS), whereas neuroticism was associated with variations in the orbitofrontal cortex, precentral gyrus (PreCG), and amygdala (AMYG) (Rauch et al., 2005; Omura et al., 2005b; Wright et al., 2006, 2007; Sollberger et al., 2009; DeYoung, 2010). In neurofunctional studies, functional magnetic resonance imaging (fMRI) experiments have also demonstrated that specific brain regions that are engaged during cognitive-affective tasks were associated with specific personality dimensions. For example, activations in the prefrontal cortex, parietal cortex, anterior cingulated cortex (ACC), and middle temporal gyrus (MTG) were correlated with extraversion (Canli et al., 2001; Eisenberger et al., 2005; Hutcherson et al., 2008; Tamura et al., 2012), while activations in the frontal cortex, dorsomedial prefrontal cortex, and AMYG were related to neuroticism (Canli et al., 2001; Haas et al., 2007; Hooker et al., 2008; Harenski et al., 2009). In addition, a positron emission tomography (PET) study assessing resting regional cerebral blood flow (rCBF) found that regions in ACC and temporal lobes were correlated with extraversion (Johnson et al., 1999). Extraversion was associated with regional cerebral glucose metabolism (rCMRglu) assessed by PET in right putamen, while neuroticism was associated with rCMRglu in the medial prefrontal cortex (MPFC) (Kim et al., 2008). These studies indicated specialized, spatially distributed regions were associated with personality dimensions of extraversion and neuroticism, respectively, and provided neurobiological evidence for the hypothesized biological model of Eysenck's personality.

Instead of detection activation paradigms by task-based fMRI, resting-state fMRI studies observe intrinsic spontaneous fluctuations in the blood oxygen level-dependent (BOLD) fMRI signal while avoiding the constraints of task-based approaches (Raichle et al., 2001; Fox and Raichle, 2007; Raichle and Snyder, 2007; Adelstein et al., 2011). There is accumulating evidence for local characteristics of resting brain functions associated with personality dimensions using resting-state fMRI (Kunisato et al., 2011; Wei et al., 2011, 2012; Hahn et al., 2012). Using regional homogeneity (ReHo) approach, our prior study found ReHo was correlated negatively with extraversion in the MPFC, and correlated positively in INS, cerebellum, and cingulate gyrus; whereas neuroticism had negative correlation with ReHo in left middle frontal gyrus (Wei et al., 2011). In addition, by using other local characteristics, i.e., the fractional amplitude of low-frequency fluctuations (fALFF), our previous study found positive correlations between LFF amplitude at Slow-5 and extraversion in MPFC and PCU, and between LFF amplitude at Slow-5 and neuroticism in right PreCG; LFF amplitude at Slow-4 was negatively associated with extraversion and neuroticism in left hippocampus (HIP) and bilateral superior temporal cortex (STC), respectively (Wei et al., 2012). **Table 1** summarizes the main results on the characteristics of resting brain functions associated with extraversion and neuroticism in recent resting-state fMRI studies.

From a functional integration perspective in the human brain, the multiple spatially distinct brain regions are functionally connected with coherent temporal dynamics (Friston et al., 1997; Sporns et al., 2000; Van Den Heuvel et al., 2009), making up complex and reciprocal brain networks even when we are at rest (Greicius et al., 2003; Damoiseaux et al., 2006; Van Den Heuvel et al., 2009). Such networks are thought to provide the physiological basis for information processing and mental representation (Canli, 2004; Bullmore and Sporns, 2009). Furthermore, evidence for small-world attributes of brain networks has been reported in the relative studies (Sporns et al., 2004; Stam, 2004; Eguiluz et al., 2005; Achard et al., 2006; Van Den Heuvel et al., 2009), indicating that small-world architectures in brain networks deviating from randomness reflect their specific functionality (Watts and Strogatz, 1998; Latora and Marchiori, 2001; Stam and Reijneveld, 2007; Bullmore and Sporns, 2009; Van Den Heuvel et al., 2009; He and Evans, 2010). Since personality factors may well be related to the networks in the brain (Canli, 2004; Wilt and Revelle, 2009), the analysis of task-independent, resting-state functional connectivity may reveal the intrinsically organized functional brain networks (Biswal et al., 1995), and allow for a better understanding of the neurobiological bases of extraversion and neuroticism. The recent study by Adelstein et al. (2011) found that extraversion and neuroticism were encoded within resting-state functional connectivity between seed regions and the lateral paralimbic regions and dorsomedial prefrontal cortex, respectively (Adelstein et al., 2011). However, the study was seedbased and lacked a network perspective on brain dynamics. In the present study, we hypothesized that the topological organization of the whole-brain functional networks would be associated with inter-individual variations in extraversion and neuroticism, and would link to Eysenck's cortical arousal theory of the two dimensions. To test our hypothesis, an exploratory analysis based on graph theory was thereby performed on the resting-state fMRI data of 71 healthy subjects, to detect the intrinsic resting-state


**Table 1 | The main results on the characteristics of resting brain functions associated with extraversion and neuroticism in recent resting-state fMRI studies.**

*ACC, anterior cingulate cortex; fALFF, fractional amplitude of low-frequency fluctuations; FC, functional connectivity; HIP, hippocampus; INS, insula; MCG, middle cingulate gyrus; MFG, middle frontal gyrus; MPFC, medial prefrontal cortex; MTG, middle temporal gyrus; PCU, precuneus; PreCG, precentral gyrus; ReHo, regional homogeneity; SFG, superior frontal gyrus; STC, superior temporal cortex.*

functional connectivity mechanisms underlying the two personality dimensions.

**MATERIALS AND METHODS**

#### **PARTICIPANTS**

We conducted the analysis with the same dataset in our previous study (Wei et al., 2011). Eighty-seven healthy right-handed subjects (48 males; age range: 17–36 yrs, mean age: 23.5 yrs) with no history of neurological or psychiatric disorders participated in the study. The present study was approved by the local Medical Ethics Committee at Jinling Hospital, Nanjing University School of Medicine, and the informed written consents were obtained from all participants.

## **PERSONALITY QUESTIONNAIRES**

The revised Eysenck personality questionnaire short scale for Chinese (EPQ-RSC) (Eysenck, 1991; Qian et al., 2000) was used to assess personality dimensions of extraversion, neuroticism, and psychoticism of each subject before MRI scanning. Raw scores of the three dimensions were then converted into *T*-scores using the formula (Qian et al., 2000), respectively:

$$T = 50 + 10 \times \frac{\text{raw score} - \text{mean}}{\text{SD}},$$

where *mean* represents the mean value of the personality scores over all the subjects; *SD* is the standard deviation of the personality scores. We focused our analyses on extraversion and neuroticism whose resultant *T*-scores were used for calculating correlations with the brain network metrics.

#### **IMAGE ACQUISITION**

Resting-state fMRI images were acquired using a single-shot, gradient-recalled echo planar imaging (EPI) sequence on a 3.0-T Siemens Trio scanner (Jinling Hospital, Nanjing, China). The acquisition parameters were: *TR* = 2000 ms, *TE* = 30 ms, field of view (FOV) = 240 mm, image matrix size = 64 × 64, voxel size = 3*.*75 × 3*.*75 × 4 mm3, 30 transverse slices without slice gap, flip angle = 90◦, and a total of 255 volumes for each subject.

#### **DATA PREPROCESSING**

Data preprocessing was performed using the Statistical Parametric Mapping software (SPM8, http://www*.*fil*.*ion*.* ucl*.*ac*.*uk/spm). The first five volumes were discarded to ensure steady-state longitudinal magnetization. The remaining restingstate fMRI images were first corrected by the acquisition time delay among different slices, and then realigned to the first volume for head-motion correction. The dataset with translational or rotational parameters exceeding ±1 mm or ±1◦ would be excluded, according to our previous study on functional connectivity network (Liao et al., 2010). The images of remaining 71 participants were further spatially normalized into a standard stereotaxic space at 3 × 3 × 3 mm3, using the Montreal Neurological Institute (MNI) template in SPM8. In order to avoid artificially introducing local spatial correlation, no spatial smoothing was applied, as previous studies suggested (Salvador et al., 2005; Achard et al., 2006; Achard and Bullmore, 2007; Liao

et al., 2010). Since recent studies have showed that functional connectivity analysis is sensitive to gross head motion effects (Power et al., 2012; Van Dijk et al., 2012), we further evaluated the framewise displacement (FD) (Power et al., 2012) to express instantaneous head motion, and the threshold of 0.5 was suggested. The *mean* ± *SD* of FD over subjects was: 0*.*1080 ± 0*.*0159. Six subjects' FD values were beyond 0.5, but only in one frame for each subject. Scrubbing process was performed using toolbox "ArtRepair" in SPM8.

The mean time series of each ROI was corrected by a linear regression to remove the possible spurious variances including six head motion parameters acquired from the SPM8 preprocessing, the white matter (WM) and the ventricular brain signals averaged from a WM mask and a ventricular mask respectively (Fox et al., 2005; Salvador et al., 2005; Tian et al., 2006; Liao et al., 2010). The residuals of these regressions were temporally band-pass filtered (0*.*01 *< f <* 0*.*08 Hz) to reduce low-frequency drifts and physiological high-frequency respiratory and cardiac noise (Biswal et al., 1995), and linearly detrended for further functional connectivity and graph-theory analysis (Tian et al., 2006; Liao et al., 2010). The following approaches based on graph theory were performed by an in-house program coded in MATLAB (The Mathworks, Natick, MA).

## **COMPUTATION OF FUNCTIONAL CONNECTIVITY NETWORK** *Node definition*

To define the brain nodes, the anatomical parcellation was performed using the automated anatomical labeling (AAL) template, segmenting the images into 90 anatomical regions of interests (ROIs) (45 ROIs for each hemisphere). The representative time series in each ROI was obtained by averaging the fMRI time series across all voxels in the ROI.

## *Edge definition*

To define the network edges, the residuals of the regression analysis were used to compute the Pearson's correlation, resulting in a 90 × 90 correlation matrix for each subject. A Fisher's *r*-to-*z* transformation was applied to the correlation matrices of all the subjects to improve the normality of the correlation coefficients (*r*) (Liu et al., 2008). The undirected edge *eij* between node *i* and node *j* is defined as:

$$e\_{ij} = \begin{cases} 1 & \text{when } |r\_{ij}| > \,\, T\\ 0 & \text{otherwise} \end{cases}$$

In general, if the absolute value of *rij* of a pair of brain regions, *i* and *j*, exceeds a predefined threshold *T*, an edge is assumed to exist; otherwise, no existence would be assumed (Liao et al., 2010).

## **GRAPH THEORETICAL ANALYSIS** *Network metrics*

The topological properties of the brain functional networks can be measured by both nodal and global network measures. In this study, we calculated the nodal measures including the degree *Ki*, the clustering coefficient *Ci*, the minimum path length *Li*, the efficiency *Ei*, and the betweenness centrality *BCi* of a node *i*; the global measures including the average degree *K*, the network efficiency involving the local efficiency *E*local and the global efficiency *E*global, the characteristic path length *L*, the clustering coefficient of a network *C*, the normalized clustering coefficient γ, the normalized characteristic path length λ, and the small-worldness σ.

*Degree.* The degree at each node, *Ki*, *i* = 1*,* 2*,...,* 90, is defined as the number of nodes in a subgraph *Gi*, which is the graph including the nodes that are direct neighbors of node *i*. Briefly, *Ki* denotes to which extent the node is connected to the rest of the network (Bullmore and Sporns, 2009; Wang et al., 2010). A node with a higher degree has more connections (where each connection is counted once). The average degree *K* is the mean of *Ki* of all the nodes in the network.

*Clustering coefficient.* The absolute clustering coefficient *Ci* of a node is the ratio between the number of existing connections and the number of all possible connections in the subgraph *Gi*. *Ci* quantifies the level of local connectedness within a network (Bullmore and Sporns, 2009; Van Den Heuvel et al., 2009; He and Evans, 2010)

$$C\_i = \frac{e\_i}{K\_i(K\_i - 1)/2},$$

where *ei* is the number of edges in the subgraph *Gi*. The clustering coefficient of the network *C* is the mean of *Ci* of all the nodes in the network.

*Minimum path length.* The nodal minimum path length *Li* is defined as the mean shortest absolute path length of node *i* to other nodes in a network (Bullmore and Sporns, 2009), which quantifies the level of routing efficiency or the capability for parallel information propagation of a network (Van Den Heuvel et al., 2009; He and Evans, 2010; Liao et al., 2010)

$$L\_i = \frac{1}{N - 1} \sum\_{i \neq j \in G} \min\{L\_{i,j}\},$$

where min {*Li,j*} is the shortest absolute path length between node *i* and node *j*, and the absolute path length is the number of edges included in the path connecting two nodes. The characteristic path length *L* is the mean of *Li* of all the nodes in the network.

*Efficiency.* The nodal efficiency *Ei* is the inverse of the harmonic mean of the length between node *i* and all other nodes in the network, to deal with the disconnected graphs, non-sparse graphs or both (Latora and Marchiori, 2001; Bassett and Bullmore, 2006; Wang et al., 2010)

$$E\_i = \frac{1}{N-1} \sum\_{\substack{j \in G \\ j \neq i}} \frac{1}{\min\{L\_{i,j}\}}.$$

The global efficiency *E*global of the network is the mean of *Ei* of all the nodes in the network.

In the subgraph*Gi*, we can calculate the local efficiency of node *i* as:

$$E\_{i\text{-local}} = \frac{1}{N\_{G\_i}(N\_{G\_i} - 1)} \sum\_{\substack{j, k \in G\_i \\ j \neq k}} \frac{1}{\min\{L\_j, k\}}.$$

The local efficiency *E*local of the network is then similarly defined as the mean of *Ei*\_local of all the nodes in the network (Rubinov and Sporns, 2010).

*Betweenness centrality.* The betweenness centrality *BCi* is defined as the fraction of all shortest paths in the network that pass through node *i* (Rubinov and Sporns, 2010). *BCi* describes the central nodes that participate in many short paths within a network, and consequently act as important controls of information flow (Freeman, 1978)

$$BC\_i = \frac{1}{(N-1)(N-2)} \sum\_{\substack{j,k \in G\\i \neq j \neq k}} \frac{\rho\_{j,k}(i)}{\rho\_{j,k}},$$

where ρ*j, <sup>k</sup>*is the number of shortest paths between node *j* and *k*; ρ*j, <sup>k</sup>(i)* is the number of shortest paths between *j* and *k* that pass through node *i* (Rubinov and Sporns, 2010).

*Small-world parameters.* Compared with random networks characterized by a low clustering coefficient and a typical short path length, networks with a small-world organization have a higher clustering coefficient and similar path length, i.e., γ = *C*/*C*random *>* 1, λ = *L*/*L*random ≈ 1, namely normalized clustering coefficient and normalized characteristic path length, respectively (Watts and Strogatz, 1998). These two conditions can also be summarized into a quantitative measurement, σ = γ/λ *>* 1, namely small-worldness (Humphries et al., 2006; Wang et al., 2010). *C*random and *L*random were calculated as the averaged clustering coefficient and characteristic path length of a set of 100 random networks with the same degree distribution as that of the examined functional connectivity network (Van Den Heuvel et al., 2009; Liao et al., 2010). The random networks were generated based on a Markov-chain algorithm, according to our previous study (Liao et al., 2010).

#### *Threshold selection*

The threshold *T* was defined as the total number of edges in a graph divided by the maximum possible number of edges (Achard and Bullmore, 2007), namely wiring cost. We investigated the topological properties of brain functional network over a range of *T*min ≤ *T* ≤ *T*max. (1) *T*min was selected by thresholding all networks to construct a sparse graph with the average degree *K* ≥ 2× log (*N*) (here *N* = 90 represents the number of nodes); (2) *T*max was selected to ensure the small-worldness σ of the thresholded networks be larger than 1.1 for all participants (Liao et al., 2010; Zhang et al., 2011). The resultant threshold range of 0*.*10 ≤ *T* ≤ 0*.*31 was used in our study. This range of sparsity allows the thresholded networks to be estimable for small-worldness and the functional connectivity mechanisms underlying the two personality dimensions.

**MATERIALS AND METHODS**

#### **PARTICIPANTS**

We conducted the analysis with the same dataset in our previous study (Wei et al., 2011). Eighty-seven healthy right-handed subjects (48 males; age range: 17–36 yrs, mean age: 23.5 yrs) with no history of neurological or psychiatric disorders participated in the study. The present study was approved by the local Medical Ethics Committee at Jinling Hospital, Nanjing University School of Medicine, and the informed written consents were obtained from all participants.

## **PERSONALITY QUESTIONNAIRES**

The revised Eysenck personality questionnaire short scale for Chinese (EPQ-RSC) (Eysenck, 1991; Qian et al., 2000) was used to assess personality dimensions of extraversion, neuroticism, and psychoticism of each subject before MRI scanning. Raw scores of the three dimensions were then converted into *T*-scores using the formula (Qian et al., 2000), respectively:

$$T = 50 + 10 \times \frac{\text{raw score} - \text{mean}}{\text{SD}},$$

where *mean* represents the mean value of the personality scores over all the subjects; *SD* is the standard deviation of the personality scores. We focused our analyses on extraversion and neuroticism whose resultant *T*-scores were used for calculating correlations with the brain network metrics.

#### **IMAGE ACQUISITION**

Resting-state fMRI images were acquired using a single-shot, gradient-recalled echo planar imaging (EPI) sequence on a 3.0-T Siemens Trio scanner (Jinling Hospital, Nanjing, China). The acquisition parameters were: *TR* = 2000 ms, *TE* = 30 ms, field of view (FOV) = 240 mm, image matrix size = 64 × 64, voxel size = 3*.*75 × 3*.*75 × 4 mm3, 30 transverse slices without slice gap, flip angle = 90◦, and a total of 255 volumes for each subject.

#### **DATA PREPROCESSING**

Data preprocessing was performed using the Statistical Parametric Mapping software (SPM8, http://www*.*fil*.*ion*.* ucl*.*ac*.*uk/spm). The first five volumes were discarded to ensure steady-state longitudinal magnetization. The remaining restingstate fMRI images were first corrected by the acquisition time delay among different slices, and then realigned to the first volume for head-motion correction. The dataset with translational or rotational parameters exceeding ±1 mm or ±1◦ would be excluded, according to our previous study on functional connectivity network (Liao et al., 2010). The images of remaining 71 participants were further spatially normalized into a standard stereotaxic space at 3 × 3 × 3 mm3, using the Montreal Neurological Institute (MNI) template in SPM8. In order to avoid artificially introducing local spatial correlation, no spatial smoothing was applied, as previous studies suggested (Salvador et al., 2005; Achard et al., 2006; Achard and Bullmore, 2007; Liao

et al., 2010). Since recent studies have showed that functional connectivity analysis is sensitive to gross head motion effects (Power et al., 2012; Van Dijk et al., 2012), we further evaluated the framewise displacement (FD) (Power et al., 2012) to express instantaneous head motion, and the threshold of 0.5 was suggested. The *mean* ± *SD* of FD over subjects was: 0*.*1080 ± 0*.*0159. Six subjects' FD values were beyond 0.5, but only in one frame for each subject. Scrubbing process was performed using toolbox "ArtRepair" in SPM8.

The mean time series of each ROI was corrected by a linear regression to remove the possible spurious variances including six head motion parameters acquired from the SPM8 preprocessing, the white matter (WM) and the ventricular brain signals averaged from a WM mask and a ventricular mask respectively (Fox et al., 2005; Salvador et al., 2005; Tian et al., 2006; Liao et al., 2010). The residuals of these regressions were temporally band-pass filtered (0*.*01 *< f <* 0*.*08 Hz) to reduce low-frequency drifts and physiological high-frequency respiratory and cardiac noise (Biswal et al., 1995), and linearly detrended for further functional connectivity and graph-theory analysis (Tian et al., 2006; Liao et al., 2010). The following approaches based on graph theory were performed by an in-house program coded in MATLAB (The Mathworks, Natick, MA).

## **COMPUTATION OF FUNCTIONAL CONNECTIVITY NETWORK** *Node definition*

To define the brain nodes, the anatomical parcellation was performed using the automated anatomical labeling (AAL) template, segmenting the images into 90 anatomical regions of interests (ROIs) (45 ROIs for each hemisphere). The representative time series in each ROI was obtained by averaging the fMRI time series across all voxels in the ROI.

## *Edge definition*

To define the network edges, the residuals of the regression analysis were used to compute the Pearson's correlation, resulting in a 90 × 90 correlation matrix for each subject. A Fisher's *r*-to-*z* transformation was applied to the correlation matrices of all the subjects to improve the normality of the correlation coefficients (*r*) (Liu et al., 2008). The undirected edge *eij* between node *i* and node *j* is defined as:

$$e\_{ij} = \begin{cases} 1 & \text{when } |r\_{ij}| > \,\, T\\ 0 & \text{otherwise} \end{cases}$$

In general, if the absolute value of *rij* of a pair of brain regions, *i* and *j*, exceeds a predefined threshold *T*, an edge is assumed to exist; otherwise, no existence would be assumed (Liao et al., 2010).

## **GRAPH THEORETICAL ANALYSIS** *Network metrics*

The topological properties of the brain functional networks can be measured by both nodal and global network measures. In this study, we calculated the nodal measures including the degree *Ki*, the clustering coefficient *Ci*, the minimum path length *Li*, the efficiency *Ei*, and the betweenness centrality *BCi* of a node *i*; the

## **THE ASSOCIATIONS BETWEEN NETWORK METRICS AND NEUROTICISM**

No global measures showed significant correlation with neuroticism. Significant correlations were revealed in the AUC of *BCi*, too. Neuroticism scores showed increased significant correlation with *BCi* in right PreCG, right olfactory cortex (OLF), right caudate nucleus (CAU), and bilateral AMYG. No significantly negative correlation was found. **Figure 4** indicates the brain regions showing significant correlations between *BCi* and neuroticism scores along with the corresponding correlation coefficients.

**Figure 5** depicts the topological characteristics of *BCi* as a function of wiring cost thresholds, in the brain regions whose *BCi*

**have significant associations with extraversion, as a function of wiring cost thresholds.** The asterisk indicates the threshold where the significant

correlation between the metric and extraversion was detected (permutation testing, *p <* 1*/*90). The inset figure indicates the correlation between the metric and extraversion at wiring cost = 0.22.

values have significant associations with neuroticism. The asterisk also indicates the threshold where the significant correlation between the metric and neuroticism was detected (permutation testing, *p <* 1*.*90). The inset figure indicates the correlation between the metric and neuroticism at wiring cost = 0.22.

#### **THE PREDICTION OF PERSONALITY SCORES BY LEAVE-ONE-OUT APPROACH**

**Figure 6** shows the predicted and original pairs of extraversion (**Figure 6A**) and neuroticism (**Figure 6B**) scores, respectively. The Pearson's correlation coefficients of the predicted and original personality scores were 0.536 (*p* = 0*.*146 × 10<sup>−</sup>7) for extraversion, and 0.547 (*p* = 0*.*784 × 10<sup>−</sup>8) for neuroticism. The precisions of individual prediction were 11.4% for extraversion and 21.7% for neuroticism.

#### **DISCUSSION**

#### **METHODOLOGICAL CONSIDERATIONS**

The present study differed from our previous studies in both hypothesis and analysis methods. In the previous studies, the purpose was to identify the associations between the personality dimensions and the local synchronization of spontaneous BOLD activity (Wei et al., 2011), or between the personality dimensions and the fLFF in individual brain regions (Wei et al., 2012). Thereby the analysis method as well as the results obtained was at the functional segregation level.

Since the multiple spatially distinct brain regions are functionally connected with coherent temporal dynamics, the topological properties of the brain functional networks may predict individual differences in the two fundamental personality dimensions. To test this hypothesis, in the present study, we applied the graph theory method to explore the correlation between the network metrics in the resting-state brain network and the personality dimensions of extraversion and neuroticism at the functional integration level. To the best of our knowledge, the present study is among the first demonstrations of an association between personality dimensions and the properties of the resting-state functional network.

#### **EXTRAVERSION AND THE NETWORK METRICS**

The present results showed that compared to individuals with lower extraversion scores, individuals with higher extraversion

**have significant associations with extraversion, as a function of wiring cost thresholds.** The asterisk indicates the threshold where the significant

correlation between the metric and extraversion was detected (permutation testing, *p <* 1*/*90). The inset figure indicates the correlation between the metric and extraversion at wiring cost = 0.22.

values have significant associations with neuroticism. The asterisk also indicates the threshold where the significant correlation between the metric and neuroticism was detected (permutation testing, *p <* 1*.*90). The inset figure indicates the correlation between the metric and neuroticism at wiring cost = 0.22.

#### **THE PREDICTION OF PERSONALITY SCORES BY LEAVE-ONE-OUT APPROACH**

**Figure 6** shows the predicted and original pairs of extraversion (**Figure 6A**) and neuroticism (**Figure 6B**) scores, respectively. The Pearson's correlation coefficients of the predicted and original personality scores were 0.536 (*p* = 0*.*146 × 10<sup>−</sup>7) for extraversion, and 0.547 (*p* = 0*.*784 × 10<sup>−</sup>8) for neuroticism. The precisions of individual prediction were 11.4% for extraversion and 21.7% for neuroticism.

#### **DISCUSSION**

#### **METHODOLOGICAL CONSIDERATIONS**

The present study differed from our previous studies in both hypothesis and analysis methods. In the previous studies, the purpose was to identify the associations between the personality dimensions and the local synchronization of spontaneous BOLD activity (Wei et al., 2011), or between the personality dimensions and the fLFF in individual brain regions (Wei et al., 2012). Thereby the analysis method as well as the results obtained was at the functional segregation level.

Since the multiple spatially distinct brain regions are functionally connected with coherent temporal dynamics, the topological properties of the brain functional networks may predict individual differences in the two fundamental personality dimensions. To test this hypothesis, in the present study, we applied the graph theory method to explore the correlation between the network metrics in the resting-state brain network and the personality dimensions of extraversion and neuroticism at the functional integration level. To the best of our knowledge, the present study is among the first demonstrations of an association between personality dimensions and the properties of the resting-state functional network.

#### **EXTRAVERSION AND THE NETWORK METRICS**

The present results showed that compared to individuals with lower extraversion scores, individuals with higher extraversion

## **THE ASSOCIATIONS BETWEEN NETWORK METRICS AND NEUROTICISM**

No global measures showed significant correlation with neuroticism. Significant correlations were revealed in the AUC of *BCi*, too. Neuroticism scores showed increased significant correlation with *BCi* in right PreCG, right olfactory cortex (OLF), right caudate nucleus (CAU), and bilateral AMYG. No significantly negative correlation was found. **Figure 4** indicates the brain regions showing significant correlations between *BCi* and neuroticism scores along with the corresponding correlation coefficients.

**Figure 5** depicts the topological characteristics of *BCi* as a function of wiring cost thresholds, in the brain regions whose *BCi* number of spurious edges to be minimized (Watts and Strogatz, 1998; Achard and Bullmore, 2007; He et al., 2008; Zhang et al., 2011).

## **ASSOCIATION BETWEEN NETWORK ORGANIZATION AND PERSONALITY DIMENSIONS**

All the nodal and global measures were thresholded repeatedly over the range of 0*.*1 ≤ *T* ≤ 0*.*31 with an interval of 0.01, and the area under the curve (AUC) for each network metric was calculated, which provides a summarized scalar for topological characterization of brain networks independent of single threshold selection (Zhang et al., 2011). The partial correlation was then calculated between the AUC of each network metric and extraversion/neuroticism scores, with age and gender being covariates.

To assess the statistical significance of the correlation, the null distribution for each network metric was obtained by nonparametric permutation tests. Accordingly, 5000 subject specific random networks were generated at each threshold as null-model reference networks. The correlations between the AUC of each network metric and the personality scores were recalculated to obtain the null distribution. 1/number of regions was used as a false-positive correction, which implied that there was less than one false positive regional result per cortical map at this threshold (Lynall et al., 2010; Fornito et al., 2011).

#### **LEAVE-ONE-OUT PREDICTION**

To test the validity of the significantly correlated measures in predicting personality scores of extraversion and neuroticism, a leave-one-out cross-validation strategy was applied. The significantly correlated measures acted as explanatory variables in the linear regression models to predict the personality scores. The predicted results of all the subjects were assessed by calculating the Pearson's correlation between the predicted values and the original values. The precision of individual prediction was assessed by the average of the absolute relative errors between the predicted and original scores.

## **RESULTS**

## **DESCRIPTIVE STATISTICS OF THE PERSONALITY DIMENSIONS**

**Table 2** describes the scores of the three personality dimensions from the EPQ-RSC questionnaire, and **Table 3** describes the correlations across the scores of the three dimensions. As two dimensions concerned in the present study, extraversion had

**Table 2 | Descriptive Statistics of the three personality dimensions of 71 participants.**


*Age and personality scores are displayed as mean* ± *SD.*

a moderate negative correlation with neuroticism (*r* = −0*.*238, *p* = 0*.*046). The result was concordant with many prior studies, suggesting an inverse relationship between extraversion and neuroticism (Rusting and Larsen, 1997; Wright et al., 2006; Kim et al., 2008). Therefore, we added extraversion (or neuroticism) scores as covariate when calculating the partial correlation between neuroticism (or extraversion) and the AUC of each network metric, to obtain effects that were uniquely driven by each personality dimension.

## **THE ASSOCIATIONS BETWEEN NETWORK METRICS AND EXTRAVERSION**

Among all the global measures of the network calculated in the present study, only the AUC of normalized clustering coefficient γ showed significant correlation with extraversion (**Figure 1**). As for the nodal measures, results indicated that only the AUC of *BCi* showed significant correlations with extraversion. Extraversion significantly increased with *BCi* in left INS, while significantly decreased with *BCi* in bilateral MTG. **Figure 2** demonstrates the brain regions showing significant correlations between their *BCi* and extraversion scores along with the corresponding correlation coefficients.

**Figure 3** depicts the topological characteristics of network metrics which have significant associations with extraversion, as a function of wiring cost thresholds. The asterisk indicates the threshold where the significant correlation between the metric and extraversion was detected (permutation testing, *p <* 1*.*90). The inset figure indicates the correlation between the metric and extraversion at wiring cost = 0.22.

#### **Table 3 | Correlations between scores of the three personality dimensions.**


*E, extraversion; N, neuroticism; P, psychoticism. \*p < 0.05.*

specific brain regions in the PreCG and limbic system, providing some supporting evidence for Eysenck's biological theory of neuroticism. Furthermore, the right lateralization of these regions with regard to neuroticism gave neurofunctional evidence to the preferential involvement of brain's right hemisphere in emotions and motivational states associated with withdrawal aspect of neuroticism.

## **REFERENCES**


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## **ACKNOWLEDGMENTS**

This work was supported by grants from the Natural Science Foundation of China (61035006, 91132721, and 61125304); Postdoctoral Science Foundation of China (20100481378) and Special Postdoctoral Science Foundation of China (2012T50772). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 29 November 2012; accepted: 22 May 2013; published online: 11 June 2013.*

*Citation: Gao Q, Xu Q, Duan X, Liao W, Ding J, Zhang Z, Li Y, Lu G and Chen H (2013) Extraversion and neuroticism relate to topological properties of resting-state brain networks. Front. Hum. Neurosci. 7:257. doi: 10.3389/ fnhum.2013.00257*

*Copyright © 2013 Gao, Xu, Duan, Liao, Ding, Zhang, Li, Lu and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Erratum: Extraversion and neuroticism relate to topological properties of resting-state brain networks

## *Qing Gao\**

*School of Mathematical Sciences, University of Electronic Science and Technology of China, Cheng du, China \*Correspondence: qingqing.gao@gmail.com*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### **A commentary on**

## **Extraversion and neuroticism relate to topological properties of resting-state brain networks**

*by Gao, Q., Xu, Q., Duan, X., Liao, W., Ding, J., Zhang, Z., et al. (2013). Front. Hum. Neurosci. 7:257. doi: 10.3389/fnhum. 2013.00257*

In the above paper, two mistakes were discovered after publication. The corrections are as following:


*Received: 11 July 2013; accepted: 20 July 2013; published online: 06 August 2013.*

*Citation: Gao Q (2013) Erratum: Extraversion and neuroticism relate to topological properties of restingstate brain networks. Front. Hum. Neurosci. 7:448. doi: 10.3389/fnhum.2013.00448*

*Copyright © 2013 Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Differences of inter-tract correlations between neonates and children around puberty: a study based on microstructural measurements with DTI

## *Virendra Mishra1, Hua Cheng2,3, Gaolang Gong4, Yong He4, Qi Dong4 and Hao Huang1,5\**

*<sup>1</sup> Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA*

*<sup>2</sup> Department of Radiology, Beijing Children's Hospital, Beijing, China*

*<sup>3</sup> Department of Radiology, Capital Medical University, Beijing, China*

*<sup>4</sup> State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China*

*<sup>5</sup> Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA*

#### *Edited by:*

*Alan Evans, McGill University, Canada*

#### *Reviewed by:*

*Pratik Mukherjee, University of California, USA Xiaobo Li, Albert Einstein College of Medicine, USA*

#### *\*Correspondence:*

*Hao Huang, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd., Dallas, TX 75390-8542, USA e-mail: hao.huang@ utsouthwestern.edu*

The human brain development is a complicated yet well-organized process. Metrics derived from diffusion tensor imaging (DTI), including fractional anisotropy (FA), radial (RD), axial (AxD), and mean diffusivity (MD), have been used to noninvasively access the microstructural development of human brain white matter (WM). At birth, most of the major WM tracts are apparent but in a relatively disorganized pattern. Brain maturation is a process of establishing an organized pattern of these major WM tracts. However, how the linkage pattern of major WM tracts changes during development remains unclear. In this study, DTI data of 26 neonates and 28 children around puberty were acquired. 10 major WM tracts, representing four major tract groups involved in distinctive brain functions, were traced with DTI tractography for all 54 subjects. With the 10 by 10 correlation matrices constructed with Spearman's pairwise inter-tract correlations and based on tract-level measurements of FA, RD, AxD, and MD of both age groups, we assessed if the inter-tract correlations become stronger from birth to puberty. In addition, hierarchical clustering was performed based on the pairwise correlations of WM tracts to reveal the clustering pattern for each age group and pattern shift from birth to puberty. Stronger and enhanced microstructural inter-tract correlations were found during development from birth to puberty. The linkage patterns of two age groups differ due to brain development. These changes of microstructural correlations from birth to puberty suggest inhomogeneous but organized myelination processes which cause the reshuffled inter-tract correlation pattern and make homologous tracts tightly clustered. It opens a new window to study WM tract development and can be potentially used to investigate atypical brain development due to neurological or psychiatric disorders.

**Keywords: brain development, neonate, DTI, microstructure, inter-tract correlation, homologous**

## **INTRODUCTION**

The human brain is complicated yet well organized. The major cerebral white matter (WM) tracts connecting different brain regions are involved in different brain functions. These major cerebral WM tracts are often categorized into different tract groups based on their distinct functions. There are roughly four tract groups, namely limbic, projection, callosal, and association tract groups (e.g., Wakana et al., 2004; Huang et al., 2012a,b), for cerebral WM tracts. The WM tracts within a tract group perform similar functions. For example, limbic tracts underlie the connectivity in the limbic system and association tracts connect between cerebral cortical areas. The pair of tracts in both cerebral hemispheres belongs to the same tract group and is considered as homologous tracts. At birth, most of major WM tracts are well formed (e.g., Huang et al., 2006; Oishi et al., 2011), except the arcuate fasciculus which is a part of the superior longitudinal fasciculus (SLF) and related to language function.

The water molecules all over the human brain tend to diffuse more freely along the WM fiber bundle, instead of perpendicular to it. This diffusion property can be measured noninvasively with diffusion MRI (dMRI), a modality of MRI. The widely used diffusion tensor imaging (DTI) (Basser et al., 1994) characterizes the water diffusion properties in the brain voxels with a tensor model. Fractional anisotropy (FA) (Pierpaoli and Basser, 1996; Beaulieu, 2002) and mean diffusivity (MD), derived from DTI, have been widely used to quantify the microstructural properties of the WM voxels. Other than FA or MD, the other two DTI-derived metrics, radial diffusivity (RD) and axial diffusivity (AxD), convey unique information related to myelination and axonal integrity, respectively (Song et al., 2002). The four DTI-derived metrics, FA, RD, AxD, and MD, characterize different aspects of diffusion tensor and are highly sensitive to WM microstructural changes.

Compared to voxel-based morphometry (VBM), recent tract analyses (Yushkevich et al., 2008; Goodlett et al., 2009; O'Donnell et al., 2009; Zhang et al., 2010; Colby et al., 2012) including ours (Huang et al., 2011, 2012a,b) have become important due to great functional and clinical significance of the tracts. These WM tracts can be noninvasively traced with tractography based on diffusion MRI (dMRI) (e.g., Conturo et al., 1999; Jones et al., 1999a; Mori et al., 1999; Basser et al., 2000; Stieltjes et al., 2001; Catani et al., 2002; Parker et al., 2002; Lazar et al., 2003; Behrens et al., 2007). And the heterogeneous WM tracts can be noninvasively segmented with the traced fibers. With these segmented WM tracts as binary masks for the maps of DTI-derived metrics, the microstructural properties of the WM tracts can be quantified.

Dramatic microstructural changes take place during normal human brain development from birth to puberty, which are two landmark time points in early brain development. Birth marks the beginning time point of postnatal brain development. Puberty marks the end of the child development and beginning of adolescence. The four DTI derived metrics, FA, RD, AxD and MD, have been incorporated in numerous studies investigating WM microstructural changes for infants, children and adolescents during development. The human brain development process is usually characterized with significant increases in FA (e.g., Barnea-Goraly et al., 2005; Snook et al., 2005; Eluvathingal et al., 2007; Dubois et al., 2008; Lebel et al., 2008; Gao et al., 2009; Giorgio et al., 2010; Schmithorst and Yuan, 2010; Tamnes et al., 2010; Westlye et al., 2010) and significant decreases in MD, AxD and RD (e.g., Snook et al., 2005; Eluvathingal et al., 2007; Dubois et al., 2008; Lebel et al., 2008; Gao et al., 2009; Giorgio et al., 2010; Schmithorst and Yuan, 2010; Tamnes et al., 2010; Westlye et al., 2010). On the other hand, it was shown with DTI-derived metrics of a cohort of adults that specific WM tracts involved in similar functions vary in a similar pattern with each other across different individuals (Wahl et al., 2010; Li et al., 2012), while hemispheric asymmetries of DTI-derived metrics in homologous pairs of WM tracts (Bonekamp et al., 2007; Wilde et al., 2009) have been reported. However, from perspective of brain development, whether or not the significant inter-tract correlations exist at birth and around puberty is still unclear. Furthermore, it remains elusive if these inter-tract correlations will be strengthened and how the correlation patterns change from birth to puberty.

During the development from birth to puberty, the human brain is likely to change from a more randomized state to a more balanced and organized state. In this study, we hypothesized that inter-tract correlations become stronger and the correlation patterns are reshuffled from birth to puberty. Specifically, the reshuffling process will cause more homologous tracts to form tight relationship. DTI data were acquired from 26 normal neonates and 28 normal children around puberty. The following 10 major WM tracts covering limbic, association, commissural and projection tract groups were selected for tract-level measurements of DTI metrics of each subject: left and right corticospinal tract (CST\_L and CST\_R), left and right inferior fronto-occipital fasciculus (IFO\_L and IFO\_R), left and right cingulate part of cingulum tract (CGC\_L and CGC\_R), left and right hippocampal part of cingulum tract (CGH\_L and CGH\_R), forceps major (FMajor) and forceps minor (FMinor). The tract level comparisons of all four DTI-derived metrics were conducted between the two age groups. Spearman's pairwise inter-tract correlations were performed. We tested if significant correlations of homologous WM tracts exist in neonates and children around puberty. After obtaining four 10 by 10 inter-tract correlation matrices corresponding to four DTI-derived metrics, FA, RD, AxD, and MD, for each age group, we tested these correlation matrices against the identity matrix or a matrix with equal non-diagonal entries. We then assessed if the inter-tract correlations become statistically stronger from birth to puberty. In addition, hierarchical clustering was performed with the pairwise correlations based on FA, RD, AxD, and MD measurements for each age group to reveal the pattern of clustering in either age group and reveal the pattern shift from birth to puberty.

## **MATERIALS AND METHODS**

## **SUBJECTS AND DATA ACQUISITION**

Twenty six normal neonates (14 males; age: 37 to 43 gestational weeks with mean and standard deviation 40*.*1 ± 2*.*0 gestational weeks) and 28 normal children around puberty (15 males; age: 9.5–15 years with mean and standard deviation 12*.*0 ± 2*.*3 years), free of current and past neurological or psychiatric disorders, were recruited at Children's Medical Center (CMC) at Dallas and Advanced Imaging Research Center (AIRC) of the University of Texas Southwestern Medical Center (UTSW), respectively. The parents of all the subjects gave written informed consents approved by Institutional Review Board of UTSW.

Two 3T Philips Achieva MR systems at CMC and AIRC were used to acquire dMRI of neonate and child group, respectively. dMRI data were acquired using a single shot echo planar imaging (EPI) with SENSE parallel imaging scheme (SENsitivity Encoding, reduction factor = 2.3). dMRI parameters for neonates were: *FOV* = 200/200/100 mm, in-plane imaging matrix = 100 × 100, axial slice thickness = 2 mm. dMRI parameters for children around puberty were: *FOV* = 224/224/143 mm, in-plane imaging matrix = 112 × 112, axial slice thickness = 2*.*2 mm. The common parameters for dMRI acquisition of both neonate and child group were: *b*-value = 1000 s/mm2, *TE* = 97 ms, *TR* = 7.6 s, 30 independent diffusion-weighted directions (Jones et al., 1999b) and 2 repetitions to increase signal-to-noise ratio (SNR).

## **DTI PREPROCESSING**

dMRI acquired from all the subjects was processed offline using DTIStudio (mristudio.org; Jiang et al., 2006). dMRI images for each subject were corrected for motion and eddy current by registering all the diffusion weighted images to the b0 image using a 12-parameter (affine) linear image registration with automated image registration (AIR) algorithm (Woods et al., 1998). After the registration, six independent elements of the 3 × 3 diffusion tensor (Basser et al., 1994) were determined by multivariate least-square fitting of diffusion weighted images. The tensor was diagonalized to obtain three eigenvalues (λ<sup>1</sup> <sup>−</sup> 3) and eigenvectors (ν<sup>1</sup> <sup>−</sup> 3). FA, MD, AxD and RD, derived from DTI, were obtained for all the subjects with the following equations of eigenvalues:

$$\begin{aligned} FA &= \frac{\sqrt{(\lambda\_1 - \lambda\_2)^2 + (\lambda\_1 - \lambda\_3)^2 + (\lambda\_2 - \lambda\_3)^2}}{\sqrt{2}\sqrt{\lambda\_1^2 + \lambda\_2^2 + \lambda\_3^2}} \\ MD &= (\lambda\_1 + \lambda\_2 + \lambda\_3)/3 \\ AxD &= \lambda\_1 \\ RD &= (\lambda\_2 + \lambda\_3)/2 \end{aligned}$$

## **TRACT-LEVEL MEASUREMENTS OF DTI METRICS**

The following 10 major WM tracts were selected for tract-level measurements of DTI metrics, left and right corticospinal tract (CST\_L and CST\_R), left and right inferior fronto-occipital fasciculus (IFO\_L and IFO\_R), left and right cingulate part of cingulum tract (CGC\_L and CGC\_R), left and right hippocampal part of cingulum tract (CGH\_L and CGH\_R), forceps major (FMajor) and forceps minor (FMinor). These tracts could be reproducibly traced with DTI of all neonates and children and cover all four major tract groups, namely projection, limbic, commissural and association tract group. Other major WM tracts such as SLF could not be traced reproducibly with our cohort of neonate DTI dataset. Following the literature (Wakana et al., 2007), the tractography protocol described in details below was used to trace all these tracts. DTIstudio (mristudio.org) was used to conduct the tractography. The binary masks of the individually traced tracts were used to compute the tract-level FA, RD, AxD, and MD. The test-retest reliability was quantified by coefficient of variation (CV) and κ values of variability shown in Supplemental Table 1, after tracing the tracts below 3 times with the data from 3 subjects randomly selected from each group. All CV values are less than 2% and κ values are greater than 95% for both neonate an child group, indicating almost perfect test-retest reliability and almost perfect agreement of measurements among different tests in both neonate and child group.

## *CST-L and CST-R*

For the first ROI, the entire cerebral peduncle of the desired hemisphere was delineated at the level of the decussation of the superior cerebellar peduncle using an axial slice. "OR" operation was used to select all the CST fibers in this hemisphere that reach the primary motor cortex. The second ROI was then drawn at the most ventral axial slice that identifies the cleavage of the central sulcus. "AND" operation is performed at this axial slice to select all the CST fibers in this hemisphere. The fibers running through to the opposite hemisphere were removed using the "NOT" operation.

#### *CGC-L and CGC-R*

For the first ROI, a coronal plane was selected at the middle of the splenium of the corpus callosum (CC) using the mid-sagittal plane and the region containing the entire cingulum in the desired hemisphere is selected. All the fibers in this coronal plane passing through the cingulum were selected using the "OR" operation. The second ROI was drawn by selecting a coronal plane in the middle of the genu of the CC and all the CGC fibers were selected using the "AND" operation.

## *CGH-L and CGH-R*

For the first ROI, a coronal plane in the middle of the splenium of the CC was selected using the mid-sagittal plane and the cingulum below the CC of the desired hemisphere was delineated. All the fibers in this coronal plane passing through the cingulum were selected using the "OR" operation. The second ROI was drawn at a coronal slice anterior to the pons using the mid-sagittal plane and the fibers passing through the cingulum in this hemisphere were selected using the "AND" operation.

## *IFO-L and IFO-R*

For the first ROI, a coronal slice at the middle point between the posterior edge of the cingulum and the posterior edge of the parieto-occipital sulcus was selected and the entire occipital lobe of the desired hemisphere was delineated. All the fibers in this hemisphere were selected using the "OR" operation. The second ROI was drawn at the anterior edge of the CC using a coronal slice and all the fibers in this hemisphere were selected using the "AND" operation. The fibers running through to the thalamus were removed by using the "NOT" operation.

## *FMajor*

For the first ROI, a coronal plane including only the left occipital lobe was selected at the most posterior edge of the parietooccipital sulcus. The "OR" operation in this coronal plane delineated all the fibers of FMajor. The second ROI was drawn in the same coronal plane on the right hemisphere using the "AND" operation such that all the fibers in the right occipital lobe was selected.

#### *FMinor*

For the first ROI, a coronal plane at the middle point between the anterior tip of the frontal lobe and the anterior edge of the genu of the CC was selected using the mid-sagittal plane. The "OR" operation was used to select all the fibers in the entire left hemisphere. The second ROI was drawn in the same coronal plane on the right hemisphere and all the fibers in the right hemisphere were selected using the "AND" operation.

#### **INTER-TRACT CORRELATION ANALYSIS**

Shapiro-Wilk normality test was performed with DTI-derived metrics of all the 10 WM tracts of 26 neonate brains and 28 child brains. Distributions of DTI-derived metrics for most of the tracts in the neonate group did not show significant difference (*p >* 0*.*05) from normality. However, distributions of DTI-derived metrics of most of the tracts in the child group differed significantly (*p <* 0*.*05) from normality. Hence, following the method in the literature (Wahl et al., 2010), non-parametric Spearman's rank correlation coefficient ρ was used to measure all correlations. Subsequently, a correlation matrix was constructed for each of the 4 DTI-derived metrics using pairwise correlation values between any two tracts. Symmetric correlation matrices were obtained with a value of unity along the diagonal of the correlation matrix. The diagonal element represents perfect correlation of the DTI-derived parameter of the tract with itself and the offdiagonal element represents the correlation of the DTI derived parameter of one tract with that of another tract. There were 10∗*(*10 − 1*)/*2 = 45 nontrivial independent correlation values in each correlation matrix.

#### **STATISTICAL ANALYSIS**

Two independent tests of correlation matrices were performed to evaluate if the correlation matrices were significantly different from identity and homogeneous matrix (Rencher, 2002; Wahl et al., 2010). The null hypothesis to test for identity was the

correlation matrix is an identity matrix, *-*<sup>0</sup> = ⎛ ⎜ ⎝ 100 *. . . ... . . .* 0 ··· 1 ⎞ ⎟ ⎠ . The

size of the correlation matrix was 10 × 10 as we were testing for pairwise correlations of 10 independent tracts. To test the correlation matrices for homogeneity, the null hypothesis was that the correlation matrix was homogeneous and that the non-diagonal

elements of the matrix were equal, i.e.,: μ<sup>0</sup> = ⎛ ⎜ ⎝ 1 ρ ρ *. . . ... . . .* ρ ··· 1 ⎞ ⎟ ⎠ . This

null hypothesized homogeneous correlation matrix was derived by following the procedure outlined in the literature (Rencher, 2002; Wahl et al., 2010). Bonferroni correction was conducted for both test of correlation matrix against identify matrix and test of correlation matrix against matrix with equal non-diagonal elements. Once the DTI-derived correlation matrices were found to be significantly different from identity and homogeneity within each group, correlation matrices of each DTI-derived metric were compared between the two age groups. Spearman's rank correlation coefficients were converted to *z* values by using Fisher's r-to-z-transform (Fisher, 1915). Note that *r* in Fisher's r-to-ztransform is the spearman's rank correlation coefficient ρ in this study. *Z*-statistics was then performed to identify the pair of tracts that showed significant change in correlation strength from birth to puberty.

## **HIERARCHICAL CLUSTERING ANALYSIS**

Hierarchical clustering methods were used to characterize the patterns of inter-tract correlation in each matrix among the groups. We used 1-ρ, where ρ is the Spearman's rank correlation coefficient, as a measure of distance or dissimilarity between the WM tracts for the purpose of clustering. Hierarchical clustering was performed using hclust function in R version 2.15.2. Depending on the correlation coefficient, different WM tracts were successively grouped into larger groups and the results were visualized as a dendrogram. WM tracts that had stronger correlation among themselves were fused together and were linked together. To characterize the uncertainty of the linkage among WM tracts and reduce the standard error in the percentage of confidence level for each cluster, a multi-scale bootstrap with 1000 repetitions of the analysis was performed using pvclust function in R (Suzuki and Shimodaira, 2006). The multi-scale bootstrap analysis yields an approximately unbiased *p*-value of each linkage in hierarchical clustering and has been applied in various other studies (Shimodaira, 2002, 2004; Wahl et al., 2010). The threshold for determining statistical significance for the grouping of tracts was set at an unbiased *p*-value of 0.05 or 95% confidence interval.

### **RESULTS**

## **CHANGES OF WHITE MATTER MICROSTRUCTURE FROM NEONATES TO CHILDREN AROUND PUBERTY**

**Figure 1** shows the three-dimensional (3D) visualization of the traced 10 major WM tracts for a typical neonate and a typical child around puberty. These 10 major tracts, namely CST\_L, CST\_R, IFO\_L, IFO\_R, CGC\_L, CGC\_R, CGH\_L, CGH\_R, FMajor and FMinor, cover projection, limbic, association and commissural tract groups involved in distinct brain functions. The microstructural changes from neonates to children at puberty and measured with FA, MD, AxD, and RD are shown in **Figure 2**. For all 10 major WM tracts, FA values are higher in the child group than those of the neonate group while MD, AxD, and RD values of the child group are less. FA of CST\_L and CST\_R of both age groups are highest among all tracts, followed by FMajor and FMinor. RD of CST\_L and CST\_R of both age groups are

**FIGURE 1 | 3-D visualization of the traced WM tracts overlaid on mid-sagittal slice of the FA image of a typical neonate (A) and a typical child around puberty (B).** Different colors represent different tracts traced for both subjects. CGC\_L/R, CGH\_L/R, CST\_L/R, FMajor/FMinor and IFO\_L/R are painted by red, orange, green, blue, and yellow color, respectively.

lowest among all tracts, indicating better myelination of CST compared to all other tracts. MD and AxD of FMajor and FMinor are highest among all tracts for child group. The values of these DTI-derived metrics of all tracts for the two age groups are shown in Supplemental Table 2.

## **ENHANCED INTER-TRACT CORRELATION FROM NEONATES TO CHILDREN AROUND PUBERTY**

**Figure 3** shows the scatterplot of the FA (**Figure 3A**), RD (**Figure 3B**), AxD (**Figure 3C**), and MD (**Figure 3D**) values for 2 pairs of homologous tracts and 2 pairs of non-homologous tracts, CGC\_L vs. CGC\_R, CST\_L vs. CST\_R, FMajor vs. FMinor, and CST-R vs. CGC-R. The 2 homologous tracts and FMajor/FMinor represent tract pairs of the three tract groups. Significant correlations (*p <* 0*.*05) can be observed for these 4 pairs of tracts in both age groups.

Significant differences (*p <* 0*.*05, Bonferroni-corrected) were observed for tests of all inter-tract correlation matrices of both groups against identity matrix or matrix with equal non-diagonal entries. The inter-tract correlation matrices of neonate and child group and the differences of these correlation matrices for FA, RD, AxD and MD are shown in **Figures 4A–D**, respectively. General stronger inter-tract correlations can be observed in the child group compared to those in the neonate group for all DTI-derived metrics, represented by the warmer colors in correlation matrices of child group. The statistics with *z*-scores (right panel of **Figure 4**) show that 64.4% (29/45), 84.4% (38/45), 73.3% (33/45), and 73.3% (33/45) of independent entries in the correlation matrix of child group are significantly higher than the corresponding entries of neonate group for FA, RD, AxD and MD, respectively. The correlation matrix from RD shows highest percentage changes (84.4%) among correlation matrices from all DTI-derived metrics, indicating more widespread enhanced inter-tract correlations with RD measurements. Note that the denominator 45 above indicates the number of all independent entries in the correlation matrix. The absolute values of correlation coefficients for AxD and MD, represented by the warmer colors in **Figures 4C,D**, are higher in both neonate and child group than those for FA (**Figure 4A**) or RD (**Figure 4B**). Much smaller percentages of independent entries of the correlation matrices are associated with the situation where correlation coefficients are significantly higher in the neonates than the children. Specifically, these percentages are 8.9% (4/45, namely CGC\_R vs. FMinor, CGC\_R vs. FMajor, CGH\_L vs. IFO\_L and IFO\_L vs. IFO\_R), 6.7% (3/45, namely CGC\_R vs. FMinor, CGC\_R vs. FMajor and IFO\_L vs. FMajor), 4.4% (2/45, namely CGC\_R vs. FMinor and CGC\_R vs. FMajor) and 4.4% (2/45, namely CGC\_R vs. IFO\_R and CGC\_R vs. FMajor) for FA, RD, AxD and MD, respectively. The inter-tract correlation coefficient values based on FA, RD, AxD and MD measurements for both age groups are shown in Supplemental Table 3.

**from 2 homologous and 2 non-homologous tracts.** The 2 homologous pairs and FMajor/FMinor represent tract pairs of three tract groups for both age groups. Top panel shows the inter-tract scatter plots for the neonates and plots represents the data from an individual subject in that group. ρ is the Spearman's rank correlation coefficient of the tract pair while *p*-value shows the statistical confidence of the inter-tract correlation strength.

**from tract-level FA (A), RD (B), AxD (C) and MD (D) measurements of both age groups.** The left and middle panels show the inter-tract correlation matrices for neonates and children around puberty, respectively. The right panel shows the *z*-scores of the changes in correlation strength between the two age groups. In the *z*-score plots, the entries with significant (*p <* 0*.*05) changes in inter-tract correlation strengths are shown in red color while entries with non-significant (*p >* 0*.*05) change in inter-tract correlation strengths are shown in green color. Color bar encoding the correlation strengths in the left and middle columns is also shown.

### **RESHUFFLED INTER-TRACT CORRELATION PATTERNS FROM NEONATES TO CHILDREN AROUND PUBERTY**

The inter-tract correlations based on each of DTI-derived metrics are reshuffled from neonates to children at puberty. These reshuffled inter-tract correlation patterns can be appreciated from dendrograms based on FA, RD, AxD, and MD measurements in **Figures 5A–D**, respectively. In general, the pairing of homologous tracts is more prominent for child group compared to that of the neonate group based on measurements of all DTI-derived metrics. The reshuffling leading to a more organized pairing among WM tracts is most prominent with the dendrograms based on tract-level RD measurements (**Figure 5B**). For inter-tract correlation based on FA measurement (**Figure 5A**), the tract pairs of CST\_L/R becomes clear in the dendrogram of child group, while this pair is not as apparent in neonate group. The IFO\_L/R pair is prominent for dendrograms of both neonate and child group based on FA measurements (**Figure 5A**). For dendrograms based on RD measurements (**Figure 5B**), it is clear that all homologous tracts and FMajor/FMinor get paired for child group while the correlation patterns are more random for neonate group. For dendrograms based on AxD (**Figure 5C**) or MD (**Figure 5D**) measurements, stronger and more clusters of the homologous tracts can be observed. The clustering pattern obtained from MD (**Figure 5D**) is similar to that obtained from RD (**Figure 5B**). However, the homologous tracts of child group are not well paired in MD-based dendrogram (**Figure 5D**), compared to those in RD-based dendrogram (**Figure 5B**).

The ranks of the clustering are shown in bold. The confidence intervals for the clustering (the percentage values) are shown in italics.

#### **DISCUSSION**

In this study, dynamics of inter-tract correlations from birth to onset of adolescence was investigated with DTI-based tract-level microstructural measurements from 10 major WM tracts, CST\_L, CST\_R, IFO\_L, IFO\_R, CGC\_L, CGC\_R, CGH\_L, CGH\_R, FMajor, and FMinor. Higher WM tract integrity, reflected by higher FA, lower MD, AxD, and RD, were found for the corresponding tracts from neonates to children around puberty. It is clear that even at birth, nearly all major WM tracts demonstrate similar morphology as those in children around puberty. Significant correlations of homologous tracts are shown for both neonate and child groups. The comparisons of the inter-tract correlation matrices between the neonate and child group indicated that stronger inter-tract correlations are established during development. Using data-driven hierarchical clustering algorithm with no *a priori* information, we were able to reveal that the linkage patterns of the major tracts differ between the two age groups. Specifically, homologous tracts involved in similar brain functions tend to cluster together for children around puberty especially with tract-level RD measurements. Such clustering patterns of homologous tracts become more prominent from birth to puberty. These changes of inter-tract correlations between neonates and children around puberty suggest inhomogeneous but organized axonal development which causes the reshuffled inter-tract correlation pattern while keeping homologous tracts tightly correlated. To the best of our knowledge, this is the first study investigating dynamics of inter-tract correlations with DTIbased microstructural measurements during early human brain development.

#### **HETEROGENEOUS WM GROWTH**

The WM development is heterogeneous among different tract groups, but more homogeneous among homologous tracts. The heterogeneity among different tract groups includes heterogeneous tract-level measurements of DTI-derived metrics at each time point and heterogeneous changes of these DTI-based tractlevel measurements from birth to puberty. The general FA increase and general MD, AxD, and RD decrease for all WM tracts in early brain development shown in **Figure 2** are consistent with the previous findings (e.g., Barnea-Goraly et al., 2005; Snook et al., 2005; Eluvathingal et al., 2007; Dubois et al., 2008; Lebel et al., 2008; Gao et al., 2009; Giorgio et al., 2010; Schmithorst and Yuan, 2010; Tamnes et al., 2010; Westlye et al., 2010). The heterogeneity among different tract groups is most prominent with FA measurements for children around puberty (**Figure 2**). From **Figure 2A**, it is clear that FA of CST-L/R (project tract group) is highest in children around puberty, followed by FMajor/FMinor (commissural tract group), IFO-L/R (association tract group) and CGC/H\_L/R (limbic tract group). This order of tract group FA measurements is preserved back at birth, but the differences of FA among the tract groups are smaller (**Figure 2A**) for neonatal brains. The heterogeneous changes of tract-level DTI metrics in **Figure 2** may cause the reshuffling of inter-tract correlation patterns. Quite similar FA values (**Figure 2A**) as well as MD, AxD, and RD values (**Figures 2B,D**) of the homologous tracts can be found for both age groups.

## **STRENGTHENED AND RESHUFFLED INTER-TRACT CORRELATION DURING DEVELOPMENT**

At birth, significant correlations can be observed based on microstructural measurements of homologous tracts, as shown in upper panels of **Figures 3A,D**. From **Figure 4**, the general intertract correlations from birth to puberty are clearly stronger for all four DTI-derived microstructural measurements. With FA measurement as an example, each entry of correlation matrix in **Figure 4A** can be expanded to the correlation scatterplots such as those shown in panels of **Figure 3A**. Our results also indicated that significantly increased inter-tract correlations are more widespread with correlation coefficients based on RD measurements. For both age groups, correlation coefficients are highest based on AxD measurements (**Figure 4C**) and lowest based on FA measurements (FA). This pattern is especially clear for children around puberty. The overall higher inter-tract correlation coefficients based on AxD measurements suggest that axonal integrity is coherent among the WM tracts within an individual child's brain but varies among different children. To help understand this finding, we could assume a situation when each of the children around puberty has identical AxD for all 10 major WM tracts and the AxD values vary among these children. Under such situation, all inter-tract correlation coefficients based on AxD would be the perfect value 1. The relatively low inter-tract correlation coefficients based on FA measurements may be caused by heterogeneity of FA values among the WM tracts within each individual subject's brain. Larger variability of FA values among WM tracts can also be observed in **Figure 2A**.

From **Figure 5A**, the FA-based dendrograms show that several homologous tracts are clustered together even for neonates. The left and right IFO are clustered with rank 1 for dendrograms based on tract-level FA of neonatal brains. IFO is thought to play a role in integrating the information from auditory and visual cortices to the prefrontal cortex (e.g., Martino et al., 2010). Resting-state fMRI studies have shown consistent pattern of activation in auditory and visual networks in neonates (Fransson et al., 2007; Doria et al., 2010). It is noteworthy that in the restingstate fMRI studies, the connectivity was also identified through correlation of brain-oxygen-level-dependent (BOLD) signal fluctuations in the homologous brain regions. The strong correlations of BOLD signal time courses in visual and auditory networks between left and right hemisphere in neonatal brains may be related to tight cluster of IFO-L and IFO-R involved in these brain functions. Both neonatal and child dendrograms based on FA measurements (**Figure 5A**) have two more separate clusters of projection tracts (CST) and limbic tracts (CGC). All these clusters can also be found in adult brains (Wahl et al., 2010). **Figure 5B** demonstrates one of the most compelling findings with dendrograms based on RD measurements. Although two pairs of homologous WM tracts are clustered together with rank 1 (IFO\_L/R) and rank 3 (CGC\_L/R), the other two pairs of homologous tracts and FMajor/FMinor are spread all over the dendrogram for neonatal brains (**Figure 5B**). It is striking that all 4 pairs of homologous tracts and FMajor/FMinor are tightly clustered together for children around puberty (**Figure 5B**). RD is closely related to myelination of WM tracts (Song et al., 2002). We should be careful to associate the RD values with myelination due to crossing-fiber and pathological situations (Wheeler-Kingshott and Cercignani, 2009). Nevertheless, during normal brain development with no implication of pathology and with the assumption that tract-level RD measurement for major WM tracts is much less affected by crossing fiber compared to voxelwise RD measurement, tract-level RD is still considered as an important index reflecting the degree of myelination (e.g., Snook et al., 2005; Eluvathingal et al., 2007; Gao et al., 2009; Tamnes et al., 2010; Westlye et al., 2010). The results in **Figure 5B** suggest that organized myelination from birth to puberty plays an important role to reshuffle the inter-tract correlations and result in clustered homologous tracts and clustered functionally similar tracts. The dendrogram patterns based on AxD and MD measurements are different than those based on FA or RD measurements. Unlike RD-based dendrogram (**Figure 5B**), neither of the dendrograms based on AxD or MD measurements for children around puberty shows well organized clusters of all 4 pairs of homologous tracts and FMajor/FMinor (**Figures 5C,D**). Previous studies (Mukherjee et al., 2002; Snook et al., 2005; Gao et al., 2009) found that the measurements of RD decrease dramatically with relatively little changes in AxD of the major WM tracts during brain development. Our results demonstrated in **Figure 2** also indicate smaller and more homogeneous changes of AxD from birth to puberty compared to those of RD. These AxD change features may explain why relatively disorganized AxD-based dendrograms remain for children around puberty. From Equation (1), MD is the linear combination of AxD and RD. The relatively disorganized dendrograms based on MD measurement for child group could also be originated from smaller and more homogeneous changes of AxD measurements during development. Nevertheless, with the brain development, there are still trends for homologous and functionally similar tracts to cluster together in dendrograms based on AxD and MD in **Figures 5C,D**.

## **POSSIBLE MECHANISMS OF THE INTER-TRACT CORRELATION CHANGES FROM BIRTH TO ONSET OF ADOLESCENCE**

This study on dynamics of inter-tract correlations from birth to onset of adolescence provides unique insight on the wellorganized cerebral WM development. The development of human cerebral WM tracts is characterized with enhanced myelination and axonal integrity. From the results in this study, inhomogeneous RD decreases among the tracts take place during development. More (84.4% of all independent correlation coefficients) inter-tract correlations become stronger with RD measurements, compared to those with any other DTI metric measurements. In addition, the dendrograms based on RD (**Figure 5B**) demonstrate that all 4 pairs of homologous tracts and FMajor/FMinor are tightly clustered at puberty while only 2 pairs of homologous tracts are clustered at birth. The cerebral WM at birth is likely to be in a relatively random and disorganized status. Due to close relationship of RD with the myelination of WM tract, these results suggest that inhomogeneous enhancement of myelination rather than strengthening of axonal integrity plays a key role in reshaping the WM configuration during development. Both genes and experiences could also play a role to adjust WM microstructures so that the homologous WM tracts reach to a coherent status to meet the needs of certain brain functions by the time of onset of adolescence. Although it is not known by what mechanism inhomogeneous myelination is modulated during WM maturation, our results suggest that the myelination process is precisely controlled so that all 4 pairs of homologous tracts and FMajor/FMinor are clustered together around puberty (**Figure 5B**).

#### **LIMITATIONS OF THIS STUDY AND FUTURE DIRECTIONS**

There are several issues which may affect the results in this study. Five pairs of major WM tracts were chosen for this study due to the fact that only these five pairs of major WM tracts could be reproducibly traced with neonate DTI. The numbers of participated subjects, 26 for neonates and 28 for children, just exceeds 25 which is needed for correlation analysis of five pairs of WM tracts. Higher sample numbers could increase the confidence level in analysis of hierarchical clustering. Corrections for multiple comparisons were only performed on testing the correlation matrices against identity matrix and matrix with equal non-diagonal elements. No correction was performed on the hierarchical clustering results due to lack of any known methods to perform such a correction on dendrograms. The accuracy of measuring tract-level DTI-derived metrics plays a key role in inter-tract correlations. This accuracy is affected by three major factors. They are the crossing-fiber factor, SNR of the data and partial volume effects. Both the tractography and the DTI-derived measurements are biased at crossing-fiber regions (Wheeler-Kingshott and Cercignani, 2009). With single tensor model and tractography method of fiber assignment by continuous tracking (FACT) (Mori et al., 1999), it is apparent that the tracing method adopted in this study cannot resolve the crossingfiber issue, resulting in imperfect binary mask of the traced tracts for tract-level measurements of DTI metrics. Nevertheless, the tracing protocol (Wakana et al., 2007) based on FACT tractography captures the core of the major WM tracts and is still widely used in the field. We conducted two repetitions of diffusion MRI and the SNR is sufficient for data acquisition with 3T magnet. With diffusion imaging resolution 2 × 2 × 2 mm3 for neonates and 2 × 2 × 2*.*2 mm<sup>3</sup> for children around puberty, the partial volume effects are inevitable. However, it seems the effects of imperfect WM fiber tracing offsets the partial volume effects for obtaining accurate tract-level DTI metrics, in that the tracing algorithm adopted in this study cannot trace the small branches of the fibers where the problem of partial volume effects is most prominent. Ranks of the DTI measurements, instead of measured metrics themselves, were used for correlation pattern analysis. Despite possibly different levels of biases of the DTI metric measurements caused by partial volume effects due to different head sizes between neonates and children, the similar shifts of measurements in the same age group could have minimum effects on the rank of metric measurements and therefore minimum effects on the Spearman's rank correlation patterns. Although same types of scanners were used in this study, systematic differences of the scanners may affect the DTI analysis results. To make sure that the effects of systematic differences caused by two different scanners are minimal, a healthy young subject ("*in vivo* human phantom") was scanned in both scanners used in this study and with the same DTI sequence. The quantitative comparisons were also conducted. Quantitative DTI measurement differences caused by scanner difference were tested to be within the range of variability of scanning the same subject twice with one scanner (Saxena et al., 2012). With the same type of scanners used in this study and rigorous quality control of both scanners, the effects of scanner differences on the presented results in this study are thus negligible. The group of children around puberty included both pre-puberty and post-puberty subjects. The age difference between the two groups, newborns and children around puberty, is much larger than the age difference within the group of around puberty. Therefore, we hypothesized that the intra-group WM developmental heterogeneity for the children group exists, but is not big enough to affect the intergroup results presented in this study. This has been tested and proved by **Supplemental Figure 1**. In **Supplemental Figure 1**, we separated the children around puberty into two subgroups, prepuberty (9.5–12 years) and post-puberty (12–15 years). With reduced sample number for each subgroup, we could conduct the correlation analysis for 6 tracts (3 pairs of homologous tracts). It is shown in **Supplemental Figure 1** that homologous tracts are still clustered together in both subgroups, like the cluster patterns shown in the right column of **Figure 5**. Moreover, the linkage patterns are very similar between the two subgroups, with slight dendrogram rank change.

In the future, several improvements can be made to address the issues affecting accuracy of measuring tract-level DTI metrics. The tracking methods (e.g., Tournier et al., 2004; Behrens et al., 2007) which are capable of resolving the crossing-fiber issue can be adopted. Although there is no general consensus on which parameters can replace these four DTI-derived metrics and better characterize the WM microstructure, there have been a few metrics such as general fractional anisotropy (GFA) (Tuch, 2004; Fritzsche et al., 2010; Zhan et al., 2010), generalized anisotropy (GA) (Ozarslan et al., 2005), mode of anisotropy (Ennis and Kindlmann, 2006; Douaud et al., 2011) and fractions (Hosey et al., 2008; Jbabdi et al., 2010) which are less sensitive to crossing-fiber problem. In addition, tract-based spatial statistics (TBSS) from FSL (http://www*.*fmrib*.*ox*.*ac*.*uk/fsl) (Smith et al., 2006) can be used to alleviate the partial volume effects, as shown in the study of FA correlations in adults (Li et al., 2012).

## **CONCLUSION**

In conclusion, inter-tract correlation changes during development from birth to onset of adolescence were investigated with tract-level FA, RD, AxD, and MD measurements. Stronger and enhanced microstructural inter-tract correlations were found during development. The linkage patterns of the major tracts also differ with the dendrograms of two age groups due to brain development. These changes of microstructural correlations from birth to puberty suggest inhomogeneous but organized myelination processes which cause the reshuffled inter-tract correlation pattern and make homologous tracts tightly clustered. Especially RD-based dendrograms reveal that all 4 pairs of homologous tracts and FMajor/FMinor investigated in this study are tightly clustered for children around puberty while only 2 out of these 5 pairs of tracts are clustered at birth, indicating important role of myelination to reshape the WM configuration. It opens a new window to study WM tract development and can be potentially used to investigate atypical brain development due to neurological or psychiatric disorders.

#### **ACKNOWLEDGMENTS**

This study is sponsored by NIH (Grant Nos. EB009545, MH092535, and MH092535-S1, Hao Huang), the Natural Science Foundation of China (Grant Nos. 31221003 and 81030028, Yong He), the National Science Fund for Distinguished Young Scholars of China (Grant No. 81225012, Yong He) and the 111 Project (Grant No. B07008, Qi Dong).

#### **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fnhum.2013. 00721/abstract

#### **Supplementary Figure 1 | Dendrograms depicting the hierarchical clustering pattern obtained from tract-level FA (A), RD (B), AxD (C) and MD (D) measurements for two subgroups of children around puberty.**

Dendrograms on the left column are for the subgroup of children younger than 12-years-old (9.5–12 years); those on the right column are for the subgroup of children older than 12-year-old (12–15 years).

## **REFERENCES**


weighted MR images of the human brain. *Magn. Reson. Imaging* 26, 236–245. doi: 10.1016/j.mri.2007.07.002


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 18 June 2013; accepted: 11 October 2013; published online: 29 October 2013. Citation: Mishra V, Cheng H, Gong G, He Y, Dong Q and Huang H (2013) Differences of inter-tract correlations between neonates and children around puberty: a study based on microstructural measurements with DTI. Front. Hum. Neurosci. 7:721. doi: 10.3389/fnhum.2013.00721*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Mishra, Cheng, Gong, He, Dong and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Graph theoretical analysis of developmental patterns of the white matter network

#### *Zhang Chen1, Min Liu1, Donald W. Gross <sup>2</sup> and Christian Beaulieu1 \**

*<sup>1</sup> Department of Biomedical Engineering, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada <sup>2</sup> Division of Neurology, Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Hao Huang, University of Texas Southwetern Medical Center, USA Pew-Thian Yap, University of North Carolina, USA*

#### *\*Correspondence:*

*Christian Beaulieu, Department of Biomedical Engineering, 1098 Research Transition Facility, University of Alberta, 8308-114 Street, Edmonton, AB T6G 2V2, Canada e-mail: christian.beaulieu@ ualberta.ca*

Understanding the development of human brain organization is critical for gaining insight into how the enhancement of cognitive processes is related to the fine-tuning of the brain network. However, the developmental trajectory of the large-scale white matter (WM) network is not fully understood. Here, using graph theory, we examine developmental changes in the organization of WM networks in 180 typically-developing participants. WM networks were constructed using whole brain tractography and 78 cortical regions of interest were extracted from each participant. The subjects were first divided into 5 equal sample size (*n* = 36) groups (early childhood: 6.0–9.7 years; late childhood: 9.8–12.7 years; adolescence: 12.9–17.5 years; young adult: 17.6–21.8 years; adult: 21.9–29.6 years). Most prominent changes in the topological properties of developing brain networks occur at late childhood and adolescence. During late childhood period, the structural brain network showed significant increase in the global efficiency but decrease in modularity, suggesting a shift of topological organization toward a more randomized configuration. However, while preserving most topological features, there was a significant increase in the local efficiency at adolescence, suggesting the dynamic process of rewiring and rebalancing brain connections at different growth stages. In addition, several pivotal hubs were identified that are vital for the global coordination of information flow over the whole brain network across all age groups. Significant increases of nodal efficiency were present in several regions such as precuneus at late childhood. Finally, a stable and functionally/anatomically related modular organization was identified throughout the development of the WM network. This study used network analysis to elucidate the topological changes in brain maturation, paving the way for developing novel methods for analyzing disrupted brain connectivity in neurodevelopmental disorders.

**Keywords: graph theory, neurodevelopment, anatomical connectivity, modular networks, small world network**

## **INTRODUCTION**

Neuroimaging studies have demonstrated widespread and regionally specific structural and functional brain changes during development from infancy to adulthood. Structural magnetic resonance imaging (MRI) studies have reported age-related changes in brain volumes (Giedd et al., 1999; Good et al., 2001), areas (Thompson et al., 2000), cortical thickness (Sowell et al., 2004; Shaw et al., 2008), and regional gray matter (GM) and white matter (WM) density (Paus et al., 1999; Gogtay et al., 2004). The developmental changes in GM and WM on gross scale MRI may reflect synaptic pruning and myelination occurring at the neuronal level (Gogtay et al., 2004). Functional neuroimaging studies have demonstrated increased connectivity among distant regions and decreased connectivity among neighboring regions in brain maturation which suggests a mechanism of segregation of local regions and integration of distant regions into disparate subnetworks for the developing brain (Fair et al., 2008, 2009; Vogel et al., 2010). Diffusion tensor imaging (DTI) studies of WM have shown age-related increases in fractional anisotropy (FA) and decrease in overall diffusion with development [many studies

but some include (Snook et al., 2005; Lebel et al., 2008; Tamnes et al., 2010)], including into young adulthood (Giorgio et al., 2008; Lebel and Beaulieu, 2011).

The recent advent of modern network analysis based on graph theory (Strogatz, 2001), has enabled the investigation of the largescale topological organization of various structural and functional brain networks such as the small-world property, network efficiency and modularity (He et al., 2007; Bullmore and Sporns, 2009; He and Evans, 2010). The network metrics have also proven useful in modeling the large-scale functional and structural organization of the developing brain. Several functional brain network studies have reported age-related increases in the small-worldness (Wu et al., 2013) and a progression from local to distributed organization (Fair et al., 2009) in brain development. The analysis of the structural brain network constructed from regional cortical thickness correlations has revealed a non-linear developmental pattern in network metrics and that most topological changes happen at the late childhood stage (Khundrakpam et al., 2013).

Recently, there has been an increasing interest in the study of how graph metrics of the anatomical brain network change during development. Using DTI, Yap et al. (2011) examined WM networks of 39 healthy pediatric subjects with longitudinal data collected at average ages of 2 weeks, 1 year, and 2 years and demonstrated that the small-world architecture exists at birth with efficiency that increases in later stages of development. Two recent brain connectivity studies of WM maturation pattern using diffusion MRI tractography demonstrated linear and non-linear patterns of increasing structural efficiency with age between ages 2 and 18 years in 30 patients scanned clinically and otherwise deemed normal post-MRI (Hagmann et al., 2010) and between ages 12 and 30 years in 439 healthy subjects (Dennis et al., 2013). However, those studies were limited by different constraints such as a binarized brain network, limited sample size, or restricted age range (early adolescence to early adulthood), thus the developmental trajectory of the WM network from early childhood to adulthood remains unclear.

Therefore, the main goal of this study was to map the developmental changes of the structural brain network based on WM connectivity in 180 typically-developing subjects from 6 to 30 years of age. We hypothesized (i) non-linear age-related developmental trajectories of network metrics as most changes would be expected to happen at late childhood and adolescent stages, and (ii) altered modular organization in different age groups that reflects a process of fine-tuning in structural brain development.

## **MATERIALS AND METHODS**

#### **SUBJECTS**

This study included 180 healthy right-handed subjects aged from 6 to 30 years. Health was verified by asking participants a series of questions to ensure there was no history of neurological or psychiatric disease or brain injury. All subjects gave informed consent; child assent and parent/guardian consent was obtained for volunteers under 18 years. The subjects were divided into 5 age groups with equal numbers of subjects and demographics of all groups are shown in **Table 1**.

#### **IMAGE ACQUISITION**

All data were acquired on a 1.5-T Siemens Sonata MRI scanner. Standard DTI was acquired using a dual spin-echo, single shot echo-planar imaging sequence with the following parameters: 40 3-mm-thick slices with no inter-slice gap, *TR* = 6400 ms, *TE* = 88 ms, 6 non-collinear diffusion sensitizing gradient directions



with *b* = 1000 s/mm2, 8 averages, field-of-view 220 × 220 mm2, matrix of 96 × 128 zero-filled to 256 × 256, and scan time of 6:06 min. T1-weighted images were also acquired using MPRAGE with *TE* = 4*.*38 ms, *TR* = 1870 ms, *TI* = 1100 ms, and scan time of 4:29 min.

#### **STRUCTURAL BRAIN NETWORK CONSTRUCTION**

Image preprocessing steps including motion and eddy current corrections were performed using FSL 5.0 for all DTI images (http://fsl*.*fmrib*.*ox*.*ac*.*uk/fsl/fslwiki). The T1-weighted (MPRAGE) image of each subject was first linearly coregistered (**Figures 1A,B**) to its corresponding b0 image. Each transformed T1 image was then non-linearly registered to a pre-segmented and validated volumetric template, the automated anatomical labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002) as shown in **Figures 1B,C**. This parcellation divided the cortical surface into 78 regions (39 per hemisphere). See **Table 2** for the name of the regions and their corresponding abbreviations. The resulting inverse deformation map (*T*<sup>−</sup>1) for each subject was then applied to warp the AAL template to the DTI native space of each subject using nearest neighbor interpolation method (**Figures 1E,F**) as each AAL region was defined as a brain network node. Whole brain WM tractography was performed using a brute-force streamline-tracking method (Basser et al., 2000) with a FA threshold of 0.2 and primary eigenvector turning angle of 45 degrees (**Figures 1A,D**). To limit false positive connections, two cortical regions were deemed connected if at least 10 connecting fibers with two end points were located between them; the same threshold was also applied in a recent brain network study (van den Heuvel et al., 2012). The effects of different nodeconnecting fiber number (FN) thresholds ranging from 3 to 10 were determined for our network analysis. We quantified the weight of each valid connection between two cortical regions (*i* and *j*) as the product of the connecting FN and mean FA of the connecting fiber, normalized by dividing the average volume of the two connecting regions to counteract the bias where larger cortical regions inherently project/receive more "virtual" fibers (*wij* = FN∗FA/Volume). Several diffusion brain network studies have applied this weighting function (Lo et al., 2010; Brown et al., 2011). As a result, the structural brain network of each participant was represented by a symmetric 78 × 78 matrix (**Figure 1G**).

To examine the small-worldness and modular organization of the WM networks for all different age groups, one weighted backbone network for each age group was generated to capture the underlying anatomical connectivity patterns using a previously published method by our group (Gong et al., 2009). In summary, to identify the highly consistent cortical connections, a non-parametric one-tailed sign test was applied. For each pair of cortical regions, the sign test was performed with the null hypothesis that there is no existing connection. The Bonferroni method was applied to correct for multiple comparisons at *P <* 0*.*05. The use of this conservative statistical criterion generated a symmetric weighted matrix as each edge weight was calculated as the mean of all existing edges in all subjects that captured underlying anatomical connectivity patterns in the human cerebral cortex (Gong et al., 2009).

#### **NETWORK ANALYSIS**

Several network topological properties were applied for the weighted anatomical brain network derived from each participant, including small-worldness, efficiency and modularity (Watts and Strogatz, 1998; Latora and Marchiori, 2001; Newman, 2006). The connection weights of all edges (*wij*) were normalized by the mean weight of the whole network to keep network cost at the same level for all subjects.

For a weighted network *G* with *N* nodes and *K* edges, the total strength *S* was defined as the mean of all edge weights in the network, *S(G)* = <sup>1</sup> *N N* -*M i* = *j* ∈*G wij* where *i* and *j* are two distinct nodes in graph *G*. The clustering coefficient (*CC*) of the weighted network *G* quantifies the likelihood whether the neighboring nodes of any network nodes are connected with each other (Onnela et al., 2005), which was defined as: *CC* <sup>=</sup> <sup>1</sup> *N* - *j,k* ∈*G (wij wjk wik)*1*/*3*/(k*<sup>∗</sup> *<sup>i</sup> (ki* − 1*)/*2*)*, where *ki* is the number of

connected neighbors of node *i*. The weighted characteristic path length *L* of a network is the average minimum connectional weights that link any two nodes of the network. To avoid the issue of disconnected nodes, *L* was measured here by using a "harmonic mean" distance between any pair of nodes such as the reciprocal of the average of the reciprocals (Newman, 2003). A real network would be considered small world if it meets the following criteria: γ = *Creal <sup>p</sup> /Crand <sup>p</sup> >* 1 and λ = *Lreal <sup>p</sup> /Lrand <sup>p</sup>* ∼ 1 (Watts and Strogatz, 1998), where *Crand <sup>p</sup>* and *Lrand <sup>p</sup>* are the mean *CC* and *L* of matched random networks that preserve the same number of nodes, edges and degree distribution as the real network (Maslov and Sneppen, 2002). In this study, we generated 1000 matched random networks for each group network.

The global efficiency *Eglob* of a weighted network *G* is defined as *Eglob(G)* = <sup>1</sup> *N(N*−1*)* - *i* = *j*∈*G* 1 *wij* , where *wij* is the smallest connectional weight between node *i* and *j* and *N* is the number of nodes. It characterizes the efficiency of a system transporting information in parallel (Latora and Marchiori, 2003). The local efficiency *Eloc* of a weighted network *<sup>G</sup>* is defined as: *Eloc(G)* <sup>=</sup> <sup>1</sup> *N* - *i* ∈*G Eglob(Gi)*, where *Gi* denotes the subgraph composed of the nearest neighbors of node *i*. The local efficiency represents the fault tolerance level of the network in response to the removal of a node (Latora and Marchiori, 2003).

The regional global efficiency *Ereg* of a given node *i* is defined as: *Enodal(i)* = <sup>1</sup> *N*−1 - *i* = *j* ∈*G* 1 *wij* , as it measures the average smallest path weight between given node *i* and all other nodes in the network. The node *i* was considered as a hub if its regional global efficiency was at least one standard deviation (*SD*) greater than the mean nodal efficiency of the whole network.

The modularity *Q(p)*for a given partition *p* of a weighted brain structural network is defined as *<sup>Q</sup>(p)* <sup>=</sup> *<sup>N</sup>* -*M s* = 1 *ws <sup>W</sup>* − *Ws* 2*W* 2 , where *NM* is the number of modules, *W* is the total weight of the network, *ws* is the sum of the connectional weights between all nodes in module *s*, and *Ws* is the sum of the nodal weights in module *s*. The modularity index quantifies the difference between the weight of intra-module links of the actual network and that of a random network in which connections are weighted at random (Newman, 2004). The aim of this module identification process is to find a specific partition *p* which yields the largest network modularity, *Q(p)*. Here a modified greedy optimization algorithm (Clauset et al., 2004; Danon et al., 2006) is used to find the modules of the network. The advantage of this modularity optimization method is that it takes into account the heterogeneity of module size observed in real networks (Danon et al., 2006).

We also determined the participation coefficient (PC) for each cortical region in terms of their inter-modular connection density (Guimera and Amaral, 2005; Guimera and Nunes Amaral, 2005; Sales-Pardo et al., 2007). The PC, *Pi*, measures the intermodular connectivity of node *i*, and is defined as: *Pi* = 1 − *N* -*M s* = 1 *wis wi* 2 , where *NM* is the number of modules and *wis* is intermodular connectional weight between the node *i* and module

*s*. *wi* is the total weight of node *i* in the network. The PC of node *i* will be close to 0 if all weights are within its module.



The node *i* was considered as an inter-modular hub if its *PC* value was at least one *SD* greater than the mean PC of the whole network.

#### **STATISTICAL ANALYSIS**

Between-group differences analysis of all the global network metrics (*S*, *CC*, *L*, *Eglob*, *Eloc*, *Ereg , Q*) was performed between adjacent age groups using the General linear model (GLM) with age and gender included as covariates. The nodal properties (*Ereg* , *z, P*) were corrected by false discovery rate at *q* = 0*.*05 (Genovese et al., 2002; Zeisel et al., 2011).

### **RESULTS**

#### **AGE-RELATED CHANGES IN FIBER NUMBER AND NETWORK SPARSITY**

To examine the age effect on the tractography results, we mapped age-related changes in the FN and sparsity of white matter network as shown in **Figures 2A,B**. We found that age has an incremental effect on both the FN and sparsity, where both increase by a factor of ∼1.6 and ∼1.2, respectively, from age 6 to 30 years. These increases are presumably due to the known elevations of FA in WM with age. Given the fact that our network edge weighting function depends on FN and FA, it is expected that the connectivity strength of the network would also increase with age.

#### **SMALL-WORLD EFFICIENCY OF DEVELOPING WM NETWORKS**

To examine the small-worldness of the WM networks for all different age groups, using a previously published method by our group (Gong et al., 2009), one weighted backbone network for each age group was generated to capture the underlying anatomical connectivity patterns as shown in **Figure 3**. Compared with their corresponding 1000 random networks, all

five age groups showed strong small-worldness (σearly childhood = 3*.*54, σlate childhood = 3*.*19, σadolescence = 3*.*25, σyoung adult = 3*.*12, σadult = 3*.*19).

### **GLOBAL NETWORK PROPERTIES AND THEIR AGE-RELATED TRAJECTORIES**

Over all subjects in each age group, the total network weight, CC, Lp, modularity (Q), Eglob and Eloc was calculated for the WM network and the age-related trajectories are shown in **Figure 4**. The total network weight displayed significant increases in three of the four developing stages, whereas the other metrics such as L*p*, Q, Eglob, and Eloc demonstrated non-linear alteration patterns where most changes happened from young childhood to late childhood that then leveled off. Both L*<sup>p</sup>* and Q decreased significantly from young childhood to late childhood but stabilized at older ages. Global network efficiency increased significantly from young childhood to late childhood but also stabilized

later. Local network efficiency increased significantly from late childhood to adolescence and stabilized afterwards.

#### **REGIONAL EFFICIENCY OF THE DEVELOPING WM NETWORKS**

We found consistent hubs regions, measured here as the AAL areas with highest regional global efficiency, such as bilateral PCUN, SFGdor, and SFGmed, that are shared by all age groups as shown in **Figure 5**. Comparing the regional efficiency changes from group to group in these hubs, seven regions had increased nodal efficiency (*P <* 0*.*05, FDR corrected) from early childhood to late childhood and two regions from late childhood to adolescence (**Figure 6**). Most regional changes from early to late childhood are in the default-mode system, including bilateral PCUN and left DCG. Left STG and right INS were found to have increased efficiency from late childhood to adolescence.

**FIGURE 4 | Age-related changes in different network metrics for the developing WM network from early childhood to adult. (A)** Total network strength (S), **(B)** Clustering coefficient (CC), **(C)** Shortest path length (L*p*), **(D)** Modularity (Q), **(E)** Global efficiency (Eglob), and **(F)** Local efficiency (Eloc). Significant changes between any adjacent age groups are indicated by their *p* value. An increase with age is observed in S over the full age span. Eglob increases only between the two youngest age groups and Eloc only between late childhood and adolescence; in both cases, the efficiency values then stay elevated. Reductions are observed in L*<sup>p</sup>* and Q from early to late childhood that is then maintained low. There is no change in CC between any adjacent age groups. The + signs indicate outliers.

### **MODULAR ORGANIZATION AND CONNECTOR HUBS OF THE DEVELOPING WM NETWORKS**

The modular organization of the developing structural brain networks for the five different age groups is shown in **Table 3** and **Figure 7**. Six modules (1–6) were detected in all age groups indicating strong stability (Greicius et al., 2003) in the modularity of the developing brain network. Despite decreased modularity from early to late childhood, the modular structures of both groups were almost identical. Module 1 was mostly composed of bilateral orbitofrontal regions (ORBsup, ORBsupmed, ORBmid, REC) in early and late childhood that becomes more lateralized in adolescence. Right orbitofrontal regions become connected with right temporal and occipital regions that resembles the ventral visual system (Grill-Spector et al., 2008) and left orbitofrontal regions become part of lateral frontal system. Module 2 consists of mostly occipital regions (SOG, CAL, CUN) throughout the youngest age groups except at adulthood when the left occipital regions become part of left ventral visual system (Grill-Spector et al., 2008). Lateralized modules 3 (left hemisphere) and 4 (right hemisphere) consist of regions mostly across frontal, parietal and temporal lobes from each hemisphere from early to late childhood. However, module 4

indicated in **Table 2**.

SFGmed are consistent over all age groups. Note that all brain images are viewed from the medial side (also for **Figures 6**, **7**).

**FIGURE 6 | Regions with significantly increased nodal efficiency from early childhood to late childhood and late childhood to adolescence.** Seven regions had increased nodal efficiency (*P <* 0*.*05, FDR corrected) from early childhood to late childhood and two regions from late childhood to adolescence. Most regional changes from early to late childhood are in the default-mode system, including bilateral PCUN and left DCG. Left STG and right INS have increased efficiency from late childhood to adolescence.

is pruned to a mainly frontal-parietal system from adolescence onwards and module 3 doesn't reach a similar outcome until adulthood. Modules 5 and 6 are two of the most consistent modules during development and include mostly bilateral frontal (SFG, MFG) and posterior parietal (PCUN, DCG, SMA) regions, respectively.

The distribution of inter-modular hubs based on PC of each region for different age groups was very consistent (**Figure 8**). They were mostly located within posterior cortex, including bilateral PCUN, SPL, and MOG. Large frontal hubs such as bilateral SFGdor appeared in late childhood and remained significant afterwards.

To ensure the change of modular organization didn't result from the different sparsity of the five age groups, an additional analysis was performed where we normalized all group networks sparsity to the lowest sparsity at 0.1142 (early childhood) as weaker connections were removed from the other networks with higher sparsity (late childhood, 0.1262, adolescence, 0.1312, young adult, 0.1289, adult, 0.1329) and re-examined the modularity of the networks. We found extremely consistent modularity (0.56 previous vs. 0.56 with normalized sparsity in late childhood, 0.56 vs. 0.56 in adolescence, 0.55 vs. 0.56 in young adults, and 0.58 vs. 0.56 in adults) and modular organization compared with our original networks. Thus, we could presume that changes in the backbone network and its modular organization were not due to different matrix densities.

## **DISCUSSION**

The present study utilized DTI tractography and network theory to characterize changes to the global structural WM network with age from early childhood to adulthood. Our main results are demonstrations of (1) a non-linear age effect on most network topological properties of brain WM network in development where most changes happen at late childhood stage (10–13 years), such as increased global network efficiency and decreased modularity, suggesting a shift of organization toward a more randomized configuration, (2) consistent hubs involving several major functional systems across all age groups and significant nodal changes only happening from early childhood to adolescence, (3) anatomically localized modules in the development of brain WM network, and (4) key connector hubs during development of the WM network.

First, using graph theoretical analysis, small-world network architecture was demonstrated in the WM networks of all age groups. During the last decade, graph theoretical analysis has been widely applied to both the functional (Stam, 2004; Bassett et al., 2006; Achard and Bullmore, 2007) and anatomical (He et al., 2007; Hagmann et al., 2008; Gong et al., 2009) brain networks and one common finding is the existence of "small-worldness" in all types of network, as defined by high CC and low characteristic path length. Recent structural brain network studies have also

#### **Table 3 | Cortical regions in each module of developing white matter network.**


*(Continued)*


#### **Table 3 | Continued**

revealed that small-world topology and modular organization are established during early brain development (*<*2 years) to support rapid synchronization and information transfer with minimal rewiring cost (Fan et al., 2011; Yap et al., 2011). Thus, our results are in agreement with previous findings that the WM network maintains small-world efficiency at all stages of development.

Total network weight shows increases in three of the four developing stages, although the greatest change is between the two youngest groups pre-adolescence. Our finding is consistent with a previous WM network study that reported a significant increase in the average node strength in a group of subjects aged from 18 months to 18 years where it was suggested that increased network weight indicates increased nodal strength and greater physiological efficacy, particularly of long pathways (Hagmann et al., 2010). A functional network study has also reported increased functional integration due to a decrease of average path length during the same period and suggested it was related to increased axonal diameter and myelin thickness of long association fiber tracts (Supekar et al., 2009). We also found significant age-related decreases in the shortest path length and modularity and increase in the global efficiency of the developing WM network from early childhood to late childhood indicating greater integration among distant brain regions and a shift of topological organization to a more randomized configuration. Previous WM network (Hagmann et al., 2010) and cortical thickness network (Khundrakpam et al., 2013) studies of brain development also demonstrated a similar pattern of network metrics evolution between age 2 and 18 years and between 5 and 18 years, respectively. However, the WM network study had applied a linear fit for all the network metrics vs. age even though network metrics such as efficiency and clustering seemed to have leveled off after late childhood in their paper (Hagmann et al., 2010). Using a similar approach to ours, (Khundrakpam et al., 2013) demonstrated a leveling off of various cortical thickness network metrics after the early adolescence stage.

Consistent global hub regions, indicated by higher regional efficiency, are observed across all age groups. Hub regions are predominately association cortices that receive convergent inputs from multiple cortical regions. Regions such as SFG and PCUN have been constantly identified as the hub regions in both structural (He et al., 2007; Gong et al., 2009) and functional brain networks (Achard and Bullmore, 2007). A recent structural brain network study also identified them as the hub regions from age 2 years suggesting that they are established at a very early age (Hagmann et al., 2010). We also found that the regions with the most age-related increases in efficiency are in the default-mode system, including bilateral PCUN and left DCG. A functional brain network study has reported a less well-developed default mode network connectivity in early childhood compared with adults, especially within posterior regions such as PCUN (Fair et al., 2008). However, evidence from structural covariance network analysis has demonstrated significant pruning in the default mode system from early childhood to late childhood (Zielinski et al., 2010). Thus, we could speculate that nodal efficiency of default mode regions might plateau by late childhood.

In this study, a stable and functionally/anatomically related modular organization was demonstrated in the developing WM network. Six modules comprising regions with known functions or connections were identified in the developing WM network. Modules 1, 2, 5 and 6 were mostly composed of orbitofrontal, occipital, frontal, and posterior parietal regions that could correspond to sensory integration, visual, executive function, and default mode network, respectively, (Duncan and Owen, 2000; Raichle et al., 2001; Kringelbach, 2005). Modular network analysis has provided rich quantitative insights into the organization of complex brain networks. Studies in mammalian anatomical brain networks have revealed clusters that overlap with many known brain functions (Hilgetag et al., 2000;

**FIGURE 7 | Modular organization of the developing WM networks.** Six **modules (1–6)** were detected in all age groups and are represented by red, green, purple, yellow, pink, and blue colors. See **Table 3** for a detailed list of modular regions. Module 1 was mostly composed of bilateral orbitofrontal regions (ORBsup, ORBsupmed, ORBmid, REC) in early and late childhood and becomes more lateralized from adolescence onwards. Module 2 consists of mostly occipital regions (SOG, CAL, CUN) bilaterally. Lateralized modules 3 (left hemisphere) and 4 (right hemisphere) consist of regions mostly across frontal, parietal and temporal lobes within each hemisphere. Modules 5 and 6 are two of the most consistent modules during development that include mostly bilateral frontal (SFG, MFG) and posterior parietal (PCUN, DCG, SMA) regions, respectively.

Zhou et al., 2006). Previous neuroimaging studies have also demonstrated anatomically- and functionally-related modules in the human brain structural network using diffusion spectrum imaging (Hagmann et al., 2008) and the functional network using resting-state functional MRI (Salvador et al., 2005; Ferrarini et al., 2009; He et al., 2009; Meunier et al., 2009; Valencia et al., 2009). Also, network modules identified by cortical thickness network analysis are comprised of brain regions known to subserve distinct brain functions such as executive function, vision, and default mode network (Chen et al., 2008, 2011). Two recent DTI studies also revealed non-random and dynamic modular organization of structural brain network in the first 2 years of brain development (Fan et al., 2011; Yap et al., 2011). Two lateralized modules (3 and 4) that correspond to the frontal-parietal network were also observed in the developing WM network. The adult human brain exhibits distinct hemispheric asymmetries in both structure and function. These asymmetries are thought to originate from evolutionary, developmental, hereditary, experiential, and pathological factors (Toga and Thompson, 2003). Thus, we could speculate that the lateralized network modules might result from the functional and structural hemispheric asymmetries.

Taken together, our results suggest an efficient modular organization in the WM network from early childhood and are consistent with modular behavior reported in previous structural and functional brain network studies and more importantly, a lateralized developmental pattern in some of the modules. The inter-modular hubs are the main connectors between modules and their existence in frontal and posterior cortex in the developing brain are consistent with previous WM network (Yap et al., 2011) and cortical thickness network (Khundrakpam et al., 2013) analysis. Resting state functional networks have also reported a high density of strong functional connections in posterior cortex (Achard et al., 2006). Thus, we could speculate that the inter-modular hubs uncovered in this study are well-established at childhood and are responsible for the connections between different functional systems of the developing brain.

A few methodological issues need to be addressed. Two drawbacks of our study include the acquisition of DTI data with six diffusion directions at low *b* values of 1000 s/mm<sup>2</sup> and the use of deterministic tractography which will give errors in such an unsupervised tractography method given abrupt terminations at low FA crossing fiber regions or erroneous connections due to errors in the primary eigenvector direction. Multiple gradient directions can reduce the uncertainty of the primary eigenvector direction and limit potential bias as a function of tract orientation, both concerns for deterministic tractography of WM tracts (Landman et al., 2007). However, a recent study from our group has demonstrated six-direction data can also provide average diffusion measures like FA over a specific tract with comparable robustness to 30- or 60-direction data and yield appropriate parameter values for many major WM tracts (Lebel et al., 2012), which is encouraging as our edge weights were calculated based on the average FA of all voxels over the whole tract connecting two nodes. However, this does not overcome potential false positive connections or missed connections from the deterministic tractography algorithm. We attempted to minimize the former by invoking a minimum FN between regions but an incorrect connection that is consistent among the subjects within a group would still be included in the network analysis. DTI data with more than six directions also permit other advantages such as alternative analysis methods (e.g., probabilistic tractography) (Dennis et al., 2013). Higher *b* values than typically acquired are also advantageous for resolving crossing fibers and increasing the accuracy of tractography derived connections (Tournier et al., 2008). Another limitation of the study is that the age ranges of the groups covered a 3.1 to 4.7 year age range for the four youngest groups. In this study, a general linear model was applied to remove those age effects within all groups before performing the between-group comparison. In future study, smaller age ranges within groups may provide more specific indices of timing for the WM network maturation. Third, a FN threshold of 10 was applied to minimize the inclusion of random connections between two cortical regions. Currently, there are no standard approaches in determining the threshold value for the number of connecting fibers between regions as small thresholds such as 3 streamlines (Shu et al., 2011) produced networks with large sparsity with many spurious connections. Thus, our choice of higher threshold reduces, but does not eliminate, the risk of false-positive connections due to noise or the limitations in deterministic tractography. Recently, a threshold of 10 connecting streamlines or more was also applied in a brain network study (van den Heuvel et al., 2012) in which they considered that edges comprising fewer than 10 streamlines were potentially spurious and were deleted from the connection matrix. To examine the influence of the threshold, we tested a range of thresholds from 3 to 10 fibers and results including all network parameters are shown in **Table A1**. Although the network efficiency decreased as the sparsity decreased, the small worldness of the network remained intact. Most importantly, the group differences among adjacent age groups also remained consistent across all applied thresholds which indicates that the network comparison results are not sensitive to the threshold choices. Cortical regions in our study are defined by an a priori volumetric template (AAL) that was employed to automatically parcellate the entire cerebral cortex into different regions. Different templates used in various studies might cause discrepancy in the specific results, though the main trend of the network properties is expected to remain intact.

Various weighting functions for cortical-cortical connections have been applied in previous brain network analyses of brain development including 1/mean diffusivity (Hagmann et al., 2010) and proportional FN (Dennis et al., 2013), whereas we used the product of tract FA (known to increase exponentially with development over this age range but at unique rates per tract— Lebel et al., 2008) and AAL regional volume-normalized FN that has been used by others in studies of Alzheimer's Disease and aging (Lo et al., 2010; Brown et al., 2011). Other diffusion indices such as mean diffusivity (MD), axial diffusivity (AD) or radial diffusivity (RD) could have been examined instead of FA as a basis of "weighting" the network connections. However, to our best knowledge, while a few studies have applied MD as an edge weighting function (Hagmann et al., 2010; Li et al., 2012), none have used AD or RD. While changes in MD and FA for the WM typically occur together during maturation, with *MD* values decreasing and *FA* values increasing, the processes by which the two parameters change are theoretically different (Schmithorst et al., 2002; Huppi and Dubois, 2006) and they do not change at the same rate (Lebel et al., 2008). Axial and RD, under certain circumstances, may be more specific to underlying biological processes, such as myelin and axonal changes (Song et al., 2002). A recent study has demonstrated changes of FA in corticospinal tract and anterior corona radiata during development (2 to 40 years) that were attributed to the different rate changes in AD and RD (Faria et al., 2010). Thus, one would expect different WM network organization if using different weighting functions. Therefore, future studies could consider using multiple diffusion tensor measures such as FA, MD, AD and RD.

In conclusion, a graph theoretical approach was used to demonstrate age-related alterations in the large scale network properties of the developing WM network from early childhood (6 years) to adulthood (30 years). It was shown that increased network weight signifies a reshaping of the WM network from early childhood to late childhood with increased integration and decreased segregation. These findings are compatible with the notion that structural and functional brain networks become stable after late childhood. Our results also have implications for understanding how the modular organizational alterations in the large-scale structural brain networks underlie maturation of cognitive function in brain development. This study may pave the way for developing novel methods for analyzing disrupted brain connectivity in neurodevelopmental disorders.

## **ACKNOWLEDGMENTS**

Operating funding from the Canadian Institutes of Health Research, and salary awards from the China Scholarship Council (Min Liu) and Alberta Innovates—Health Solutions (Christian Beaulieu).

## **REFERENCES**


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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 01 June 2013; accepted: 09 October 2013; published online: 01 November 2013.*

*Citation: Chen Z, Liu M, Gross DW and Beaulieu C (2013) Graph theoretical analysis of developmental patterns of the white matter network. Front. Hum. Neurosci. 7:716. doi: 10.3389/fnhum. 2013.00716*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Chen, Liu, Gross and Beaulieu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## **APPENDIX**

**Table A1 | White matter network parameters derived from different fiber-number node-to-node connection thresholds in all age groups.**


*Adjacent groups show significant differences or trend in network properties are highlighted in shaded cells. FN: fiber number, Eglob: global efficiency, Eloc: local efficiency, Q: modularity, σ: small worldness.*

## Age-related changes in brain structural covariance networks

#### *Xinwei Li 1,2, Fang Pu2, Yubo Fan2, Haijun Niu1,2, Shuyu Li 1,2\* and Deyu Li <sup>2</sup> \**

*<sup>1</sup> State Key Laboratory of Software Development Environment, Beihang University, Beijing, China*

*<sup>2</sup> Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Department of Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Xiaobo Li, Albert Einstein College of Medicine, USA Zhang Chen, University of Alberta, Canada*

#### *\*Correspondence:*

*Shuyu Li and Deyu Li, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China. e-mail: shuyuli@buaa.edu.cn; deyuli@buaa.edu.cn*

Previous neuroimaging studies have suggested that cerebral changes over normal aging are not simply characterized by regional alterations, but rather by the reorganization of cortical connectivity patterns. The investigation of structural covariance networks (SCNs) using voxel-based morphometry is an advanced approach to examining the pattern of covariance in gray matter (GM) volumes among different regions of the human cortex. To date, how the organization of critical SCNs change during normal aging remains largely unknown. In this study, we used an SCN mapping approach to investigate eight large-scale networks in 240 healthy participants aged 18–89 years. These participants were subdivided into young (18–23 years), middle aged (30–58 years), and older (61–89 years) subjects. Eight seed regions were chosen from widely reported functional intrinsic connectivity networks. The voxels showing significant positive associations with these seed regions were used to describe the topological organization of an SCN. All of these networks exhibited non-linear patterns in their spatial extent that were associated with normal aging. These networks, except the primary motor network, had a distributed topology in young participants, a sharply localized topology in middle aged participants, and were relatively stable in older participants. The structural covariance derived using the primary motor cortex was limited to the ipsilateral motor regions in the young and older participants, but included contralateral homologous regions in the middle aged participants. In addition, there were significant between-group differences in the structural networks associated with language-related speech and semantics processing, executive control, and the default-mode network (DMN). Taken together, the results of this study demonstrate age-related changes in the topological organization of SCNs, and provide insights into normal aging of the human brain.

**Keywords: connectivity, structural covariance network, normal aging, neuroimaging, sensorimotor, neurocognition**

## **INTRODUCTION**

The majority of neuroimaging studies of aging have reported a consistent pattern of gray matter (GM) volumetric reductions in the human cortex, involving mainly prefrontal regions, parietal, and temporal association cortices, and the insula and cingulum (Resnick et al., 2003; Sowell et al., 2003; Raz et al., 2005; Du et al., 2006; Terribilli et al., 2011). However, more and more studies have demonstrated that cerebral changes with normal aging are not simply characterized by regional alterations but rather by the reorganization of cortical connectivity patterns (O'Sullivan et al., 2001; Koch et al., 2010; Wu et al., 2011b; Zhu et al., 2012). Using diffusion tensor imaging (DTI), several studies have consistently reported a diffuse loss of axonal integrity in senior populations (Salat et al., 2005; Pagani et al., 2008; Madden et al., 2009), which allowed for inferences regarding changes of structural connectivity in older people compared with younger adults. Building on complex network analysis methods, Gong et al. (2009) reported a reduction in overall cortical connectivity, decreased local efficiency, and a shift in regional efficiency from

parietal and occipital to frontal and temporal neocortex in older brains.

The investigation of structural covariance networks (SCNs) using structural magnetic resonance imaging (sMRI) is another useful method to explore structural brain networks. This approach mainly characterizes the pattern of structural covariance in GM morphology (e.g., volume, thickness and surface area) between brain regions using a general linear model (GLM) framework (Mechelli et al., 2005; Lerch et al., 2006; Nosarti et al., 2010; Zielinski et al., 2010; Montembeault et al., 2012; Soriano-Mas et al., 2012). Many studies have demonstrated underlying relationships among brain areas using structural correlation by sMRI, anatomical connectivity by DTI, and functional correlation by resting-state functional MRI. For example, He and colleagues found that structural networks based on cortical thickness measurements were compatible with known functional networks (He et al., 2007). Greicius et al. measured functional connectivity using independent component analysis and anatomical connectivity using DTI and found there existed white matter tract structural connections between functionally connected regions (Greicius et al., 2009). A recent study reported agreement in the correlations in GM thickness and underlying fiber connections across brain areas, but more information was included for the thickness network than the fiber network (Gong et al., 2012). In addition, Seeley and colleagues demonstrated that SCNs using voxel-based morphometry (VBM) were able to recapitulate the functional connectivity network topologies (Seeley et al., 2009). The covariance of different cortical regions in their GM volumes was considered to be the result of mutual trophic influences (Ferrer et al., 1995) or common experience-related plasticity (Draganski et al., 2004; Mechelli et al., 2004). The consistency among these three networks provides substantial support for SCNs serving a measure of network integrity in cross-sectional studies.

Of note, the analysis of SCNs using VBM has been successfully applied to map the eight SCNs and explore how neural systems build large-scale structural covariance during development (Zielinski et al., 2010). One previous aging study using structural covariance approaches compared two populations (younger vs. older subjects) and reported reduced structural associations in order adults, specifically in high-order cognitive networks (Montembeault et al., 2012). However, because subjects in only two age categories were involved in that experiment, it could not determine the aging trajectories of these sensorimotor and high-order cognitive networks. Zielinski et al. (2010) found that there were non-linear trajectories of primary visual, auditory, and sensorimotor networks during development. Moreover, these networks were provisionally established by early childhood, but underwent significant expansion in early adolescence before contraction or pruning in late adolescence. Many studies have shown that age-related atrophy of some neural regions follows variable, non-linear patterns (Allen et al., 2005; Kennedy et al., 2009; Terribilli et al., 2011). However, it remains unknown whether and how age-related changes of primary sensorimotor and high-order cognitive SCNs exhibit non-linear trajectories during normal aging.

Here, we used an SCN mapping approach to investigate eight large-scale networks in 240 healthy participants aged 18–89. These participants were subdivided into young (18–23 years), middle aged (30–58 years), and older (61–89 years) subjects. Given that SCNs subserving language, social–emotional, and executive control functions have shown gradual deterioration through normal aging, we expected to observe age-related trajectories in the deterioration of these large-scale networks. To test our hypothesis, we first investigated the aging trajectories of three sensorimotor and five high-order cognitive SCNs in three groups composed of an equal number of subjects at different ages, and then compared these SCNs differences between the groups.

#### **MATERIALS AND METHODS**

#### **PARTICIPANTS**

Three hundred sixteen right-handed, healthy subjects were selected from the Open Access Series of Imaging Studies (OASIS) cross-sectional database (http://www.oasis-brains.org) (Marcus et al., 2007). Data from seven subjects were excluded from further analysis because of poor image quality or image preprocessing. Because of relatively few subjects aged 30–60 years, we first selected 80 subjects from this age range as the middle-aged group (30 males and 50 females). Then, we selected 80 separate subjects around 75 years of age as the old group. Finally, we selected the youngest 80 subjects matched for gender as the young group. The names and characteristics of the groups are shown in **Table 1**. All subjects were evaluated using the Mini-Mental State Examination (MMSE) (Folstein et al., 1975) and Clinical Dementia Rating (CDR) scales (Morris, 1993; Morris et al., 2001). MMSE scores were higher than 29, and CDR scores were all zero. For demographic data on all subjects, see Marcus et al. (2007). This dataset has been used in several previous studies (Bakkour et al., 2009; Fjell et al., 2009; Salat et al., 2009; Li et al., 2011).

## **IMAGE ACQUISITION**

For each subject, three to four individual T1-weighted magnetization-prepared rapid gradient echo (MP-RAGE) images were acquired on a 1.5T Vision scanner (Siemens, Erlangen, Germany) within a single session. Head movement was minimized with cushioning and a thermoplastic facemask. Images were motion corrected and averaged to create a single image with a high contrast-to-noise ratio. MP-RAGE parameters were empirically optimized for gray/white contrast: *TR* = 9*.*7 ms; *TE* = 4 ms; flip angle = 10◦; slice number = 128; resolution = 256 × 256 (1 × 1 mm); thickness = 1*.*25 mm.

#### **IMAGE PREPROCESSING**

Structural MR images were processed using a technical computing software program (MATLAB 2010; The MathWorks Inc., Natick, Mass) and Statistical Parametric Mapping software (SPM 8; The Wellcome Department of Imaging Neuroscience, London, UK). Following the inspection of image artifacts, image preprocessing was performed with the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm/). Briefly, all native-space MRIs were segmented to extract GM based on an adaptive maximum *a posteriori* technique (Rajapakse et al., 1997) and partial volume estimation method (Tohka et al., 2004) without the need for *a priori* tissue probability information. In addition, a spatially adaptive non-local denoising filter (Manjon et al., 2010) and a hidden Markov random field model (Rajapakse et al., 1997) were applied to minimize the level of noise in the resulting GM segments. Subsequently, the high-dimensional DARTEL (diffeomorphic anatomical registration using exponentiated lie algebra) approach provided non-linear deformation to normalize the images to the DARTEL template in Montreal Neurological Institute (MNI) space, which was derived from 550 healthy control subjects (Ashburner, 2007). Non-brain tissue was also

#### **Table 1 | Characteristics of the subjects in this study.**


*Abbreviations: F, female; M, male.*

removed. Additionally, the Jacobian determinants derived from the spatial normalization were used to modulate the GM value for each voxel to preserve the total amount of GM from the original images (Good et al., 2001). We used non-linear components only, which allowed us to analyze relative (i.e., corrected for individual brain size) differences in regional GM volume. Finally, the resulting modulated and normalized images were smoothed with a 12 mm full width at half maximum isotropic Gaussian kernel.

## **EXTRACTION OF SEED VOLUMES**

To assess the structural covariance pattern of each large-scale network, we extracted individual GM volumes from eight seed regions of interest (ROIs). These ROIs included the primary visual, auditory, and motor cortex, as well as language-related speech and semantic areas, areas related to salience and executive control, and the default-mode network (DMN). We based our ROIs on previous studies (Zielinski et al., 2010; Montembeault et al., 2012). For each region, seeds were defined with the MarsBar ROI toolbox (http://marsbar.sourceforge.net/) as 4-mm radial spheres centered at the following MNI coordinates: right calcarine sulcus (9, −81, 7), right Heschl's gyrus (46, −18, 10), right precentral gyrus (28, −16, 66), left inferior frontal gyrus, pars opercularis (IFGo) (−50, 18, 7), left temporal pole (−38 10, −28), right frontoinsular cortex (38, 26, −10), right dorsolateral prefrontal cortex (DLPFC) (44, 36, 20), and right angular gyrus (46, −59, 23). In addition, we selected contralateral seeds by changing the sign on each seed's x coordinate to perform similar SCNs analyses (**Table A1**, and **Figure A1**).

## **STATISTICAL ANALYSIS**

A voxel-wise statistical analysis was performed on the GM images using the GLM as applied in SPM8. Eight multiple regression models were used to test the strength of the structural covariance between each seeds and all other regions across whole-brain GM for each age group. In each regression model, the extracted mean GM volume from each ROI was entered as a covariate of interest, and gender as a confounding covariate. Because of the unequal sample size across the genders, we removed the effects of gender on the SCN patterns in the correlation analysis. These statistical analyses identified voxels that had a positive covariance with each *a priori* selected ROI in each group. The resulting correlation maps were using height and extent thresholds at *P <* 0*.*05 with family-wise error (FWE) correction. They were displayed on the MNI template to allow qualitative comparisons between the age groups using BrainNet Viewer software (http://www.nitrc.org/projects/bnv/). To quantify differences in normal aging trajectories across networks, we calculated the total number of significant positive ipsilateral, contralateral, and whole-brain voxels and plotted these across the age groups.

To further examine the effects of age on specific regional covariance, we performed between-group difference analyses of SCN patterns for any two groups according to the scientific literature (Lerch et al., 2006). For any pair of voxels in two groups, their structural correlation may have different slopes, and the difference in slope may represent the difference in their structural association. The difference in slope was tested using a classic interaction linear model:

$$V\_i = \beta \alpha + \beta\_1 V\_j + \beta\_2 \text{Group} + \beta\_3 (V\_j \times \text{Group}) + \varepsilon\_i$$

where *Vi* and *Vj* represented the GM volumes of a pair of voxels in two groups. The Group component was modeled using treatment contrasts, and significance tested using the Student's *t* statistic. Specific *t*-value contrasts were established to map the voxels that expressed a significantly different structural association between any two groups. The threshold for the resulting statistical parametric maps was given at *P <* 0*.*05 for the height and extent thresholds, with FWE multiple comparisons correction.

## **RESULTS**

For each network, the age-related SCN trajectory was identified by the spatial extent of each SCN. These results were demonstrated by functional domain, as described in the following sections. We also explored regions that significantly differed in their structural associations between any two groups.

## **PRIMARY SENSORY AND MOTOR NETWORKS**

Seeds within primary visual cortex (right calcarine sulcus) produced SCNs with a relatively preserved pattern (**Figure 1**). However, there were small changes in the distributions of the covariance maps. In the young and middle-aged groups, the covariance regions mainly included the bilateral calcarine sulcus, lingual gyrus, cuneus and right lateral occipital gyrus, whereas only included bilateral calcarine sulcus, lingual gyrus, and cuneus in the old group. Primary auditory cortex (i.e., the right Heschl's gyrus) covaried with the bilateral insula and precuneus, the right posterior cingulate cortex (PCC), parahippocampus, and inferior frontal gyrus, and the left orbital-frontal cortex in the young participants, and underwent significant contraction in the middle-aged and old groups to include only the bilateral insula regions. There was a flat transition in the covariance maps during aging. Primary motor cortex (right precentral gyrus) correlated with the ipsilateral precentral regions in the young participants, which progressed at middle age to include the contralateral precentral and supplementary motor areas. In old participants, these structural associations were similar to the young participants.

In addition, within the primary sensory and motor networks, there were no significant differences observed in the structural association between any two groups when we compared the slopes of the structural associations on a voxel-by-voxel basis.

## **LANGUAGE-RELATED SPEECH AND SEMANTIC NETWORKS**

The two language-related speech and semantic networks followed similar variation patterns (**Figure 2**). From the young participants to the middle group, the covariance regions showed abrupt decreases. They then showed smaller changes at the following ages. For the language-related speech network, the covariance map involved the bilateral anterior insula, medial frontal, cingulate cortex, and temporal regions in the young participants, and then contracted to include seed autocorrelation in the older participants. The semantic regions correlated with the bilateral

**covariance networks. (A)** Statistical maps of regions significantly correlated with the seed region in each group. The results are presented as correlation coefficient values (*P <* 0*.*05, FWE corrected). **(B)** The plots of voxel counts by group indicate small covariance changes throughout the life-span. The y-axis represents the voxel number (×104). Abbreviations: Calcs, calcarine sulcus; HG, Heschl's gyrus; PreCG, precentral gyrus; R, right; L, left; Y, young group; M, middle-aged group; O, old group; CC, correlation coefficient.

temporal cortices, cingulate gyrus, insula and frontal regions in the young participants, and shrank sharply to include only the anterior temporal cortices during normal aging.

Comparing the other SCNs, language-related speech and semantic networks showed more age-related changes. Specifically, the left supplementary motor and superior temporal areas showed significant positive associations with the speech seed region in young adults, whereas this covariance disappeared in the middle-aged group (**Table 2** and **Figure 3**). Decreased positive associations between the left superior temporal region and left temporal pole were found in the middle-aged group compared with the young group (**Table 2** and **Figure 4**). There were


**Table 2 | Contrast analysis of structural covariance network trajectories.**

*The regions listed showed significant between-group differences (P < 0.05, FWE corrected). x, y, z coordinates are reported in standard MNI space. Abbreviations: DMN, default-mode network; ANG, angular gyrus; IFGo, inferior frontal gyrus, pars opercularis; TPole, temporal pole; DLPFC, dorsolateral prefrontal cortex; PFC, prefrontal cortex; PCC, posterior cingulate cortex; ACC, anterior cingulate cortex; SMA, supplementary motor area; STC, superior temporal cortex; SFC, superior frontal cortex; OFC, orbitofrontal cortex; R, right; L, left; Y, young group; M, middle-aged group; O, old group; BA, Brodmann area.*

significant positive associations between the left anterior and the PCC and left IFG in the young participants, and this covariance became a negative correlation in the old group (**Table 2** and **Figure 4**). When compared with the old group, there was a significant positive association between the right PCC and the semantic seed region in the young participants (**Table 2** and **Figure 5**).

#### **SALIENCE, EXECUTIVE CONTROL, AND DEFAULT-MODE NETWORKS**

The three networks associated with social–emotional and cognitive function showed similar patterns to the language-related speech and semantic networks, with more distributed structural covariance in the young participants and limited covariance patterns throughout the later stages (**Figure 5**). Specifically, the right frontoinsular cortex anchored covariance maps included extensive areas of the bilateral lateral and medial frontal cortex, temporal cortices, and cingulate regions. This SCN underwent significant shrinkage in the middle-aged group to include only bilateral insular regions. In the older subjects, this network somewhat extended to include the bilateral medial prefrontal regions. The right DLPFC seed covaried with the bilateral frontal, temporal, and cingulate cortices in the young participants, but shrank into a more focal distribution in the middle-aged and older subjects. A seed in right angular gyrus produced an aging SCN, including the bilateral angular gyrus, middle temporal, cingulum, and prefrontal regions in the young participants, and contracting to include the bilateral PCC and angular gyrus in the middle-aged group, and only the bilateral angular gyrus in the old group.

There were no significant differences observed in the structural associations between any two groups within the salience network. The left PCC showed a significant positive association with the executive control seed region and right angular region (DMN seed) in the young adults, whereas this covariance disappeared in the old group (**Table 2** and **Figure 5**). Furthermore, a less robust positive association between the right prefrontal region and DMN seed region was found in the middle-aged group compared with the young participants (**Table 2** and **Figure 4**).

## **DISCUSSION**

In this study, we investigated the age-related trajectories of eight large-scale networks using an SCN mapping approach in three distinct age groups. All networks exhibited non-linear patterns across normal aging in terms of the spatial extent of the network. Except for the primary motor network, these networks showed a more distributed topology in young participants, which shrank sharply to a more localized topology in the middle-aged group, and maintained this localized topology in the old group. Primary sensory and motor networks showed fewer age-related changes compared with high-order cognitive networks. Moreover, there were significant between-group differences in language-related speech and semantic networks, the executive control network, and the DMN. Taken together, our results provide evidence of variations in the topological organization of SCNs during normal aging.

**FIGURE 3 | Age-related changes in salience, executive control, and default-mode structural covariance networks. (A)** Statistical maps of regions significantly correlated with the seed region in each group. The results are presented as correlation coefficient values (*P <* 0*.*05, FWE corrected). **(B)** The plots of voxel counts by group indicate abrupt contraction in the middle-aged group and mild changes in the old group. The y-axis represents the voxel number (×104). Abbreviations: DMN, default-mode network; DLPFC, dorsolateral prefrontal cortex; FI, frontoinsular cortex; ANG, angular gyrus; R, right; L, left; Y, young group; M, middle-aged group; O, old group; CC, correlation coefficient.

**FIGURE 4 | Group differences for the young vs. the middle-aged subjects.** Significant between-group differences within the DMN **(A)**, speech **(B)**, and semantic **(C)** structural covariance networks were found. For each network, the region of interest (upper) and region showing the most significant structural association (lower) are presented on the right, and a plot of slop differences between the seed region and a 4-mm radius

sphere centered on the peak voxel are presented in the left. Voxels with *P*FWE *<* 0*.*05 are displayed. Abbreviations: DMN, default-mode network; ANG, angular gyrus; IFGo, inferior frontal gyrus, pars opercularis; TPole, temporal pole; PFC, prefrontal cortex; SMA, supplementary motor area; STC, superior temporal cortex; R, right; L, left; Y, young group; M, middle-aged group.

## **COMPARISON OF STRUCTURAL COVARIANCE NETWORKS AND OTHER NETWORKS**

The cerebral cortex is organized into networks of functionally complementary areas. New advances in modern neuroimaging techniques and quantitative analysis of complex networks have made the investigation of brain network topological organization possible. DTI is a useful tool for non-invasively mapping cortico-cortical anatomical connections by examining axonal integrity. Insight into the structural covariance of GM morphology (e.g., volume, thickness, and surface area) between brain regions is provided by sMRI. Resting-state fMRI has allowed for assessments of the strength of functional connections within a network by quantifying correlated activity [i.e., spontaneous, low-frequency fluctuations in the blood oxygen level-dependent (BOLD) signal] between brain regions at rest.

The current studies have demonstrated the underlying relationships among structural correlations, anatomical connectivity, and functional correlations. For example, structural networks based on cortical thickness measurements were consistent with known functional networks (He et al., 2007). A recent study

reported agreement in the correlations in GM thickness and underlying fiber connections across brain areas, but more information was included for the thickness correlation network than the fiber connection network (Gong et al., 2012). In addition, SCNs determine using VBM recapitulated the canonical intrinsic connectivity networks topologies (Seeley et al., 2009). The consistency among the SCNs and other networks provides substantial support for the use of SCNs as a measure of network integrity for cross-sectional studies.

## **AGE-RELATED CHANGING TRAJECTORY OF SCNs**

In this study, we employed three age groups to map the trajectory of SCN changes over age. Except the primary motor network, these SCNs appeared the similar non-linear pattern that had a distributed topology in young participants, a sharply localized topology in middle aged participants, and were relatively stable in older participants. This trajectory was similar to agerelated changes of integrated local efficiency of brain network (Wu et al., 2011b). They employed the graph theory analysis method and reported that the local efficiency in the young group was significantly larger than those of the middle and old groups, whereas no significant difference was found between the middle and old groups. The shrinkage of SCNs as well as the reduction of local efficiency might be explained by the regionally distributed pattern of GM atrophy (Bergfield et al., 2010). As for primary motor network, we found inverted V-curve tendency among three age groups in the SCN of primary motor cortex. The previous study reported increased inter-regional correlations between bilateral primary motor cortex in the aging brain (Chen et al., 2011). In our study, we found the structural covariance of primary motor cortex between two hemispheres in the middle group. Alternatively, we found that the volume of right precentral gyrus reduced with age increasing (**Figure A2**), which might reflect a compensation mechanism that a network may need to work harder by becoming overactive due to regional volumetric atrophy with the network (Reuter-Lorenz and Cappell, 2008).

## **INTRAGROUP PATTERNS AND BETWEEN-GROUP DIFFERENCES OF SCNs**

## *Primary sensory and motor networks*

In this study, we found that seeds within the primary visual, auditory, and motor cortices produced SCNs with smaller spatial extents (i.e., total voxel count exhibiting a significant correlation at the corrected threshold) compared with other networks. This is consistent with previous work (Lerch et al., 2006) with mapped anatomical correlations across cerebral cortex (MACACC) using cortical thickness. Similarly, they used the number of cortical region showing significant correlations with seed region to describe the strength of MACACC. They found the primary motor, sensorimotor, and visual areas had the lowest strengths of correlation, whereas the association cortices had the highest strength. This could be explained by the functions of association cortices, because these cortices receive and integrate inputs from multiple cortical and non-cortical sources, and distribute information to multiple areas.

In addition, primary sensory and motor networks showed fewer age-related changes compared with high-order cognitive networks. There were no significant differences observed in the structural association between any two groups when the slopes of the structural associations were contrasted on a voxel-by-voxel basis within the primary sensory and motor networks. This finding is in line with a recent study (Montembeault et al., 2012) in which between-group differences were only observed in high-order cognitive SCNs between young and old subjects, rather than primary sensory and motor networks.

#### *Language-related speech and semantic networks*

The language-related speech and semantic networks showed significant age-related changes, with a more localized topology with increasing age. These results could account for the decline of language abilities across in normal aging. Federmeier and colleagues observed age-related changes in the timing with which messagelevel information impacted semantic analysis using event-related potentials, which indicated that aging affects high-order language processes (Federmeier et al., 2003). Wierenga and colleagues also reported that healthy older adults had increased difficulty in word retrieval with unchanged semantic knowledge (Wierenga et al., 2008).

Within the language-related speech SCN, a reduced structural association was observed the between inferior frontal gyrus and the supplementary motor area in the middle-aged group compared with the young participants. The speech SCN linked the language and motor systems that enable speech fluency (Seeley et al., 2009). This reduced structural covariance may explain the decline of motor speech skills with the transference of language to a speech code in older adults. Similarly, there was a significant decrease in the covariance between the seed region and the anterior cingulate cortex (ACC) in the old group compared with the young participants. This could be related to the functional degradation of these areas. Takahashi et al. reported that significant age-related reductions in regional cerebral blood flow in the left IFG and the bilateral medial frontal gyri and ACC using single-photon emission tomography (Takahashi et al., 2005). In addition, as the ACC plays a critical role in the control of speech responses (Paus et al., 1993), this reduced covariance may explain some speech difficulties in senior populations.

In semantic SCN analysis, a reduced structural association between the left temporal pole and left superior temporal cortex was found in the middle-aged group compared with the young participants. Many studies have shown that the superior temporal cortex is associated with understanding spoken words (Demonet et al., 1992; Chao et al., 2002; Okada and Hickok, 2006). Thus, this reduced structural association may be related to a decline in auditory word comprehension with increasing age. In the comparisons of the young and old participants, the main differences we found were in the structural associations between the left temporal pole and PCC. The results of functional neuroimaging studies in young, healthy adults provide compelling evidence for the involvement of the PCC in memory retrieval. For example, PCC activation was elicited during recognition of thematic narrative information learned during training sessions (Maguire et al., 1999). The reduced structural association between these two regions may explain the decline of semantic memory in senior populations.

### *Salience, executive control, and default-mode networks*

The three high-order cognitive SCNs showed a similar pattern to the language SCNs, showing a distributed topology in young participants that shrank sharply to a localized topology in the middle-aged group, and maintained a localized topology in the old group. Cepeda and colleagues found agerelated decline of executive control processes by examining taskswitching performance (Cepeda et al., 2001). Kelly and colleagues also demonstrated the stability of executive control decreased in older subjects (West et al., 2002). Several studies have shown that the DMN is altered in old subjects (Andrews-Hanna et al., 2007; Koch et al., 2010; Sambataro et al., 2010), especially in anterior regions (Damoiseaux et al., 2008). Similarly, Chen et al. found the decreased inter-regional correlations with the DMN module (Chen et al., 2011). Cognitive decline with age, such as cognitive control (Persson et al., 2007) and working memory (Sambataro et al., 2010), is associated with decreased DMN connectivity.

In the executive-control SCN analysis, the main differences we found were in the structural association between the right DLPFC and PCC, which exhibited a positive correlation in the young participants and no association in the old group. The DLPFC shows increased activity when experimental stimuli are presented, and is thought to support on-task processing in attention tasks. The PCC, in contrast, shows decreased activity during stimulus presentation, and is thought to support offtask processing in attention tasks. Some studies have reported that activity in the DLPFC shows a negative correlation with activity in the PCC during the resting state (Greicius et al., 2003; Fransson, 2005), and the negative coupling between these regions is lower in older adults (Sambataro et al., 2010). Our findings are inconsistent with functional connectivity between these areas, which should be further investigated in future studies.

Finally, for the DMN, we found a reduction in the structural association between the right angular gyrus and right prefrontal cortex from the young to the middle-aged participants. In the comparison of the young and old group, we found age-related differences in the structural association between the right angular gyrus and PCC. Similarly, Wu and colleagues reported that the left angular gyrus has a significantly reduced correlation with the PCC in older compared with younger participants in a resting-state fMRI study (Wu et al., 2011a). As the PCC is a prominent region within the DMN, the reduced structural association between the seed regions and the PCC indicates that the DMN SCN shrinks with age.

#### **LATERALITY OF THE SCNs**

We found similar SCNs across both hemispheres (**Figure A1**), consistent with previous SCN analyses of the developing brain (Zielinski et al., 2010). However, the auditory and speech networks showed a notable exception. The SCN derived from the left seed of Heschl's gyrus was distinctly smaller than the one derived from the right seed. In contrast, the SCN derived from the left seed of the IFGo was distinctly larger than the one derived from the right seed, which may explain why speech networks are left-dominated (Powell et al., 2006; Xiang et al., 2010).

## **FURTHER CONSIDERATIONS**

To build upon this study, several issues need to be addressed. First, we assigned subjects in their 30s to the middle-aged group because there were fewer subjects in their 40s and 50s in our sample. This resulted in wide age range in the middle-aged group. In future, this age range should be refined by including more subjects. Second, a previous study has reported the brain functional connectivity pattern could be affected by the sex factor (Biswal et al., 2010). In future, it is interesting to explore the sexual differences when the SCNs change with age. Third, it should be highlighted that this study used ROIs based on previous studies (Zielinski et al., 2010; Montembeault et al., 2012). Future studies

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using ROIs extracted from ICNs of the same subjects may obtain more accurate definitions of seed regions.

## **CONCLUSIONS**

In this study, we used an SCN mapping approach to investigate eight large-scale networks in a large cohort of healthy participants. Our results show age-related changes in the topological organization of SCNs and provide insights into the normal aging process of the human brain.

## **ACKNOWLEDGMENTS**

This work was supported by the National Science Foundation of China (Nos. 81171403, 81071212, and 30700182) and the State Key Laboratory of Software Development Environment (No. SKLSDE-2011ZX-10).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 26 December 2012; accepted: 08 March 2013; published online: 26 March 2013.*

*Citation: Li X, Pu F, Fan Y, Niu H, Li S and Li D (2013) Age-related changes in brain structural covariance networks. Front. Hum. Neurosci. 7:98. doi: 10.3389/fnhum.2013.00098*

*Copyright © 2013 Li, Pu, Fan, Niu, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## **APPENDIX**


#### **Table A1 | Contrast analysis of structural covariance network trajectories derived from contralateral seeds.**

*The regions listed showed significant between-group differences (P < 0.05, FWE corrected). x, y, z coordinates are reported in standard MNI space. Abbreviations: DMN, default-mode network; ANG, angular gyrus; IFGo, inferior frontal gyrus, pars opercularis; TPole, temporal pole; FI, frontoinsular cortex; HG, Heschl's gyrus; IFC, inferior frontal cortex; PCC, posterior cingulate cortex; ITC, inferior temporal cortex; FG, fusiform gyrus; OFC, orbitofrontal cortex; ACC, anterior cingulate cortex; R, right; L, left; Y, young group; M, middle-aged group; O, old group; BA, Brodmann area.*

**FIGURE A1 | Trajectories of the structural covariance networks derived from contralateral seeds.** The plots of voxel counts by group (*P <* 0*.*05, FWE) indicate a similar pattern to the ipsilateral seed, except for the auditory and speech networks, which show differences in their spatial extents. The y-axis represents the voxel number (×104).

Abbreviations: Calcs, calcarine sulcus; HG, Heschl's gyrus; PreCG, precentral gyrus; IFGo, inferior frontal gyrus, pars opercularis; TPole, temporal pole; ANG, angular gyrus; DLPFC, dorsolateral prefrontal cortex; FI, frontoinsular cortex; R, right; L, left; Y, young group; M, middle-aged group; O, old group.

# **HUMAN NEUROSCIENCE**

## A longitudinal study of structural brain network changes with normal aging

## *Kai Wu1,2\*, Yasuyuki Taki 1,3,4, Kazunori Sato1, Haochen Qi 2, Ryuta Kawashima4,5,6 and Hiroshi Fukuda1*

*<sup>1</sup> Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan*

*<sup>2</sup> Department of Biomedical Engineering, School of Materials Science and Engineering, South China University of Technology, Guangzhou, China*

*<sup>3</sup> Division of Medical Image Analysis, Department of Community Medical Supports, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan*

*<sup>4</sup> Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan*

*<sup>5</sup> Smart Ageing International Research Centre, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan*

*<sup>6</sup> Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Frini Karayanidis, University of Newcastle, Australia Gaolang Gong, Beijing Normal University, China*

#### *\*Correspondence:*

*Kai Wu, Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Seiryo-Machi 4-1, Smart Ageing Research Building, Sendai 980-8575, Japan. e-mail: kai.wu.22@gmail.com*

The aim of this study was to investigate age-related changes in the topological organization of structural brain networks by applying a longitudinal design over 6 years. Structural brain networks were derived from measurements of regional gray matter volume and were constructed in age-specific groups from baseline and follow-up scans. The structural brain networks showed economical small-world properties, providing high global and local efficiency for parallel information processing at low connection costs. In the analysis of the global network properties, the local and global efficiency of the baseline scan were significantly lower compared to the follow-up scan. Moreover, the annual rate of change in local and global efficiency showed a positive and negative quadratic correlation with the baseline age, respectively; both curvilinear correlations peaked at approximately the age of 50. In the analysis of the regional nodal properties, significant negative correlations between the annual rate of change in nodal strength and the baseline age were found in the brain regions primarily involved in the visual and motor/control systems, whereas significant positive quadratic correlations were found in the brain regions predominately associated with the default-mode, attention, and memory systems. The results of the longitudinal study are consistent with the findings of our previous cross-sectional study: the structural brain networks develop into a fast distribution from young to middle age (approximately 50 years old) and eventually became a fast localization in the old age. Our findings elucidate the network topology of structural brain networks and its longitudinal changes, thus enhancing the understanding of the underlying physiology of normal aging in the human brain.

**Keywords: structural brain network, economical small-world, normal aging, longitudinal study, regional gray matter volume**

## **INTRODUCTION**

Recent advances in generating a network map of the human brain, known as the human connectome, provided new insights into structural and functional connectivity patterns of the human brain (Sporns et al., 2005; Bullmore and Bassett, 2011; Sporns, 2011a,b). The quantitative analysis of the structural and functional systems of the human brain, based largely on graph theory, reveal the topological properties of complex networks, such as economical small-world properties, highly connected hubs, and modularity (Bullmore and Sporns, 2009; He and Evans, 2010; Wig et al., 2011). Prodigious efforts in the study of the human connectome have greatly expanded our knowledge of the topological principles of brain network organization in the healthy, developing, aging, and diseased brains (Bassett and Bullmore, 2009; Uddin et al., 2010; Lo et al., 2011; Xia and He, 2011; Xie and He, 2011; Greicius and Kimmel, 2012; Sun et al., 2012).

It has been well-established that advanced aging is accompanied by cognitive decline, even in the absence of disease. Cognitive deficits in normal aging might arise from anatomical changes in specific brain regions or alterations of the structural and functional associations between distinct brain regions (Andrews-Hanna et al., 2007). Normal aging has been proven to be associated with changes in both functional (Achard and Bullmore, 2007; Meunier et al., 2009; Wang et al., 2010, 2012; Meier et al., 2012; Spreng and Schacter, 2012) and structural (Gong et al., 2009; Montembeault et al., 2012; Wu et al., 2012; Zhu et al., 2012) brain networks. However, these findings were revealed by cross-sectional studies, and few studies using a longitudinal design have been applied to investigate human brain networks with normal aging. Several Alzheimer's disease Neuroimaging Initiative (ADNI) studies have shown longitudinal changes in default mode network (DMN) regions, including the medial temporal lobe and posterior cingulate cortex (PCC), as patients progress into Alzheimer's disease (AD) and through its later stages (Risacher et al., 2010; Li et al., 2012). Thus, we hypothesized that significant longitudinal changes might occur in the topological properties of structural brain networks with normal aging.

By applying a longitudinal design over 6 years in a large number of healthy subjects aged 21–80, our previous studies have indicated the following: significant correlations between the annual percentage change in the ratio of gray matter and the age at baseline (Taki et al., 2011a), as well as significant correlations between the annual rate of regional gray matter volume change in many brain regions and the age at baseline (Taki et al., 2012a). In the present study, we aimed to investigate structural brain networks with normal aging by applying the above-mentioned longitudinal design. Structural brain networks have been constructed from inter-regional correlation of morphological measurements [e.g., cortical thickness (He et al., 2007), regional gray matter volume (RGMV) (Bassett et al., 2008), and surface area (Sanabria-Diaz et al., 2010)] in structural magnetic resonance imaging (sMRI) data. Recently, many studies have investigated the topological organization of structural brain networks in health [e.g., healthy subjects with normal aging (Montembeault et al., 2012; Wu et al., 2012; Zhu et al., 2012)] and disease [e.g., AD (He et al., 2008), multiple sclerosis (He et al., 2009), schizophrenia (Bassett et al., 2008), and breast cancer (Hosseini et al., 2012)]. In this study, we divided 380 healthy subjects into 29 age-specific groups using a sliding boxcar grouping ordered by baseline age. A structural brain network consisting of 90 regions was constructed by computing the correlation matrix of the RGMV across subjects within each age group in both the baseline and follow-up scans. We then computed both global and regional network properties in the structural brain networks and compared their differences between baseline and follow-up. Finally, to characterize the longitudinal changes of structural brain networks with normal aging, the correlations between the baseline age and the annual rate of change in both global and regional network properties were analyzed.

## **MATERIALS AND METHODS**

#### **SUBJECTS**

The subjects were normal, community-dwelling Japanese subjects recruited by the Aoba Brain Imaging Project (Sato et al., 2003). Subject recruitment was described previously (Taki et al., 2011a,b,c, 2012a,b). Briefly, we performed longitudinal followup (Aoba2) scans of 442 subjects who were selected from 1604 participants in the baseline (Aoba1) scan. In both the baseline and follow-up scans, we excluded those subjects who had a past or present history of malignant tumors, head traumas, cerebrovascular diseases, epilepsy, or psychiatric diseases. After the interview, brain MR images were obtained from each subject. The MR images were inspected by 2–3 well-trained radiologists. Images with any of the following findings were excluded from this study: head injuries, brain tumors, hemorrhage, major and lacunar infarctions, or moderate to severe white matter hyperintensities. Thus, the final sample consisted of 380 participants (157 men/223 women). The mean ± standard deviation (SD) interval between baseline and follow-up was 7.41 ± 0.54 years (range, 6.1–9.0). The mean ± SD age of the participants at baseline was 51.1 ± 11.7 years old (range, 21–80).

A total of 11 subjects (mean age = 65.3 years; range, 57.7–73.4 years at follow-up; 3 men/8 women) were scanned twice on the same day to obtain an estimation of the measurement reliability. We observed no significant differences in the gray matter volume or intracranial volume between the baseline and follow-up scans. The details of the measurement reliability are reported elsewhere (Taki et al., 2011a).

After a full explanation of the purpose and procedures of the study, written informed consent according to the Declaration of Helsinki (1991) was obtained from each subject prior to MRI scanning. Approval for these experiments was obtained from the institutional review board of Tohoku University.

## **IMAGE ACQUISITION**

All images were collected using the same 0.5-T MR scanner (Signa contour; GE-Yokogawa Medical Systems, Tokyo, Japan) for both the baseline and follow-up studies. The scanner was routinely calibrated using the same standard GE phantom between baseline and follow-up. During the course of this study, no major hardware upgrade occurred. At baseline and follow-up, all subjects were scanned with identical pulse sequences: 124 contiguous, 1.5-mm-thick axial planes of three-dimensional T1-weighted images (spoiled gradient recalled acquisition in steady state: repetition time, 40 ms; echo time, 7 ms; flip angle, 30; voxel size, 1.02 mm × 1.02 mm × 1.5 mm).

## **MEASUREMENTS OF REGIONAL GRAY MATTER VOLUME**

After the image acquisition, the RGMV for each subject was measured using statistical parametric mapping 2 (SPM2) (Wellcome Department of Cognitive Neurology, London, UK) (Friston et al., 1995) in Matlab (MathWorks, Natick, MA). First, the T1-weighted MR images were transformed to the same stereotactic space by registering each of the images to the ICBM 152 template (Montreal Neurological Institute, Montreal, Canada), which approximates the Talairach space (Jean Talairach, 1988). Then, tissue segmentation from the raw images to the gray matter, white matter, cerebrospinal fluid space, and non-brain tissue was performed using the SPM2 default segmentation procedure. We applied these processes using the "cg\_vbm\_optimized" MATLAB function (http://dbm*.*neuro*.*uni-jena*.*de/vbm*.*html). WFU\_PickAtlas software was employed to label the regions in the gray matter images, providing a method for generating ROI masks based on the Talairach Daemon database (Lancaster et al., 2000; Maldjian et al., 2003, 2004). To calculate the regional gray matter volume (RGMV) for each subject, we parcellated the entire gray matter into 45 separate regions for each hemisphere (90 regions in total, see **Table 1**) defined by the Automated Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002).

#### **CONSTRUCTION OF STRUCTURAL BRAIN NETWORKS**

We applied the methodology described in our previous studies (Wu et al., 2011, 2012) to construct structural brain networks. Briefly, we computed a correlation matrix using the measurement of RGMV across a group of subjects. In this study, we created 29 age groups using a sliding boxcar grouping (Fair et al., 2009) in the order of baseline age (i.e., Group1: subjects 1–100, Group2: subjects 11–110, Group3: subjects 21–120, *...* Group 29: subjects 281–380). Similarly, 29 age groups in the follow-up scan were also created, which corresponded to the age groups in the baseline scan. For each age group, a linear regression



analysis was performed on the RGMV to remove the effects of the total gray matter volume, age, sex, and age-by-sex interaction. Thus, the residuals of this regression were employed as the substitute for the raw RGMV and denoted as the corrected RGMV (cRGMV). We then computed the Pearson correlation coefficient between cRGMV across 100 subjects included in one group to construct an interregional correlation matrix (*N* × *N*, where *N* is the number of gray matter regions; here, *N* = 90). Each element of the correlation matrix represents the structural connectivity between two regions. For example, the bilateral precentral gyrus (PreCG) showed strong correlations in Group 1 in both the baseline and follow-up scans (**Figure 1A**), indicating high connectivity between the same region in the bilateral hemispheres; however, the correlation between the left PreCG and the left opercular part of the inferior frontal gyrus (IFGoperc) in Group 1 was stronger in the follow-up scan compared to the baseline scan (**Figure 1B**). A correlation matrix (*rij*, *N* × *N*) can be converted to a weighted and undirected network *G* using a cost threshold approach (*t*, 0 *< t <* 1), which can normalize all networks to have the same number of edges or wiring cost and, thus, provide an avenue to detect changes in topological organization with aging (Achard and Bullmore, 2007).

$$G(i,j) = \begin{cases} 1, & \left| r\_{\vec{\eta}} \right| \ge r\_t \\ 0, & \left| r\_{\vec{\eta}} \right| < r\_t \end{cases}$$

Finally, we constructed a structural brain network for each of the 29 age groups in both the baseline and follow-up scans.

#### **GRAPH THEORETICAL ANALYSIS**

To ensure that the resulting brain networks are sparse, fully connected, and distinguishable from degree-matched random and regular networks, we adopted a range of cost thresholds (0*.*11 ≤ *t* ≤ 0*.*25, step = 0.01) to calculate the topological properties of structural brain networks (Bassett et al., 2008; Liu et al., 2008; Wang et al., 2009b; Wu et al., 2012). Small-world efficiency metrics (local efficiency, *LE*, and global efficiency, *GE*) were computed to characterize the global network properties of the structural brain networks. The node strength (*NS*) was used to examine regional nodal properties because of its high test-retest reliability (Wang et al., 2011). Here, both global and regional network metrics are briefly described as follows (Rubinov and Sporns, 2010) and were calculated using the Brain Connectivity Toolbox (www.brain-connectivity-toolbox.net).

The global efficiency of the graph *G* can be computed as (Latora and Marchiori, 2001):

$$GE(G) = \frac{1}{N(N-1)} \sum\_{i \neq j \in G} \frac{1}{d\_{ij}},$$

where *dij* is the shortest path length between nodes *i* and *j*. The path length between nodes *i* and *j* is defined as the sum of the edge lengths along this path, where each edge's length was obtained by computing the reciprocal of the edge weight, 1/*wij*. Thus, the shortest path length *dij* is the length of the path

with the shortest length between nodes *i* and *j*. The local efficiency of the graph *G* is defined as (Latora and Marchiori, 2001):

$$LE(G) = \frac{1}{N} \sum\_{i \in G} GE(Gi),$$

where *GE*(*Gi*) is the global efficiency of *Gi*, the subgraph of the neighbors of node *i*. The small-world efficiency metrics (*GE* and *LE*) of real brain networks were compared with 1000 random networks (*G*rand) that preserved the degree and weight distributions of real networks (Maslov and Sneppen, 2002). A real brain network is considered to be a small-world network if it shows similar global efficiency but much higher local efficiency than its matched random networks (Latora and Marchiori, 2001).

The node strength (*NSi*) for a given node *i* is defined as the sum of all of the edge weights between this node and all of the other nodes in the network. Regions with a high nodal strength indicate high interconnectivity with other regions.

Regarding the structural brain network for each age group, we averaged the global and regional network metrics (*LE*, *GE*, and *NS*) over the range of cost thresholds (0*.*11 ≤ *t* ≤ 0*.*25) to obtain the summary network metrics (Bassett et al., 2008). To investigate the longitudinal changes of network properties, the annual rate of change in the summary network metrics (*ARC\_X*) was defined as:

$$ARC\\_X = \frac{X\_2 - X\_1}{\text{Age}\_2 - \text{Age}\_1},$$

where *X*<sup>1</sup> and *X*<sup>2</sup> are the summary network metrics at baseline and follow-up, respectively; and Age1 and Age2 are the mean age of 100 subjects included in the age group at baseline and followup, respectively. The *ARC\_X* value indicates the differences in

regions (*i* and *j*) using the measurement of regional gray matter volume, which was corrected by a linear regression analysis to remove the effects of total gray matter volume, age, sex, and age-by-sex interaction. The data from both the baseline (Aoba1, *N* = 1) and follow-up (Aoba2, *N* = 2) scans are shown.

summary network metrics between the baseline and follow-up scans, normalized by the interval of age.

#### **STATISTICAL ANALYSIS**

To analyze the differences in the summary global network properties (e.g., *LE* and *GE*) of the same age group between two scans (e.g., Group 1 at baseline vs. Group 1 at follow-up), a non-parametric permutation test method was applied (Bullmore et al., 1999; He et al., 2008; Wu et al., 2012). Moreover, a paired *t*-test was performed to determine whether there were significant longitudinal changes in each summary network metric (*LE*, *GE*, and *NS*) between all age groups at baseline and those at followup. To evaluate correlations between the longitudinal changes in network properties and the baseline age, we performed multiple linear regression analyses with the annual rate of change in the summary network metrics as the dependent variables and the baseline age as the independent variable. Here, three multiple linear regressions (Model I, II, and III) modeling mean value, age, age2, and age3 as predictors were applied to detect the linear, quadratic, and cubic changes with the baseline age. We then determined the best model among the three regressions using Akaike's information criterion (AIC) (Akaike, 1974).

$$ARC\\_X = \text{mean} + a \times \text{Age}\_1 + e \tag{I}$$

$$ARC\\_X = \text{mean} + a\_1 \times \text{Age}\_1 + a\_2 \times \text{Age}\_1^2 + e \tag{11}$$

$$\text{ARC\\_}X = \text{mean} + a\_1 \times \text{Age}\_1 + a\_2 \times \text{Age}\_1^2$$

$$+ a\_3 \times \text{Age}\_1^3 + e\tag{\text{III}}$$

For the regression analysis of regional nodal property, we only included regions with significant differences in the summary regional network metric (e.g., *NS*) between the baseline and follow-up scans by the paired *t*-test (*p <* 0*.*05, FDR-corrected).

## **RESULTS**

## **ECONOMICAL SMALL-WORLD PROPERTIES AND LONGITUDINAL CHANGES**

The structural brain networks of the age-specific groups exhibited economical small-world properties, showing higher local efficiency but similar global efficiency compared to the matched random networks (Latora and Marchiori, 2001). This finding is illustrated in **Figure 2**, where we plot the local and global efficiency of the structural brain networks of the age-specific groups from both the baseline and follow-up scans against those of the matched random networks. Moreover, significant differences (a non-parametric permutation test; *p <* 0*.*05) in the summary local efficiency were found in several age groups across the baseline age but those in the summary global efficiency were found in the middle age groups (**Figure 3**). For all age groups, the structural brain networks from the baseline scan showed significantly lower local efficiency (a paired *t*-test; *t*-value = 8.446; *p <* 10<sup>−</sup>4) and global efficiency (a paired *t*-test; *t*-value = 10.478; *p <* 10<sup>−</sup>4) compared to those from the follow-up scan. The annual rate of change in local efficiency (*ARC\_LE*) and global efficiency (*ARC\_GE*) showed a positive quadratic (*F*-value = 3.622, *p* = 0*.*041) and a negative quadratic (*F*-value = 3.506, *p* = 0*.*045) correlation with the baseline age, respectively (**Figures 3A**,**B**). The curvilinear correlations peaked at the baseline ages of 45.49 years and 50.95 years, respectively.

#### **REGIONAL NODAL PROPERTIES AND LONGITUDINAL CHANGES**

We found significant correlations between the annual rate of change in node strength (*ARC\_NS*) and the baseline age in many

## **DISCUSSION**

To our knowledge, this is the first study to investigate longitudinal changes in the topological organization of structural brain networks in a large number of healthy individuals. We found that the structural brain networks of age-specific groups exhibit economical small-world properties. *ARC\_LE* and *ARC\_GE* showed

(*ARC\_LE*) and the baseline age (Age*1*), peaked at the baseline age of 45.49 years. **(B)** Significant negative quadratic correlations between the

**Table 2 | Significant negative linear correlation between the annual rate of change in node strength and the baseline age.**


*The level of significance was set at p < 0.05.*

significant curvilinear correlations with the baseline age, with a peak at the baseline age of approximately 50. Our results also revealed significant correlations between the *ARC\_NS* and the baseline age in many brain regions. Structural brain networks develop into a more distributed organization from young to middle age (approximately 50 years old) and then achieve a localized organization with substantial alterations in old age. Thus, revealing longitudinal changes in the topological properties of structural brain networks may enhance our understanding of the physiology underlying normal aging in the human brain.

## **ECONOMICAL SMALL-WORLD PROPERTIES AND LONGITUDINAL CHANGES**

In this study, the structural brain networks derived from measurements of RGMV in all age-specific groups exhibited the key

annual rate of change in global efficiency (*ARC\_GE*) and the baseline age, peaked at the baseline age of 50.95 years. Note that significant differences (*p <* 0*.*05) in the summary global network properties of the same age group between two scans by the nonparametric permutation test are indicated by violet stars.

properties of economical small-world organization. An economical small-world network can provide a topological substrate for both locally specialized processing in the neighborhoods of highly clustered nodes and globally distributed processing on a highly efficient network with short characteristic path lengths (Sporns and Zwi, 2004; Stam, 2004; Achard et al., 2006; Achard and Bullmore, 2007). Our finding of high global and local efficiency in the structural brain networks is consistent with the results of previous functional and structural brain networks studies (He et al., 2007, 2008, 2009; Bassett et al., 2008, 2009; Wang et al., 2009b; Khundrakpam et al., 2012; Wu et al., 2012; Zhu et al., 2012).

We also noted longitudinal changes in small-world efficiency metrics of the structural brain networks. Several age groups in the follow-up scan showed significant higher values in local or global efficiency compared to those in the baseline scan. Moreover, the differences in both local and global efficiency between two scans varied across the age groups and showed significant correlations with the baseline age. *ARC\_LE* and *ARC\_GE* showed a U-curve and an inverted-U curve trajectory with the baseline age, respectively. In particular, the trajectories of *ARC\_LE* and *ARC\_GE* peaked at a baseline age of 45.49 and 50.95 years, respectively. These results are consistent with our previous cross-sectional study findings, in which the local and global efficiency showed U-curve and inverted-U curve tendencies, respectively, in young (18–40 years), middle (41–60 years), and old age (61–80 years) groups (the subjects used were from the same dataset of the baseline scan in this study) (Wu et al., 2012).

The longitudinal changes in local and global efficiency could be divided into two processes based on the peaks. First, the period from young to middle age (approximately 50 years old) showed

decelerated increases in local efficiency and accelerated increases in global efficiency, indicating a fast distribution in the middle age. This period might reflect a maturation process in the structural brain network. A previous study demonstrated that the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more distributed architecture in young adults, indicating the maturation process of the functional systems (Fair et al., 2009). A more recent study on



*The level of significance was set at p < 0.05.*

structural brain networks constructed from the measurement of cortical thickness also indicated a more distributed configuration in late childhood, accompanied by significant increases in global efficiency but decreases in local efficiency (Khundrakpam et al., 2012). In addition, white matter plays a vital role in the efficient transfer of information between gray matter regions. Our previous longitudinal study of a large number of healthy subjects (the same datasets of both the baseline and follow-up scans in this study) demonstrated that the white matter ratio increased until approximately age 50 and then decreased in both men and women (Taki et al., 2011a). Several previous studies also indicated that white matter volume seems to increase until the middle age of approximately 45 years and decrease thereafter (Bartzokis et al., 2001; Sowell et al., 2003). Increases in the white matter represent maturational changes, such as myelination that continue until middle adulthood and may, therefore, provide evidence of the maturation of structural brain networks.

Second, the period from middle (approximately 50 years old) to old age showed an accelerated increase in local efficiency and a decelerated increase in global efficiency, leading to a fast localization in the old age. The changes over this period might reflect a degenerative process in the structural brain network with advanced aging. A recent study demonstrated that the structural brain networks in an older cohort (mean age = 66.6 years, range 64–68) had lower global efficiency but higher local efficiency, revealing a more localized configuration compared to the younger cohort (mean age = 46.7 years, range 44–48) (Zhu et al., 2012). Using a sample of 342 healthy individuals aged 72–92 years, a previous DTI tract-derived connectivity study indicated that the global efficiency of the structural brain networks decreased significantly with older age (Wen et al., 2011). It is important to note that a regular configuration with less global integration upsets the optimal balance of a small-world network and is related to many neurological and psychiatric disorders described as dysconnectivity syndromes (Catani and ffytche, 2005). Several previous studies have reported a regular configuration or a reduction in the global efficiency of brain networks in patients with diseases such as AD and amnestic mild cognitive impairment (aMCI, the prodromal stage of AD), providing further support for the characterization of AD and aMCI as dysconnectivity syndromes and indicating the functional basis of cognitive deficits (Stam et al., 2007; He et al., 2008; Bai et al., 2012; Zhao et al., 2012; Wang et al., 2013). Therefore, we speculate that advanced aging is associated with a high risk for dysconnectivity syndromes.

## **REGIONAL NODAL PROPERTIES AND LONGITUDINAL CHANGES**

Node strength measures the interconnectivity of a node with other regions and can be used to determine the relative importance of a node within a network. We identified significant correlations between the *ARC\_NS* and the baseline age in many brain regions, mainly consisting of recently evolved association (9/17) and primitive limbic/paralimbic (5/17) regions. Association regions contribute to the integrity of multiple functional systems such as the attention and memory systems, while limbic/paralimbic regions are highly interconnected with the prefrontal regions and subcortical regions and are mainly involved in emotional processing and the maintenance of a conscious state of mind (Mesulam, 1998). Thus, our results support the view that age-related changes are mainly a characteristic of the association cortex rather than the primary cortex (Albert and Knoefel, 1994).

The brain regions showing significant negative correlations with the baseline are primarily involved in the visual and motor/control systems. A previous study of the structural brain networks in elderly subjects using DTI data demonstrated significant positive correlations between the regional nodal efficiency and visuospatial, processing speed, and executive functions in many cortical regions (Wen et al., 2011). Therefore, we speculate that our findings of the decreases of *ARC\_NS* with the baseline age in the visual and motor/control systems might be related to the decline of these functions with normal aging. It is well-known that visual abilities decline during normal (non-pathological) aging, and older individuals tend to have reduced visual acuity and contrast sensitivity (Spear, 1993; Owsley, 2011). A recent study using event-related potentials (ERPs) also found that visual acuity declined as a function of age when young adults (18–32 years), young–old adults (65–79 years), and old–old adults (80+ years) performed a visual processing task involving selective attention to color (Daffner et al., 2012). Moreover, normal aging-related degeneration in the brain is accompanied by reduced force control, progressive slowness, and impaired motor ability (Roos et al., 1997; Smith et al., 1999; Krampe, 2002). Worsened task performance (e.g., slower speed with increasing memory load) in old adults (mean age = 71.27) is associated with decreases in the functional network connectivity between components comprising the supplementary motor area and the middle cingulate gyrus and between the precuneus and the middle/superior frontal cortex (Steffener et al., 2012). A previous resting-state fMRI study indicated a significant decrease in the functional connectivity of the motor network in aged subjects (mean age = 61.8) compared to young subjects (mean age = 26.5 years) (Wu et al., 2007). A more recent fMRI study using a visual oddball task also indicated that elderly subjects (mean age = 63.9 years) showed a decrease in connectivity within the somatomotor network compared to younger subjects (mean age = 24.1 years) (Geerligs et al., 2012).

We also found significant quadratic correlations between the *ARC\_NS* and the baseline age in many brain regions, predominately from frontal (4/10), temporal (2/10), parietal (2/10), and subcortical (2/10) areas. It is notable that the significant quadratic correlations peaked at a baseline age from 51.17 to 54.87 years. Thus, in these brain regions, the *ARC\_NS* increased with the baseline age in the period from middle (approximately 50 years old) to old age. More importantly, the identified brain regions are mainly associated with the default-mode, attention, and memory systems. The scaffolding theory of aging and cognition (STAC) suggests that scaffolding is a normal process present across the lifespan that involves use and development of complementary, alternative neural circuits to achieve a particular cognitive goal and is protective of cognitive function in the aging brain (Park and Reuter-Lorenz, 2009). Thus, our results are in line with the STAC and suggest a compensation mechanism of structural brain network reorganization with advanced aging. It has been indicated that cognitive decline is associated with differences in the structure and function of the aging brain, and it has been suggested that increased activation is either caused by disruption, whether structural or functional, or is a compensatory response to such disruption (Hedden and Gabrieli, 2004; Persson et al., 2006; Grady, 2012). Previous findings from several studies on structural and functional brain networks also support this view. Many brain regions, primarily from the frontal and temporal lobes, show increases in regional nodal efficiency in structural brain networks (Gong et al., 2009). Several regions, mostly in the lateral occipital-parietal junction and the paralimbic/subcortical area, reveal increased node betweenness in old age (Wu et al., 2012). The decrease in visual memory and visuoconstructive functions is strongly associated with the age-dependent enhancement of functional connectivity in both temporal lobes (Schlee et al., 2012). However, a recent study showed reduced structural association in the high-order cognitive networks of older adults compared to young adults, while no differences were observed in the sensorimotor networks (Montembeault et al., 2012). The following possible reasons are given for the discrepancies between this finding and our results: only eight brain regions were included in the previous study, whereas the present study was a whole-brain analysis; furthermore, only a comparison between young (mean age = 23*.*5 ± 3*.*1 years) and old (mean age = 67*.*3 ± 5*.*9 years) age was analyzed in the previous study, neglecting the other comparisons (young vs. middle; middle vs. old). Moreover, most of the identified brain regions showing positive quadratic correlations with the baseline age are found to be altered in AD patients (Bai et al., 2012; Zhao et al., 2012; Wang et al., 2013). For example, several brain regions (e.g., ACG.L, ORBmed.R, IFGoperc.R, IPL.L, IPL.R, and PUT.R) in AD patients show significant increases in regional nodal properties (e.g., the regional local and global efficiency) (Zhao et al., 2012). Thus, these findings provide further evidence supporting the view that advanced aging confers a high risk for neurodegenerative diseases, such as AD.

## **METHODOLOGY**

Several methodological issues need to be addressed. First, structural brain networks can be constructed in two ways: (1) indirectly from inter-regional correlation of morphological measurements (e.g., cortical thickness, RGMV, and surface area) in sMRI data; (2) directly from characteristics of white matter fibers (e.g., fiber number, fractional anisotropy, apparent diffusion coefficient, or distance) in diffusion tensor imaging (DTI) data (Bassett and Bullmore, 2009; He and Evans, 2010; Lo et al., 2011; Xia and He, 2011). Although there is still no direct proof that correlations of morphological measurements across subjects are indicative of axonal connectivity via white matter tracts, strong correlations between brain regions known to be anatomically connected have been observed in previous optimized voxel-based morphometry studies (Mechelli et al., 2005; Pezawas et al., 2005). Moreover, a recent study indicated that approximately 35–40% of cortical thickness correlations showed convergent diffusion connections across the cerebral cortex and most of them were the positive thickness correlations (Gong et al., 2012). However, the authors also found that almost all of the negative correlations (*>*90%) did not have a matched diffusion connection, suggesting different mechanisms behind the positive and negative thickness correlations. Since we defined structural connectivity as the absolute value of correlation of RGMV in this study, the association between correlation of RGMV and diffusion connections should be investigated further in future studies. Second, previous studies indicate that different parcellation strategies affect the topological properties (e.g., the local efficiency, global efficiency, small-worldness, and modularity) of structural or functional brain networks (Wang et al., 2009a; Fornito et al., 2010; Zalesky et al., 2010). A previous study also indicates that regional volumes are positively correlated with their mutual information, which measures the functional connectivity between each region and the remaining brain regions (Salvador et al., 2008). Thus, variations in parcellation templates (e.g., AAL used in this study) may affect the network structure of the human brain; future studies should include comparisons of network topology with different parcellation templates. Third, because all of the subjects in this study were over 20 years old, young and adolescent subjects should be included in future studies of brain network development. Finally, further investigations will also examine longitudinal changes in the topological properties of the human brain network using different neuroimaging modalities, such as diffusion tensor imaging, functional MRI, and electroencephalography.

## **CONCLUSION**

In this study, we quantitatively analyzed the topological organization of structural brain networks using a longitudinal design over 6 years. Our results reveal economical small-world properties of structural brain networks and longitudinal changes in both global and regional network properties. The structural brain networks develop into a fast distribution at approximately the age of 50 and then transform into a fast localization with substantial alterations in old age. Our findings may contribute to understanding the mechanism of normal aging in the human brain and help to distinguish neurodegenerative diseases from normal aging.

## **REFERENCES**


Weinberger, D. R., and Coppola, R. (2009). Cognitive fitness of costefficient brain functional networks. *Proc. Natl. Acad. Sci. U.S.A.* 106, 11747–11752.


## **ACKNOWLEDGMENTS**

This study was supported by the 2007 Tohoku University Global Century Centre of Excellence (GCOE) Program (Ministry of Education, Culture, Sports, Science and Technology; MEXT) titled "Global Nano-Biomedical Engineering Education and Research Network Centre." The brain MRI database was constructed at the Aoba Brain Imaging Center with a grant from the Telecommunications Advancement Organization (National Institute of Information and Communications Technology) of Japan.

## **FUNDING**

This study was supported by grants-in-aid from the Ministry of Education, Culture, Sports, Science, and Technology (20790875, 24103701, and 23240056), Japan and Guangdong Natural Science Foundation (S2012040007743), China.

develop from a "local to distributed" organization. *PLoS Comput. Biol.* 5:e1000381. doi: 10.1371/journal.pcbi.1000381


large-scale cortical networks in Alzheimer's disease. *J. Neurosci.* 28, 4756–4766.


functional brain mapping. *Hum. Brain Mapp.* 10, 120–131.


Aging influence on functional connectivity of the motor network in the resting state. *Neurosci. Lett.* 422, 164–168.


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 04 December 2012; accepted: 15 March 2013; published online: 03 April 2013.*

*Citation: Wu K, Taki Y, Sato K, Qi H, Kawashima R and Fukuda H (2013) A longitudinal study of structural brain network changes with normal aging. Front. Hum. Neurosci. 7:113. doi: 10.3389/fnhum.2013.00113*

*Copyright © 2013 Wu, Taki, Sato, Qi, Kawashima and Fukuda. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients

#### *Guangyu Chen1, Hong-Ying Zhang2,3, Chunming Xie1,4, Gang Chen1, Zhi-Jun Zhang4, Gao-Jun Teng2 \* and Shi-Jiang Li 1,5\**

*<sup>1</sup> Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA*

*<sup>2</sup> Department of Radiology, Jiangsu Key Laboratory of Molecule Imaging and Functional Imaging, Medical School of Southeast University, Nanjing, PR China*

*<sup>3</sup> Department of Radiology, Subei People's Hospital of Jiangsu Province, Yangzhou University, Yangzhou, PR China*

*<sup>4</sup> Department of Neuropsychiatry, Affiliated Zhong Da Hospital of Southeast University, Nanjing, PR China*

*<sup>5</sup> Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, WI, USA*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Christian Sorg, Klinikum rechts der Isar Technische Universität München, Germany Jinhui Wang, Beijing Normal University, China*

#### *\*Correspondence:*

*Gao-Jun Teng, Department of Radiology, Jiangsu Key Laboratory of Molecule Imaging and Functional Imaging, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing 210009, PR China e-mail: gjteng@vip.sina.com; Shi-Jiang Li, Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA e-mail: sjli@mcw.edu*

Previous studies have demonstrated disruption in structural and functional connectivity occurring in the Alzheimer's Disease (AD). However, it is not known how these disruptions alter brain network reorganization. With the modular analysis method of graph theory, and datasets acquired by the resting-state functional connectivity MRI (R-fMRI) method, we investigated and compared the brain organization patterns between the AD group and the cognitively normal control (CN) group. Our main finding is that the largest homotopic module (defined as the insula module) in the CN group was broken down to the pieces in the AD group. Specifically, it was discovered that the eight pairs of the bilateral regions (the opercular part of inferior frontal gyrus, area triangularis, insula, putamen, globus pallidus, transverse temporal gyri, superior temporal gyrus, and superior temporal pole) of the insula module had lost symmetric functional connection properties, and the corresponding gray matter concentration (GMC) was significant lower in AD group. We further quantified the functional connectivity changes with an index (index A) and structural changes with the GMC index in the insula module to demonstrate their great potential as AD biomarkers. We further validated these results with six additional independent datasets (271 subjects in six groups). Our results demonstrated specific underlying structural and functional reorganization from young to old, and for diseased subjects. Further, it is suggested that by combining the structural GMC analysis and functional modular analysis in the insula module, a new biomarker can be developed at the single-subject level.

**Keywords: Alzheimer's disease, MCI, validation, module analysis, resting-state functional connectivity, brain network, gray matter concentration, graph theory**

## **INTRODUCTION**

Alzheimer's disease (AD) is considered a disconnection syndrome (Geschwind, 1965; Delbeuck et al., 2003). Recent studies demonstrated that the underlying neural mechanisms responsible for the disconnection syndrome are involved in the functional disruption in the brain of AD patients (Horwitz et al., 1987; Wada et al., 1998). An increasing number of studies have focused on imaging the default mode network (DMN) in aging and dementia by using intrinsic blood oxygenation level-dependent (iBOLD) signals, acquired by the resting-state functional MRI (R-fMRI) method (Lustig et al., 2003; Greicius et al., 2004; Sorg et al., 2009; Khalili-Mahani et al., 2012). The measurement of functional disruption in the DMN could become a potential clinical diagnostic biomarker for AD because convergent evidence demonstrated that brain atrophy, Aβ-amyloid plaque deposition and metabolic deficits co-occurred in the DMN (Buckner et al., 2009). Several other studies demonstrated that functional disruption also occurred in other areas besides the DMN, such as the hippocampus and the insular networks (Li et al., 2002; Bonthius et al., 2005; Royall, 2008; Xie et al., 2012). However, despite these scientific advancements, efforts to cross-validate the functional disruption trait as a biomarker have been of limited success.

Specifically, several studies provided the diagnostic power of the DMN for AD (Li et al., 2002; Greicius et al., 2004; Fleisher et al., 2009; Koch et al., 2010, 2012), but follow-up studies by other research groups are either lacking (Li et al., 2002; Greicius et al., 2004; Fleisher et al., 2009; Koch et al., 2010, 2012), controversial (Zhang et al., 2009; Yu et al., 2011), or failed to confirm a solid diagnostic value (Prvulovic et al., 2011). As a result, despite the efforts during the past decade, there is no robust biomarker based on R-fMRI technology, which has substantially limited its potential utility value in AD research and treatment. There are several factors that may contribute to the current stagnant status. First, in typical seed-based R-fMRI studies, the group-level *t*-tests often statistically identified the connectivity maps that highlight voxels where functional connectivity is disrupted. Such a statistical approach often overestimates the diagnostic power, even if the leave-one-out approach or seven-fold cross-validation method is employed (Chen et al., 2011a; Westman et al., 2012a,b). Second, because of compensatory mechanisms or increased activation, brain connectivity may be reorganized along the continuum of disease progression (He et al., 2008; Sanz-Arigita et al., 2010). Not only did the functional connectivity decrease in certain regions, but it also increased in other regions (Zhang et al., 2010). As a result, the summation of the overall connectivity strength may not change significantly. Third, the disconnection syndrome in AD may be the result of the functional and structural disruptions in the large-scale networks; therefore, the seed-based network alone, such as the DMN, may have no sufficient power as a biomarker. As a result, when applying trained classifiers to independent datasets, the specificity and sensitivity were low.

To overcome these shortcomings and to move the research field forward, the present study is focusing on three new approaches. First, we extend the seed-based analysis to the modular analysis method (He et al., 2009; Meunier et al., 2009a,b, 2010) to examine the patterns of brain network reorganization at the large-scale network level to test the hypothesis that the AD network organization is a reconfiguration from CN networks where some subnetworks that are related to cognitive processing may change and others are preserved. A previous study (Faria et al., 2012) addressed the factor that network (or called Atlas)-based analysis can enhance SNR and reproducibility of resting-state functional connectivity. In addition, in using network-based functional connectivity, the number of false positive cross-correlations can be significantly reduced due to the reduced number of the total pairs of correlations. To our knowledge, the applicability of the modular analysis to examine the resting-state functional network reorganization pattern in mild cognitive impairment (MCI) and AD brains has not been demonstrated. Second, based on specific changes in brain reorganization patterns at the module level, an exploratory analysis was performed to evaluate if the changes can be employed as a biomarker for AD. Third, we employed an additional six independent R-fMRI datasets from human subjects to independently cross-validate the module-based biomarker at the single-subject level.

## **MATERIALS AND METHODS HUMAN SUBJECTS**

A total of 331 subjects in eight groups were employed for this study. Two R-fMRI datasets obtained from the cognitively normal (CN) group (*N* = 30) and the mild AD group (*N* = 30) from the Medical College of Wisconsin (MCW) site (referred to herein as MCW datasets) (**Table 1**) were employed as the testing datasets to identify changes in the modular reorganization patterns occurring in AD brains as a biomarker. We then employed six additional independent R-fMRI datasets to cross-validate the biomarker. Among the six sets of datasets, one was obtained from amnestic mild cognitive impairment (aMCI) subjects (*N* = 23) from the MCW site, three datasets were obtained from a group of 56 elderly subjects from Southeast University, Nanjing, China, comprised of elderly CN subjects (*N* = 20), aMCI subjects (*N* = 22), and AD subjects (*N* = 14) (referred to herein as Nanjing datasets) (**Table 1**) (Zhang et al., 2010). The other two independent R-fMRI datasets are comprised of 192 young subjects; these were downloaded from the 1000 Functional Connectomes Project database (www.nitrc.org/ projects/fcon\_1000/) from Beijing Zang's datasets (http://www. nitrc.org/frs/shownotes.php?release\_id=819) (referred to herein as Beijing datasets) (**Table 1**) (Biswal et al., 2010). All of these subjects were obtained from databanks. For detailed subject information, please refers to originally published papers (Biswal et al., 2010; Zhang et al., 2010; Chen et al., 2011a).

## **IMAGING ACQUISITION OF MCW DATASETS**

Imaging was performed using a whole-body 3T Signa GE scanner with a standard quadrature transmit receive head coil. During the resting-state acquisitions, no specific cognitive tasks were performed, and the study participants were instructed to close their eyes and relax inside the scanner. Sagittal resting-state functional MRI (fMRI) datasets of the whole brain were obtained in 6 minutes with a single-shot gradient echo-planar imaging (EPI) pulse sequence. The fMRI imaging parameters were: TE of 25 ms, TR of 2 s, flip angle of 90◦; 36 slices were obtained without gap; slice thickness was 4 mm with a matrix size of 64 × 64 and field of view of 24 × 24 cm. High-resolution SPGR 3D axial images were acquired for anatomical reference. The parameters were: TE/TR/TI of 4/10/450 ms, flip angle of 12◦, number of slices of 144, slice thickness of 1 mm, matrix size of 256 × 192. To make sure that cardiac and respiratory frequencies did not account for any significant artifacts in the low-frequency spectrum, a pulse oximeter and respiratory belt were employed to measure these physiological noise sources. Further processing ensured a minimizing of the potential aliasing effects.

#### **IMAGING ACQUISITION OF BEIJING DATASETS**

The data was acquired at 3T Siemens Scanner. We used 192 subjects out of a total of 198 young subjects from Beijing Zang's datasets. Six subjects were discarded during the preprocessing procedures for a variety reasons. The imaging acquisition parameters can be found on the website (http://www*.*nitrc*.*org).

#### **IMAGING ACQUISITION OF NANJING DATASETS**

The data was acquired at 1.5T Philips Scanner. Subjects wore headphones and were instructed to lie in a supine position in a standard head coil of a 1.5-T MR imaging unit (Eclipse; Philips, Best, The Netherlands). Structural images were obtained. Restingstate functional images were acquired by using a gradient-echo EPI sequence (TR/TE, 3000/40 ms; flip angle, 90◦, slice thickness, 6 mm; slice gap, 0 mm; field of view, 240 mm; and matrix size, 64 × 64; 18 axial slices and 128 time points). For detailed parameters and demographic information, please refer to previous study (Zhang et al., 2010). All of these studies were conducted with Institutional Review Board approval and were in compliance with Health Insurance Portability and Accountability Act (HIPAA) regulations or similar polices in China.

#### **DATA PREPROCESSING**

We used Analysis of Functional NeuroImages (AFNI) software (http://afni*.*nimh*.*nih*.*gov/afni/) and MATLAB (Mathworks) in this study for data processing. The first five volumes of each raw resting-state functional imaging dataset were discarded to allow for T1 equilibration. Interleaved slice acquisitiondependent time shifts were corrected (AFNI command, *to3d -time:zt* nz nt TR tpattern). Spikes in time series data were removed (AFNI command, *3dDespike*). Data were then motion corrected (Six motion parameters, including roll, pitch, in the superior, left and posterior direction displacement were estimated by volume registration of the R-fMRI data, and then, were regressed out by using Afni command 3dDeconvolve to control possible micromovement effects). There was no group difference for movement parameters. Detrend processing procedure using AFNI commands (*3dvolreg and 3dDetrend*) was performed. The reference template in Talairach space, which contained 116 anatomically defined regions of interest (ROIs) (Tzourio-Mazoyer et al., 2002), was transformed and aligned to the SPGR images and EPI resting-state functional images for each subject (AFNI command, *3dfractionize*). This resulted in 116 mapped ROIs. The average time course within each ROI was extracted from the resting-state functional imaging datasets. Averaged white matter signal and cerebrospinal fluid (CSF) signal were extracted using white matter mask (http://afni*.*nimh*.*nih*.* gov/pub/dist/data/TTwm+tlrc) and CSF (http://afni*.*nimh*.*nih*.* gov/pub/dist/data/ TT\_csf+tlrc) mask in Talairach space. These two masks were transformed and aligned to the SPGR and echo planar images for each subject (AFNI command, *3dfractionize*). Then, the average time courses within the CSF or the eroded white matter mask, together with global mean signals, were removed as nuisance regressors from the 116 regional time courses with linear regression using Matlab (Mathworks).

#### **POSTPROCESSING**

#### *Brain functional network*

We constructed a region-wise whole-brain resting-state functional network for each subject. A network size is *N* (*N* is the num*ber of Nodes or ROIs, in this study, N* = *116),* there are *N* × *(N – 1)/2* possible edges in a fully connected network expressed in a matrix. The weighted strength of each edge between nodes *i* and *j* was defined as CCij (cross-correlation coefficient (**CC)** between two time series of *ROI(i)* and *ROI(j)*). The weighted distance of each edge between a pair of directly connected nodes *ROI(i)* and *ROI(j)* was defined as *dij* = 1 − *CCij*. The adjacent matrix of *CCij* represents graph *G*, such that *G* = {*V, S, D*}, consisting of a set of vertices(Nodes) *V* = {*V*1*, V*2*,..., VN*}, a set of edges *S* = {*CCij*|*i, j* = 1*,* 2*,..., N*} and a set of associating weighted edge distances D = {1 − CC\_i, j|i, j = 1, 2, . . . ,N} between brain regions *ROI(i)* and *ROI(j)*.

#### *Group network*

A group functional network matrix **(A)** is constructed by the ratio of mean to the standard deviation of all individuals' matrices in this group. Each element value of **A** is calculated as follows:

$$a\_{i\bar{j}} = \frac{\frac{1}{n} \sum\_{k=1}^{n} CC\_{k,\bar{i},\bar{j}}}{\sqrt{\frac{1}{n} \sum\_{k=1}^{n} (CC\_{k,\bar{i},\bar{j}} - \mu\_{i\bar{j}})^2}} \tag{1}$$

This matrix can reduce the intersubject variation of the functional connectivity especially those connections with large intersubject variation. *k* is the subject number, *n* is the number of subjects, *i* and *j* are two ROIs of *ROI(i)* and *ROI(j)*. In this study, we only use the positive CC value in group network as previously described (Chen et al., 2011b) for further modular analysis.

#### *Modularity*

Module is defined as a community, the inside of which has denser connections than the rest of the network (Newman and Girvan, 2004). Several algorithms have been developed to detect those modules (Clauset et al., 2004; Duch and Arenas, 2005). The basic approach is to measure the maximum modularity value, Q, which is defined as:

$$Q = \frac{1}{2m} \sum\_{i,j} \left[ a\_{ij} - \frac{k\_i k\_j}{2m} \right] \delta(c\_i, c\_j). \tag{2}$$

*aij* is the adjacent weighted matrix which represents the network, *m* is the number of connections in the network, and *ki* is the degree of node *i* (Ahnert et al., 2007) and *ci* is the module *i*.

In order to find the communities in the brain functional network, we use the spectral algorithm of Newman (Newman, 2006; Leicht and Newman, 2008), which is implemented in the Brain Connectivity Tool Box (https://sites*.*google*.*com/a/ brain-connectivity-toolbox*.*net/bct/Home). This program can find the network organization pattern with the best modularity value (Q).

#### *Quantitative measurement of modular reorganization in AD*

Based on our hypothesis that AD may reorganize modular patterns compared to CN, the reorganization pattern may exhibit the disruption properties of the whole-brain function network. In order to quantify the changes in the modular patterns in the subnetworks, we created two functional indices (index A and index B) to measure the inter- and intra-hemisphere connections. Index A measures an average of functional connectivity strength



in homotopic pairs (*ROI*\_*L(i), ROI*\_*R(i)*)

$$\text{indexA} = \frac{1}{n} \sum\_{i=1}^{n} CC\left(ROI\\_L(i), ROI\\_R(i)\right) \tag{3}$$

Where *ROI*\_*L(i)* and *ROI*\_*R(i)* are two corresponding bilateral homotopic regions, and *CC(ROI*\_*L(i), ROI*\_*R(i))* is the two time courses CC value of *ROI*\_*L(i)* and *ROI*\_*R(i)*. The *n* is the number of pairs of homotopic regions to calculate index A. Index B is used to measure an average of functional connectivity strength within selected unilateral ROIs.

$$\text{indexB} = \frac{1}{n} \left( \sum\_{i=1}^{n} \sum\_{j=1}^{n} \text{CC} \left( \text{ROI\\_L}(i), \text{ROI\\_L}(j) \right)$$

$$+ \sum\_{i=1}^{n} \sum\_{j=1}^{n} \text{CC} \left( \text{ROI\\_R}(i), \text{ROI\\_R}(j) \right) \right) \qquad (4)$$

There are n ROIs from the right hemisphere (ROI\_R) and n corresponding homotopic ROIs from the left hemisphere (ROI\_L).

#### *Gray matter concentration (GMC)*

Besides the functional connectivity, we also calculated the GMC on the AAL template within the regions that showed functional disruption. The gray matter of each subject is segmented by using SPM 8 software (www*.*fil*.*ion*.*ucl*.*ac*.*uk/spm/software/spm8/) and then normalized into Talairach space to extract each part of the regions using AFNI and 116 AAL templates. GMC value of each region is the average overall voxel values within those regions involved in modular reorganization.

## **RESULTS**

#### **MAXIMUM MODULARITY VALUE (Q)**

The Q is determined with the modular algorithm, which measures how a network can be separated into different subnetworks. With the MCW dataset, **Figure 1** shows the averaged maximum modularity values of individual subjects in each group as a function of number of edges (NE). All subjects in groups CN and AD

have larger averaged Q values than their corresponding random networks, indicating that their complex functional networks have a strong ability to form modules, and the module analysis method can be applied to disease populations, such as MCI and AD. There is no group difference in Q value at all different thresholds of NE after the familywise error correction.

## **MODULE STRUCTURES IN THE AD GROUP WERE REORGANIZED, UNLIKE THE CN GROUP**

Although the complex functional network of the AD group has the ability to form the modular structures, similar to the CN group, the modular patterns and membership are quite different between the CN and AD groups, and demonstrated network reorganization patterns. The modular structures are expressed into two forms of presentations: the graphic presentation and mapping presentation, as illustrated in **Figures 2A,B** for CN and AD groups, respectively. For the CN group, the brains were organized into seven modules. For the AD group, the brain modules were reorganized into eight modules. The module-reorganization patterns between CN and AD are graphically illustrated in **Figures 2C,D**. The largest module in the CN group (CN-1) was broken down into two separated modules in the AD group (AD-1 and AD-2). The module CN-2 is disrupted into three modules (AD-3, AD-4, and AD-8) and module CN-6 is disrupted into four modules (AD-4, AD-6, AD-7, AD-8).

brain module organization overlaid in the brain template and the last row (**C** and **D**) is the module reorganization pattern between CN and AD. The label module numbers in the brain views of **(A)** and **(B)** are matched with the module numbers in **(C)** and **(D)**, respectively. Two matrices, **(C)** and **(D)**, show the grouped CC matrix of CN and AD. In **(C)** and **(D)**, numbers along each matrix labeled the module number for each group. Red arrow and red connection lines show the reorganization pattern from CN to AD. The thickness of each line represents the number of members.

To compare the module membership composition between CN and AD groups and identify specific brain regions that disrupted away from the original module, the module CN-1 was cited as an example. As listed in **Table 2**, the module CN-1 contained eight pairs of homotopic brain regions, which are defined as geometrically symmetric across interhemispheric regions. This well-organized module is highly symmetric across hemispheres in the control network (**Figure 2A**). We called this module the "insula module," because its members are involved in saliency, switching, attention and control functions of the insula network (Menon and Uddin, 2010). Noticeably, eight out of 16 homotopic regions were broken in the AD group. The eight regions on the right hemisphere formed a new module in the AD group (AD-2) (labeled in Red bold in **Table 2**). These eight regions are the right opercular part of inferior frontal gyrus, right area triangularis, right insula, right putamen, right globus pallidus, right transverse temporal gyri, right superior temporal gyrus, and right superior temporal pole. The formation of the new module AD-2 not only indicated that the insula module is broken down, but also indicated there is severe disruption between left and right hemisphere communication in the AD brains.

## **QUANTIFICATION AND VALIDATION OF THE INSULA MODULE IN HEALTHY YOUNG, CN, MCI, AND AD GROUPS**

To quantify the insula module disruption between hemispheres, two functional indices were obtained [index A calculated from Equation (3) and index B calculated from Equation (4)]. As illustrated in **Figures 3A,B**, functional connections of the eight pairs of homotopic (contralateral) regions of the insula module were

**Table 2 | Regions in the insula module (blue module).**


*The eight right brain regions (in red bold) are no longer the members of the blue module in the AD brains.*

disconnected in the AD group. As shown in **Figure 3C**, index A is significantly (*p <* 0.018) decreased in the AD group compared to the CN group. Index B shows no significant difference related to the AD and CN groups but has increasing trends (*p <* 0*.*12). To cross-validate the results with indices A and B, we employed six additional independent datasets in order to avoid an *overly optimistic* estimate of the error rate by the *resubstitution method* or the *leave-one-out method*. First, we employed the MCW dataset containing 23 aMCI subjects. As shown in **Figure 3D**, index A, individually calculated from each aMCI subject in the aMCI group, was significantly lower than that in the CN group and index B showed no difference. Second, we employed the Beijing datasets containing the young groups of subjects (group age between 18 and 22 years old, and group age between 23 and 26 years old, total 192 subjects). As shown in **Figure 3D**, index A of young subjects has stronger homotopic connectivity strength than that of the elderly CN subjects and no differences for index B. Third, we further demonstrated that datasets acquired on the 1.5T scanner can be employed to validate our results. With the Nanjing datasets acquired from 1.5T scanner, index A of the MCI and AD groups is significantly reduced compared to the CN group, as shown in **Figure 3E**. These validated results demonstrated that index A as a biomarker can be quantitatively employed for monitoring AD progression in the continuum of disease processes: higher index A in young, decrease in elderly CN groups, and more significantly decreased in the MCI and AD groups.

Decreased gray matter concentration (GMC) of these eight pairs of homotopic regions in MCI and AD groups. The disrupted functional connectivity occurred in the eight pairs of homotopic regions in the insula module. In addition, the average gray matter concentration (GMC) of those regions showed significant decrease in the MCI and AD groups in comparison to the CN groups. The GMC decrease in the MCI and AD group was observed, as shown in **Figure 3F**. To determine if the GMC changes affect the calculation of index A, the variance of the GMC factor was controlled out. As shown in **Figure 3G**, index A is still valid in distinguishing between CN from MCI or AD status. These results also indicated that although structural density and functional connectivity decrease may be related, their changes are not necessary proportional (Palop et al., 2006). With this trait, we have combined index A and GMC to examine their diagnostic potential, as described below.

Diagnostic power of index A and GMC as biomarkers to classify CN, MCI, and AD statuses. Through the measurement of index A and GMC on each single subject, we have explored their potential as biomarkers to classify CN, MCI, and AD statuses. As shown in **Figure 4**, the result from testing groups (CN vs. AD) provided 94% of area under the curve (AUC) of the receiver operation characteristic (ROC) curve. The validation results provided 78 and 71% of AUC to classify between CN and AD, and between MCI and AD, respectively. With Nanjing datasets acquired on the 1.5T Siemens scanner, these validation results become 85% (AD vs. CN) and 80% (MCI vs. CN), and 70% (MCI vs. AD). For the young subject groups as the healthiest population, there is a perfect 100% specificity and sensitivity (AUC 100%) in comparison to the AD group.

connection, while the dash line represents the weaker connection. **(C)** Two indices (index A and index B) of CN\_3T and AD\_3T (test groups). **(D)** Two indices of two young subject groups, CN\_3T and MCI\_3T (validation groups). **(E)** Two indices of CN\_1.5T, MCI\_1.5T, and AD\_1.5T

## **DISCUSSION**

Several studies have employed the modular analysis method to demonstrate that the brain has modular organization (Hilgetag et al., 2000; Chen et al., 2008; Hagmann et al., 2008). In comparison to small-world metrics, modular analysis can provide detailed network organization patterns as to how the nodes are connected to form subnetworks or communities in a complex network (Hilgetag et al., 2000; Chen et al., 2008; Hagmann et al., 2008). Using this advantage, modular analysis methods have been applied to diseased resting-state brain networks, such as in chronic back pain (Balenzuela et al., 2010) and schizophrenia (Alexander-Bloch et al., 2010; Yu et al., 2011). Using magnetoencephalography (MEG), it was also found that the module strength and the number of modules significantly changed in AD patients (de Haan et al., 2012). Our results are consistent with these findings and demonstrated the applicability of R-fMRI datasets for modular analysis to AD.

represents the significance (*p <* 0*.*05) in comparison to the MCI group. Error bar indicates the standard deviation. (++) represents both young groups have significant larger index A value than the old CN\_3T and

In the control network, as expected, module patterns are well organized with symmetric distribution. Each pair of the interhemispheric homotopic regions, for the most part, is in the same communities. Many literature references substantiate that the brain functional network forms an interhemispheric symmetric pattern with highly consistent functional connectivity between homotopic regions (Zuo et al., 2010). A high degree of symmetry in the motor cortex of resting-state functional connectivity has been reported (Biswal et al., 1995; Van den Heuvel and Hulshoff

MCI\_3T groups.

Pol, 2010). The well-known DMN (Raichle et al., 2001; Greicius et al., 2004) has a symmetric, well-organized pattern. Similar to the module method, Mezer (Mezer et al., 2009) used the clustering method and discovered a symmetric pattern of clusters between the two hemispheres. This was true not only in the human brain, but also in the rat brain. The highest values of functional connectivity exist between interhemispheric homotopic regions (Pawela et al., 2008, 2010). However, not all the interhemispheric homotopic regions are symmetric, only some regions and their homotopic regions belong to different communities. This may be due to the dynamic changes of the functional connectivity (Chang and Glover, 2010).

As expected in mild AD group, some of the communities lost symmetric properties. There are more single regions whose homotopic regions are in different communities. For example, module 1 (blue module in **Figure 2A**) in the control group is very symmetric, while it is separated into two modules in the AD group (blue and brown modules in **Figure 2B**). Decreased symmetric properties or functional connectivity between interhemispheric homotopic regions have been found in many diseased functional networks. In behavioral research, Yamina (Lakmache et al., 1998) found that AD subjects performed normally when using intrahemispheric processing, but did poorly when interhemispheric communication was required. For instance, in imaging research, EEG studies (Locatelli et al., 1998; Babiloni et al., 2004) of AD found decreased coordination between interhemispheric networks. In the cocaine-dependent group, Kelly (Kelly et al., 2011) investigated the interhemispheric homotopic connections using the Voxel-Mirrored Homotopic Connectivity method, and found the striking cocaine-dependence-related reduction in interhemispheric resting-state functional connectivity among nodes of the dorsal attention network. Also, decreased interhemispheric functional connectivity in subjects with impaired awareness were found (Ovadia-Caro et al., 2012). Therefore, this phenomenon of losing symmetric properties may reflect the cognitive decline and unbalanced state in the functional network of the diseased brain.

The most significant finding of this module study is the interrupted integration of insula module in AD group. Anatomically, the insula is a crucial hub in the human brain network; it is widely connected to the cortical, limbic, and paralimbic structures. Functionally, it is involved in high-order cognition, emotion, autonomic, and sensory process (Naqvi et al., 2007; Allen et al., 2008). The previous study has shown that the insula was affected in AD and its atrophy was significantly decreased from the normal population (Fan et al., 2008). The seed-based functional connectivity of the insular regions was discovered to be significantly decreased in the regions that functionally connected with insula. This disruption was associated with episodic-memory deficits in aMCI patients (Xie et al., 2012). Our results are not only consistent with these previous findings, they indicate a disruption between the insula and other brain regions. Also, we detected the breakdown of the insula module in the AD group, which is a possible neural underpinning of AD dementia.

Our findings demonstrated that the specific reorganized modular patterns can be quantified with index A in the CN, MCI, and AD groups. Unlike biomarkers with inverse U-shape patterns, such as the fMRI method due to the compensatory mechanisms (Dickerson and Sperling, 2009), index A is a monofunction with the disease progression of AD. Index A of aMCI and AD subjects is significantly lower than that of CN and young subjects. Because the biggest risk factor of AD is aging, the congruency between the changes in the index A value, and changes in age, demonstrated the potential of index A to serve as a biomarker. This characteristic of the monofunction of index A with age is very important for diagnostic accuracy by decreasing false positive and negative errors.

We showed the potential of using structural changes (GMC) and functional disruption in the insula module (index A) as a biomarker for AD. Recent revision of the NINCDS-ADRDA (National Institute of Neurological and Communicative Disorders and Stroke and the AD and Related Disorders Association [now known as the Alzheimer's Association]) criterion for the diagnosis of AD suggested adding abnormal biomarkers, such as MRI, positron emission tomography (PET), CSF, and brain atrophy to strengthen their roles (Kohannim et al., 2010; Nettiksimmons et al., 2010; Walhovd et al., 2010; McKhann et al., 2011; Zhang et al., 2011; Dai et al., 2012; Ewers et al., 2012; Westman et al., 2012b). The effective combination of these biomarkers can clinically provide more diagnostic power than using a single biomarker. In this study, we found that the combination of MRI atrophy biomarker and the R-fMRI biomarker of insula module could enhance the classification of AD and monitor the progression along the continuum of AD development both in the test and validation group. Our results demonstrated the great feasibility of combining both MRI-based biomarkers of the insula module in AD diagnosis.

In summary, with the modular analysis, we demonstrated the ability of index A and GMC of the insula module in distinguishing MCI and AD from old and young, healthy CN subjects, and its power of cross-validation with six independent datasets.

## **REFERENCES**


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The combination of the MRI-based structural biomarker and functional biomarker will significantly enhance the diagnostic power. Further studies will be needed to characterize the relationships between different biomarkers for AD (Sperling et al., 2009; Kohannim et al., 2010; Nettiksimmons et al., 2010; Sheline et al., 2010a,b; Walhovd et al., 2010; Zhang et al., 2011; Ewers et al., 2012; Johnson et al., 2012).

## **ACKNOWLEDGMENTS**

Dr. Gao-Jun Teng has full access to all of the study data and took responsibility for the integrity of the data and the accuracy of the data analysis. The authors thank Ms. Carrie M. O'Connor, M.A., for editorial assistance, and Mr. B. Douglas Ward, M.S., for discussions related to the statistical analysis. This work was supported by National Natural Science Foundation of China (30825014, 30870704, 30971016, 81061120529 (Zhi-Jun Zhang); 81171323 (Chunming Xie); 91132727 (Xiangyong Zhang); 81171021 (Yongmei Shi); National Institutes of Health grants: RO1 AG20279 (Shi-Jiang Li), the DANA Foundation (Shi-Jiang Li). Social Development and Scientific and Technology Project of Yangzhou (YZ2011087).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 06 May 2013; accepted: 22 July 2013; published online: 09 August 2013. Citation: Chen G, Zhang H-Y, Xie C, Chen G, Zhang Z-J, Teng G-J and Li S-J (2013) Modular reorganization of brain resting state networks and its independent validation in Alzheimer's disease patients. Front. Hum. Neurosci. 7:456. doi: 10.3389/fnhum.2013.00456*

*Copyright © 2013 Chen, Zhang, Xie, Chen, Zhang, Teng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## GroupICA dual regression analysis of resting state networks in a behavioral variant of frontotemporal dementia

## *Riikka Rytty1,2, Juha Nikkinen3, Liisa Paavola1,2, Ahmed Abou Elseoud3, Virpi Moilanen2, Annina Visuri 1, Osmo Tervonen3, Alan E. Renton4, Bryan J. Traynor 4, Vesa Kiviniemi 3 † and Anne M. Remes 5,6\*†*

*<sup>1</sup> Department of Neurology, Institute of Clinical Medicine, University of Oulu, Oulu, Finland*

*<sup>2</sup> Department of Neurology, Oulu University Hospital, Oulu, Finland*

*<sup>3</sup> Department of Radiology, Institute of Clinical Medicine, University of Oulu, Oulu, Finland*

*<sup>4</sup> Neuromuscular Diseases Research Unit, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA*

*<sup>5</sup> Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland*

*<sup>6</sup> Department of Neurology, Kuopio University Hospital, Kuopio, Finland*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Xi-Nian Zuo, Beijing Normal University, China Smadar Ovadia-Caro, Humboldt University, Germany Norman Farb, Baycrest, Canada*

#### *\*Correspondence:*

*Anne M. Remes, Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland e-mail: anne.remes@uef.fi*

*†These authors have contributed equally to this work.*

Functional MRI studies have revealed changes in default-mode and salience networks in neurodegenerative dementias, especially in Alzheimer's disease (AD). The purpose of this study was to analyze the whole brain cortex resting state networks (RSNs) in patients with behavioral variant frontotemporal dementia (bvFTD) by using resting state functional MRI (rfMRI). The group specific RSNs were identified by high model order independent component analysis (ICA) and a dual regression technique was used to detect between-group differences in the RSNs with *p <* 0*.*05 threshold corrected for multiple comparisons. A *y*-concatenation method was used to correct for multiple comparisons for multiple independent components, gray matter differences as well as the voxel level. We found increased connectivity in several networks within patients with bvFTD compared to the control group. The most prominent enhancement was seen in the right frontotemporal area and insula. A significant increase in functional connectivity was also detected in the left dorsal attention network (DAN), in anterior paracingulate—a default mode sub-network as well as in the anterior parts of the frontal pole. Notably the increased patterns of connectivity were seen in areas around atrophic regions. The present results demonstrate abnormal increased connectivity in several important brain networks including the DAN and default-mode network (DMN) in patients with bvFTD. These changes may be associated with decline in executive functions and attention as well as apathy, which are the major cognitive and neuropsychiatric defects in patients with frontotemporal dementia.

#### **Keywords: default, resting state, functional MRI, dorsal attention network, frontotemporal dementia**

#### **INTRODUCTION**

Frontotemporal lobar degeneration (FTLD) is a clinically and pathologically heterogeneous syndrome characterized by a progressive decline in behavior and/or language associated with degeneration of the frontal and anterior temporal lobes (Neary et al., 2005). FTLD is the second most frequent neurodegenerative disease leading to early onset dementia after Alzheimer's disease (AD). The most common clinical presentation of FTLD is behavioral-variant frontotemporal dementia (bvFTD). The prominent symptoms in bvFTD are a gradual decline in social behavior with loss of insight and decline in executive functions instead of typical memory problems.

Resting state functional MRI (rfMRI) can be used to study the intrinsic connectivity of brain networks in task-free settings by mapping temporally synchronous, spatially distributed, spontaneous low frequency (*<*0.08 Hz) blood oxygen level-dependent (BOLD) signal fluctuations (Fox and Raichle, 2007). rfMRI reports have revealed a number of functional networks in the brain, but mainly two major intrinsic connectivity networks, the default-mode network (DMN) and salience, have been studied in neurodegenerative diseases. The DMN is a posterior network that consists of the hippocampi, posterior cingulate cortex/precuneus, lateral parietal regions, and the rostromedial prefrontal cortex (Raichle et al., 2001). Regions of the DMN seem to participate in episodic memory and visuospatial imagery (Zhou et al., 2010). The salience network is a large anterior network that consists of the anterior cingulate cortex (ACC), orbital frontoinsula (FI), amygdala, and striatum and is related to socially-emotionally relevant information processing (Seeley et al., 2007a,b). Decreased connectivity in the DMN is consistently associated with AD (Greicius et al., 2004; Zhang et al., 2009; Gili et al., 2011), while reduction of connectivity in the salience network has been usually found in patients with bvFTD. However, the findings in patients with bvFTD are controversial (Zhou et al., 2010; Whitwell et al., 2011; Borroni et al., 2012). Disruption in other functional networks such as the dorsal attention network (DAN) has been found in AD (Li et al., 2012).

Previous rfMRI analyses have been done using relatively low model order (*<*30) independent component analysis (ICA) estimated from a single subject or group analyses (Zhou et al., 2010; Whitwell et al., 2011; Borroni et al., 2012). Detecting multiple networks from 1.5 T BOLD data from a single subject is not optimal in all occasions. Instead, group ICA offers much more robust delineation of identifiable networks (Kiviniemi et al., 2009; Smith et al., 2009; Abou-Elseoud et al., 2010). Also, the use of a higher model order in ICA detects more networks under detected at low model orders and introduces sub-network delineation of resting state networks (RSNs) at more detailed level separating noise more appropriately (Abou Elseoud et al., 2011, 2012). The problem with the more robust group ICA networks is still the challenge of acquiring accurate representations of networks from the individual level for a statistical group analysis.

The dual regression method has been introduced in order to overcome statistical inference problems (Filippini et al., 2009). Zuo and co-workers found that not only is the temporal concatenated ICA dual regression approach reliable, it produces more robust results than template matching ICA performed on an individual level (Zuo et al., 2010). This variability at the individual level seems to arise from the non-stationarity of the networks themselves, rather than the ICA method instability (Zuo et al., 2010; Kiviniemi et al., 2011; Hutchison et al., 2013). Moreover, group-level averaging seems to increase matching accuracy to RSN templates as well.

In order to obtain robust whole brain cortex network status with all relevant functional RSNs one still has to account for the multiple comparison problem introduced by the usage of several ICs. We have recently developed a *y*-concatenation method for correcting for multiple comparisons for multiple IC components as well as at the voxel level (Abou Elseoud et al., 2012). This correction enables the detection of statistically significant alterations between groups in all analysed RSNs of the brain cortex.

In bvFTD executive dysfunction is the prominent neuropsychological finding, and neurodegeneration is seen in frontal, temporal and insular areas including parts of the salience network (Neary et al., 1998, 2005; Rosen et al., 2002; Seeley et al., 2007a,b; Whitwell et al., 2009). Thus, we hypothesized to find decreased connectivity in the salience network associated with changes in executive networks. Our aim was to obtain a robust, fully data driven measure of the functional connectivity changes of all RSNs covering the whole brain. For this we performed time concatenated temporal ICA with a dual regression approach with y-concatenation statistics of bvFTD data compared to controls.

## **MATERIALS AND METHODS PATIENTS**

The patient group included 19 cases with bvFTD (nine male). A clinical diagnosis of bvFTD was made according to the criteria of Lund and Manchester (Neary et al., 1998; Rascovsky et al., 2011). Patients presenting other types of FTLD phenotype such as progressive aphasia and semantic dementia were excluded. All patients were examined in Oulu University Hospital at the Memory outpatient clinic of the Department of Neurology. The mean age at examination was 60.3 years (range 47–77 years). MMSE (Mini-Mental State Examination) score was on average 24.1 (17–30). Neuropsychological examination was performed within 6 months of the fMRI scan of each patient. Impairments in memory, language, executive, and visuospatial functions were then rated in three stages (absent, mild to moderate, severe). The declines in attention and executive functions were the most prominent findings in all of the patients, but there was also significant deterioration in other cognitive domains. Patients' reading and writing skills were normal. Dyspraxia was not present, and motor speed and control were at a normal level. Apathy was the most prevalent neuropsychiatric symptom (74%), but depression and irritability was also common (50%). The severity of the disease was rated as mild in nine patients, moderate in nine patients and severe in one patient (Piguet et al., 2011). Medications for neuropsychiatric symptoms were used in some of the patients (acetylcholinesterase inhibitors in three patients, memantine in two, neuroleptics in eight and valproate in three). Positive family history for any type of dementia was seen in 52.6% (*n* = 10) of patients. DNA samples were available from ten patients and five of them carried a C9ORF72 repeat expansion (Renton et al., 2011). Mutations in progranulin or microtubuleassociated protein tau genes were not found in any of the patients.

Age and gender-matched controls (*n* = 19) were also examined. Mean age at examination was 57.8 years (range 50–70). No psychiatric or neurological disorders or medications affecting the central nervous system were allowed. Beck's depression inventory (BDI) score was mean 2.7 (range 0–10) and MMSE mean was 28.9 (range 26–30).

Written informed consent was obtained from all of the patients or their guardians. The research protocol was approved by the Ethics Committee of the Northern Ostrobothnia Hospital District.

## **IMAGING PROTOCOL**

Resting-state BOLD data were collected on a GE Signa 1.5 T MRI scanner with an 8-channel parallel imaging-coil ASSET system (acceleration factor × 2) with an EPI GRE sequence (TR 1800 ms, TE 40 ms, 285 time points, 28 oblique axial slices, slice thickness 4 mm, inter-slice space 0.4 mm, covering the whole brain with an FOV of 25.6 cm × 25.6 cm with 64 × 64 matrix, and a flip angle of 90◦). Hearing was protected using ear plugs and motion was minimized by using soft pads fitted over the ears. The subjects were instructed to simply lay still inside the scanner with their eyes closed, think of nothing in particular and not to fall asleep. High-resolution T1-weighted 3D FSPGR BRAVO images were taken in order to obtain anatomical images for co-registration of the fMRI data to the standard space coordinates and to investigate voxel-wise changes in the gray matter volume.

### **STRUCTURAL ANALYSIS**

Structural data were analysed with FSL-VBM (www.fmrib.ox.ac.uk/fsl), a voxel-based morphometry style analysis (Ashburner and Friston, 2000; Good et al., 2001). Firstly, structural images were brain-extracted using BET (Smith, 2002). Next, tissue-type segmentation was carried out using FAST4 (Zhang et al., 2001). The resulting gray matter partial volume images were aligned to MNI152 standard space using nonlinear registration FNIRT in FSL (www*.*fmrib*.*ox*.*ac*.*uk/ analysis/techrep), which uses a b-spline representation of the registration warp field (Rueckert et al., 1999). The resulting images were averaged to create a study-specific template, to which the native gray matter images were then non-linearly re-registered. The registered partial volume images were then modulated to correct for local expansion or contraction by dividing by the Jacobian of the warp field. The modulated segmented images were then smoothed with an isotropic Gaussian kernel with a sigma of 3 mm. Finally, voxelwise GLM was applied using FSL's randomize, which is a permutation-based non-parametric testing, correcting for multiple comparisons across space with *p <* 0*.*05 threshold.

#### **fMRI DATA PRE-PROCESSING**

Data pre-processing was carried out with FSL tools. Head motion in the fMRI data was corrected using multi-resolution rigid body co-registration of volumes, as implemented in the MCFLIRT software (Jenkinson et al., 2002). Brain extraction was carried out for motion corrected BOLD volumes with optimization of the deforming smooth surface model, as implemented in the BET software (Smith, 2002). This procedure was verified with visual inspection of the extraction result. The resulting image data was used as a mask for a secondary brain extraction. Multi-resolution affine co-registration as implemented in the FLIRT software was used to co-register fMRI volumes to 3D FSPGR volumes of the corresponding subjects and further the 3D FSPGR volumes to the MNI152 standard space. The images were transformed to 4 mm cubic voxels with 5 mm FWHM smoothing. There were no differences in head motion parameters in absolute [FTD (0*.*30 ± 0*.*12 mm) vs. control (0*.*26 ± 0*.*13 mm, *t*-test *p* = 0*.*36)] or relative [FTD (0*.*07 ± 0*.*03 mm) vs. controls (0*.*06 ± 0*.*03 mm, *t*-test *p* = 0*.*34)] between the study groups. Maximum absolute (2.9 mm) and relative (3.1 mm) head motion were below the voxel size in all subjects.

#### **FUNCTIONAL CONNECTIVITY ANALYSIS**

ICA analysis has been conducted as previously described (Abou Elseoud et al., 2011). Briefly, ICA analysis was carried out using FSL 4.1.4 MELODIC software implementing probabilistic independent component analysis (PICA) (Beckmann and Smith, 2004). A multisession temporal concatenation tool in MELODIC was used to perform PICA related pre-processing and data conditioning in the group analysis setting. Spatial ICA using 70 independent component maps (IC maps) was applied to detect RSNs from the control group. Control group data was chosen for two reasons: Firstly, our experience is that a combined groupICA having both cases and controls produces averaged maps of both groups which are then less sensitive in detecting differences between the groups in dual regression. Secondly, control data groupICA results are more robust match with previous healthy control data groupICA templates without disease-related alterations (Kiviniemi et al., 2009; Smith et al., 2009). Variance normalization was used. The IC maps were thresholded using an alternative hypothesis test based on fitting a Gaussian/gamma mixture model to the distribution of voxel intensities within spatial maps and controlling the local false-discovery rate at *P <* 0*.*5 (Beckmann and Smith, 2004; Beckmann et al., 2005). Thirty-six RSNs were identified as anatomically and functionally classical RSNs upon visual inspection by an experienced neuroradiologist (VK) using previously described criteria (Kiviniemi et al., 2009; Smith et al., 2009; Abou-Elseoud et al., 2010). The 36 RSNs are presented in **Figure 1**.

The analysis for the differences between groups was carried out using an FSL dual regression technique that allows for voxel-wise comparisons of resting-state fMRI (Filippini et al., 2009; Littow et al., 2010; Veer et al., 2010; Abou Elseoud et al., 2011). This involves (A) using the group-ICA spatial maps in a linear model fit against the separate fMRI data sets, resulting in matrices (time-course matrices) describing the temporal dynamics for each component and subject, and (B) using these time-course matrices to estimate subject-specific spatial maps. The ICA template for the dual regression was selected from the healthy control data. The dual regression analysis was performed both with and without variance normalization (FSL414 dual\_regression command with des norm-option 1 or 0, respectively), since the results have a different emphasis on the spatial or amplitude of the BOLD signal depending on normalization (Allen et al., 2012a,b). With variance normalization, the dual regression reflects differences in both activity and spatial spread of the RSN. Without normalization, only spatial alterations are reflected in principle.

As a statistical analysis the different component maps are collected across subjects into single 4D files (1 per original ICA map) and tested voxel-wise for statistically significant differences between the groups using FSL randomize nonparametric permutation testing, with 5000 permutations, using a threshold-free cluster enhanced (TFCE) technique to control for multiple comparisons (Nichols and Holmes, 2002). The hypothesized differences between groups were calculated using a *p <* 0*.*05 threshold with voxel-wise changes in the FSL randomize tool. A newly developed inter-IC concatenation technique was used in order to control for multiple comparisons across the detected IC differences (Abou Elseoud et al., 2012). After discarding noise, motion and other artifact components, all the 36 RSN ICs were concatenated in the y-direction and fed to the FSL randomize tool as a joint dataset. After 5000 permutations the resulting datasets were separated with fslroi and thresholded for *p <* 0*.*05 correcting for type I error for the selected multiple ICs. As a new step, a gray matter regressor image was also fed in a similar fashion such that it was concatenated in the y-direction to control for gray matter differences with FSL randomize using—vxl and—vxf options.

The Juelich histological atlas incorporated in FSL and the Harvard-Oxford cortical and subcortical atlases (Harvard Center

**FIGURE 1 | Resting-state networks from the control group identified as anatomically and functionally classical RNSs and which were used for the dual regression analysis.** The normal resting-state networks are shown in FSL red-yellow color encoding using a 3 *< z*-score *<* 9 threshold.

for Morphometric Analysis) which are provided with the FSL4 software were used to identify the anatomical characteristics of the resulting PICA maps. The FSL4 fslstats and fslmaths tools were used to calculate the number of non-zero voxels in the selected difference maps, and their *t*-score values.

## **RESULTS**

The differences between patients with bvFTD and healthy controls were markedly smaller in the dual regression without variance normalization than with normalization. Thus, the differences between groups in the network shape seemed to be relatively limited when gray matter differences were adjusted. The variance normalized and non-normalized dual regression results suggest that the functional connectivity of the baseline RSNs is altered rather than the shape of the network in patients with bvFTD when compared with healthy controls. Based on this we further performed y-concatenation correction for multiple ICA components with the variance normalized (des norm = 1) results. We corrected for both multiple ICs and gray matter volume simultaneously. After this analysis, the number of ICs with significant differences reduced from fifteen detected after only voxel-level correction to six (**Table 1**, **Figure 2**).

Using this protocol, increased connectivity was found in several networks within patients with bvFTD compared to the control group. The most prominent enhancement was seen in the right frontotemporal area and insula (IC 25). Increased connectivity was also detected in the left DAN (IC 55), in the DMN (anterior paracingulate gyrus; IC 17) and in anterior parts of the frontal pole (IC 12). Minor changes were also seen in the visual network in the occipital region (IC 3) and in the premotorpostsensory network (IC 33).

The bvFTD group was divided into two separate groups according to the degree of severity of the disease, mild (*n* = 9) and moderate/severe (*n* = 10). There were no significant differences between mild and moderate to severe subgroups in functional connectivity.

In structural analysis, widespread gray matter atrophy was detected (**Table 2**, **Figure 3**). Atrophy was most prominent in the right temporal lobe in addition to the precuneus cortex and posterior cingulate gyrus. Mild atrophy was also detected in both hippocampi. After the adjustment for gray matter differences, the increased connectivity was predominantly placed around the atrophic area (**Figure 3**).

## **DISCUSSION**

This is the first report of RSNs of the whole brain cortex in patients with bvFTD using a new correction method that allows the assessment of several brain networks. Connectivity changes as well as changes in the shape of the brain networks were analysed. The most extensive increase in connectivity was detected in the frontal poles bilaterally, in the right insular cortex and the ACC in several networks (IC 12, IC 17, IC 25). This area is associated with the human mirror neuron system and the von Economo cells, which are thought to be crucial in social cognition (Seeley et al., 2006, 2007a,b). These cells are destroyed in patients with bvFTD and atrophy in this area is associated with a decline in behavior and loss of empathy and insight in bvFTD. In a recent study, prefrontal hyperconnectivity has been associated with apathy in patients with bvFTD (Farb et al., 2012). Apathy was the most prevalent neuropsychiatric symptom in the present study and may be associated with hyperconnectivity in the prefrontal areas, which confirms the previous findings. Farb and colleagues also detected that prefrontal hyperconnectivity was associated with dementia severity, but this phenomenon was not seen in our patients (Farb et al., 2012).

Impairment in executive functions and attention are prominent neuropsychological findings in bvFTD patients. Significant increase in connectivity was detected in the left DAN (**Table 1**, **Figure 2**; IC 55). However, we did not detect any changes in the executive network itself (IC 42), which confirms the previous finding of normal executive networks in patients with bvFTD (Filippi et al., 2012). In previous studies the DAN has been found to be disrupted in AD, but not in bvFTD (Filippi et al., 2012; Li et al., 2012). Despite the normal executive network, a decline in executive functions may be explained by changes in the attention networks and the destruction of several frontal control networks (IC12, 17, 25).

Increased connectivity was also found in the frontal subnetwork of the DMN (IC 17). In several studies the disruption of the DMN is consistently associated with AD (Greicius et al., 2004; Zhang et al., 2009; Gili et al., 2011), but the findings are controversial in patients with bvFTD (Zhou et al., 2010; Whitwell et al., 2011; Borroni et al., 2012). In bvFTD, increased DMN connectivity has often been associated with decreased connectivity in the salience network (Zhou et al., 2010; Whitwell et al., 2011; Borroni et al., 2012; Farb et al., 2012). However, in the present study, decreased connectivity was not detected in the salience network even if increased connectivity in the DMN was found.

In structural analyses, the most prominent gray matter atrophy was detected in the right temporal region, precuneus, and posterior cingulate. Atrophy in the posterior cingulate is typically associated with AD and decreased connectivity in DMN. The research reports about gray matter atrophy in posterior brain areas in patients with bvFTD have been controversial. Broadly, precuneus and posterior cingulate seem to be preserved in bvFTD, however gray matter atrophy of these areas has been detected in some studies (Du et al., 2007; Zhou et al., 2010; Hartikainen et al., 2012). In present study increased functional connectivity was consistently located around the atrophic areas. The centrifugal disposition of increased functional connectivity around the atrophic areas suggests plasticity related shifting of function to areas still capable of functioning. It is noteworthy that multimodal imaging in detecting functional changes in diseases with abnormal anatomy is demanding. For example, how is the variance normalization of the BOLD signal affected by shifted and increased function; is it merely overlap of functional connectivity or plasticity effects? This question currently remains unanswered. In this study we were able to correct our results for both multiple comparisons related to the selecting of 36 RSNs and for gray matter differences known to exist in bvFTD.

We found only increased connectivity in patients with bvFTD compared to controls. Several factors may have an influence


**Table 1 | Functional brain regions showing increased functional connectivity in patients with bvFTD compared to healthy controls.**

*Significant differences in functional connectivity were detected in fifteen RSNs by voxel level correction. By Inter-IC corrected y-concatenation for all RSNs significant differences in functional connectivity were detected in six RSNs. Significant differences are demonstrated by; the anatomical areas involved, number of voxels, MNI coordinates (in mm) of the involved anatomical areas, t-score and its standard deviation. Abbreviations: IC, independent component; R, right; L, left; DAN, dorsal attention network.*

upon our results. We have used different data analysing methods compared to the previous rfMRI studies (Zhou et al., 2010; Whitwell et al., 2011; Borroni et al., 2012; Farb et al., 2012; Filippi et al., 2012). Higher model order (a.k.a 70) was used, which yields a more detailed RSN partition and reduces the presence of noise in the maps (Kiviniemi et al., 2009; Smith et al., 2009; Abou-Elseoud et al., 2010). Furthermore, global mean regression may be involved with unpredictable anti-correlations and this may also be one source of differences between our results and others' (Murphy et al., 2009). We also used a group ICA

**functional connectivity in patients with bvFTD.** The differences in resting state networks corrected for multiple comparison at voxel level in green and for y-concatenation correction for including all RSNs in lilac-blue, threshold *p <* 0*.*05. The differences between groups are also adjusted for gray matter differences. The involved RSN is presented in red-yellow color encoding with (3.6 *< z*-score *<* 6) threshold. Arrows in ICs 3, 17, and 33 indicate the y-concatenated results.

#### **Table 2 | Regional atrophy detected by structural MRI.**

dual regression approach in this study. Matching individual level ICA maps is more prone to errors when compared to group map templates (Zuo et al., 2010). We also share this experience in this dataset. We analysed individual level ICA maps with low (20) model order, but had considerable difficulty in detecting a robustly identifiable salience and other high cognitive networks from individual ICA runs for each subject, especially when compared to group templates. This is not a problem of ICA *per se*, since it has been known to be robust in detecting fMRI sources (Kiviniemi et al., 2009; Smith et al., 2009). Actually, it may be the other way around; ICA is very sensitive to sparsity (Daubechies et al., 2009). On the individual level, the RSNs have marked spatiotemporal non-stationarity due to strong momentary neuronal avalanches (Chang and Glover, 2010; Kiviniemi et al., 2011; Hutchison et al., 2013; Liu and Duyn, 2013; Palva et al., 2013). The neuronal avalanches arising within RSN nodes have unique spatial connectivity distributions and match only with group level RSN templates when strongly averaged in both time and space (Zuo et al., 2010; Liu and Duyn, 2013). Dual regression maps may therefore provide a closer matching representation of an individual subject's RSN compared to ICA based maps.

The advantage of the present method is the whole brain cortex coverage and higher hierarchical level of sub-networks (Kiviniemi et al., 2009; Abou-Elseoud et al., 2010; Littow et al., 2010). Moreover, the high model order enables increased accuracy of regressors of also the unwanted artifacts such as pulsating blood vessels, compartmental CSF pulsation/jet and motion artifacts that cannot be identified with other methods. The regression of these artifacts is crucial for the correct analysis of group differences in any kind of connectivity measurement. ICA dual regression offers a simultaneous data-driven regression of both artifacts and RSNs. Importantly, with group ICA, the effects of curved and often individually different cerebral artery and CSF


*R, right; L, left.*

**FIGURE 3 | Regional atrophy was detected in several frontal and temporal regions and also in the posterior cingulate cortex, occipital cortex, and occipital poles.** Increased connectivity was constantly placed around the atrophied area. Atrophic regions are in red, difference in the RSNs are in yellow (bvFTD *>* controls) and overlapping areas are in orange color. Arrows in ICs 3, 17, and 33 indicate the difference in the RSN.

## **REFERENCES**


pulsations become separated by the best way within the analysed group as a whole, also with individual weighting in their time series. The adequate modeling of blood vessel noise may have an effect on the salience network results in the insular cortex near the medial cerebral artery branches. Advanced spiral in/out scanning sequences at the higher 3 T magnetic field are also likely to be more sensitive than conventional 1.5 T EPI sequences in providing frontal cortex/insular data at the individual level (Zhou et al., 2010; Whitwell et al., 2011; Farb et al., 2012; Filippi et al., 2012).

Despite careful diagnostics, long follow-up and genetic confirmation of bvFTD, it is possible that some patients with atypical AD may be included in the study cohort. The wide range in disease severity may affect the rfMRI results even though no difference was seen between the patient groups with different disease severity. Neuroleptic and cholinergic drugs may also modify results. However, there are no rfMRI data available concerning the effects of these drugs.

In conclusion, this is the first analysis of the whole brain cortex networks using high model order ICA in patients with bvFTD. Increased connectivity was seen in the left DAN and frontal control networks neighboring executive network, as well as the frontal poles and insula. These changes may explain bvFTD associated cognitive and neuropsychiatric symptoms. In general, the increase in functional connectivity centrifugally was surrounding the atrophied areas, which suggest plasticity related shifting of neuronal activity into intact brain structures.

## **ACKNOWLEDGMENTS**

This work was supported by grants from the Finnish Academy [grants 117111 and 123772 (Vesa Kiviniemi)], Finnish Medical Foundation (Vesa Kiviniemi, Anne M. Remes), Finnish Neurological Foundation (Vesa Kiviniemi), KEVO grants from Oulu University Hospital (Vesa Kiviniemi, Anne M. Remes), National Graduate School of Clinical Investigation (Riikka Rytty), the Intramural Research Programs of the NIH, and National Institute on Aging (Z01-AG000949- 02)(Bryan J. Traynor) the ALS Association, AriSLA, Packard Center for ALS research, FIGC, Microsoft Research (Bryan J. Traynor), and the Myasthenia Gravis Foundation (Bryan J. Traynor).

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**Conflict of Interest Statement:** Dr. Traynor has a patent pending on the diagnostic and therapeutic uses based on the discovery of the hexanucleotide repeat expansion in C9ORF72. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 23 March 2013; accepted: 25 July 2013; published online: 26 August 2013.*

*Citation: Rytty R, Nikkinen J, Paavola L, Abou Elseoud A, Moilanen V, Visuri A, Tervonen O, Renton AE, Traynor BJ, Kiviniemi V and Remes AM (2013) GroupICA dual regression analysis of* *resting state networks in a behavioral variant of frontotemporal dementia. Front. Hum. Neurosci. 7:461. doi: 10.3389/fnhum.2013.00461*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Rytty, Nikkinen, Paavola, Abou Elseoud, Moilanen, Visuri, Tervonen, Renton, Traynor, Kiviniemi and Remes. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Dysregulated but not decreased salience network activity in schizophrenia

## *Thomas P. White\*†, James Gilleen† and Sukhwinder S. Shergill*

*Department of Psychosis Studies, Institute of Psychiatry, King's College London, London, UK*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Han Zhang, Hangzhou Normal University, China Yuan Zhou, Chinese Academy of Science, China*

#### *\*Correspondence:*

*Thomas P. White, Cognition, Schizophrenia and Imaging e-mail: thomas.1.white@kcl.ac.uk Laboratory, Department of Psychosis Studies, PO Box 96, Institute of Psychiatry, King's College London, de Crespigny Park, London, SE5 8AF, UK.*

*†These authors have contributed equally to this work.*

Effective estimation of the salience of environmental stimuli underlies adaptive behavior, while related aberrance is believed to undermine rational thought processes in schizophrenia. A network including bilateral frontoinsular cortex (FIC) and dorsal anterior cingulate cortex (dACC) has been observed to respond to salient stimuli using functional magnetic resonance imaging (fMRI). To test the hypothesis that activity in this salience network (SN) is less discriminately modulated by contextually-relevant stimuli in schizophrenia than in healthy individuals, fMRI data were collected in 20 individuals with schizophrenia and 13 matched controls during performance of a modified monetary incentive delay (MID) task. After quantitatively identifying spatial components representative of the FIC and dACC features of the SN, two principal analyses were conducted. In the first, modulation of SN activity by salience was assessed by measuring response to trial outcome. First-level general linear models were applied to individual-specific time-courses of SN activity identified using spatial independent component analysis (ICA). This analysis revealed a significant salience-by-performance-by-group interaction on the best-fit FIC component's activity at trial outcome, whereby healthy individuals but not individuals with schizophrenia exhibited greater distinction between the response to hits and misses in high salience trials than in low salience trials. The second analysis aimed to ascertain whether SN component amplitude differed between the study groups over the duration of the experiment. Independent-samples *T*-tests on back-projected, percent-signal-change scaled SN component images importantly showed that the groups did not differ in the overall amplitude of SN expression over the entire dataset. These findings of dysregulated but not decreased SN activity in schizophrenia provide physiological support for mechanistic conceptual frameworks of delusional thought formation.

**Keywords: schizophrenia, salience, cortical networks, fMRI, reward**

## **INTRODUCTION**

The finite capacity of our attentional and behavioral resources necessitates that we assign preferential salience to certain environmental stimuli, while limiting responses to others. Appropriately selecting the stimuli to which we assign salience is therefore a key component of adaptive behavior. Relatedly, the allocation of incentive salience to both primary and more abstract rewarding stimuli potently modulates behavior (Robbins and Everitt, 1996).

Electrophysiological recordings from the macaque striatum show that phasic dopaminergic responses to rewarding stimuli temporally mimic the prediction error of reward-value models, implicating this region as the source of a reinforcement signal required to adjust the probability of subsequent action selection (Schultz et al., 1997). Comparable human blood oxygenationlevel dependent (BOLD) responses in striatum to novel, aversive and intense stimuli suggest that this response indexes saliencerelated features such as familiarity, contextual relevance and predictability more generally (Zink et al., 2004). A complementary model of the role of phasic striatal dopamine therefore proposes that the basal ganglia concertedly act as a centralized selection device, allocating attentional resources between competing motor programs in a contextually-relevant manner (Redgrave et al., 1999). The reward findings uphold this model insofar as test animals are generally required to shift attention to rewarding stimuli and carry out a motor program to realize their consumption.

Human BOLD findings also imply an analogous, attentionswitching function in a cortical network comprising dorsal anterior cingulate cortex (dACC) and bilateral frontoinsular cortex (FIC), subsuming anterior insula (AI), and inferior frontal gyrus (IFG). These regions are consistently coactive with task-specific regions when stimuli are modulated in terms of cognitive, emotional and homeostatic salience, which implies they fundamentally code salience (Menon, 2011); they also exhibit a temporal signature dissociable from task-specific regions via seed-region correlation and independent component analysis (ICA) suggesting that they represent a salience network (SN; Seeley et al., 2007), Moreover, analyzing Granger-causal relationships from multi-task functional magnetic resonance imaging (fMRI) data, Sridharan et al. (2008) observed that right FIC activity consistently preceded and predicted activity in regions of the default mode network (DMN; medial prefrontal cortex, precuneus and bilateral angular gyrus), where activity is typically greater during times of introspection, and also regions of the central executive network (CEN; bilateral dorsolateral PFC and posterior parietal cortex), where activity is typically greater when attention is focused on environmental stimuli. They suggested that right FIC was therefore crucial in switching between these two contrasting modes of brain function.

The cortical SN appears to be a focus of pathology in schizophrenia. Structurally, these regions are amongst the most consistently observed sites of gray-matter reduction in the disorder (Ellison-Wright et al., 2008), and focal alterations in SN volume are observed to be associated with the severity of reality distortion in schizophrenia (Palaniyappan et al., 2011). Reduced functional connectivity has also been observed within the SN in schizophrenia compared to controls during volitional eye saccades (Tu et al., 2010) and at rest (Tu et al., 2012). However, within-SN dysconnectivity is not unequivocally apparent in schizophrenia. Resting-state connectivity between a FIC seed and other SN constituents was recently reported to be unaffected by schizophrenia, despite significant connectivity reductions in both DMN and CEN (Woodward et al., 2011). Combined assessment of within- and between-network connectivity in schizophrenia as compared with controls has revealed less consistent functional relationships within the SN and between the SN and DMN during passive perceptual stimulation (White et al., 2010a).

The monetary incentive delay (MID) task presents an adaptable framework for assessing SN functional modulation in schizophrenia. Investigating gains (but not losses) Walter et al. (2009, 2010) demonstrated that activity in dACC and a region encompassing AI and ventrolateral prefrontal cortex is more sensitively modulated at outcome in healthy individuals than schizophrenia patients in tasks that vary reward magnitude and those that vary reward probability. Furthermore, Waltz et al. (2010) reported significant group-by-outcome interactions in right FIC and pregenual ACC BOLD responses in a MID task involving both gains and losses, with controls exhibiting greater activity for gains than losses and patients exhibiting greater responses for losses than gains. While these latter findings are in line with anhedonic symptoms of schizophrenia, from a biological perspective salience should be attributed to both positive and negative events—potential rewards and dangers must both be appropriately detected. As a result, salience coding should be expectedly heightened to both positive and negative extremes. Moreover, the success with which potential losses and gains are respectively obtained or avoided should additionally contribute to salience coding to maximally adapt subsequent behavioral output.

The notion that individuals with schizophrenia exhibit not just muted salience attribution to conventionally salient stimuli but also aberrantly excessive salience attribution at other times is central to dominant theories of delusion formation (Kapur, 2003; Kapur et al., 2005). This suggests that attenuated reward signals in cortical SN and striatum (for review, see Heinz and Schlagenhauf, 2010) paint an incomplete picture. Here, we use spatial ICA of fMRI data to identify the cortical SN in individuals with schizophrenia and matched controls during performance of a modified MID task. We present analyses conducted to assess the explicit hypotheses that: (1) cortical SN activity focused in both dACC and FIC will be modulated by the salience of rewarding monetary stimuli at reward outcome in healthy individuals; (2) SN modulation by task and performance will be diminished in schizophrenia; and (3) despite this putative dysregulation in schizophrenia, the cortical SN will be no less evident in these individuals than in healthy controls over the duration of the task.

## **MATERIAL AND METHODS**

## **PARTICIPANTS**

Twenty individuals satisfying DSM-IV criteria for schizophrenia (American Psychiatric Association, 1994) and 13 healthy controls were recruited to take part in the study. All participants were right-handed and groups did not differ significantly in terms of age or intelligence quotient (IQ) assessed using the National Adult Reading Test (NART; Nelson, 1982). Summary demographic and psychiatric-symptom details are provided in **Table 1**.

Diagnosis of schizophrenia was confirmed by assessment of clinical case notes and confirmation of suitability by each individual's consultant psychiatrist. Patients were recruited in a clinically stable condition and were excluded if presenting evidence of comorbid diagnosis or a medical disorder resulting in an IQ of less than 85. Symptom severity and classification were assessed using the Positive and Negative Syndrome Scale (PANSS; Kay et al., 1987). Sixteen patients were receiving treatment with atypical antipsychotic medications: olanzapine (*n* = 8); risperidone (*n* = 4); quetiapine (*n* = 2); clozapine (*n* = 1); and sulpiride (*n* = 1). The remaining four patients were receiving typical antipsychotic medications: zuclopenthixol (*n* = 2); flupentixol (*n* = 1); and chlorpromazine (*n* = 1). Chlorpromazine equivalent doses were computed for oral antipsychotic medications using data presented by Woods (2003). In the case of risperidone Consta injection, 25 mg Consta injection every 14 days was taken to equate to 4 mg oral risperidone per day, in accordance with the British National Formulary recommendation (Joint Formulary Committee, 2008).

#### **Table 1 | Sample details.**


*NART, national adult reading test (Nelson, 1982); PANSS, positive and negative signs and symptoms of schizophrenia (Kay et al., 1987).*

The average chlorpromazine-equivalent dose was 292.7 (range: 100–700) mg/day.

Healthy volunteers were recruited by local advertisement and excluded from study if: they reported a personal history of psychiatric or neurological illness or diagnosis of schizophrenia in a first-degree relative; they exhibited an IQ of less than 85; or they had a recent history of illicit substance use.

Ethical approval was provided by Essex 1 Research and Ethic Committee (08/H0301/116). All participants provided informed written consent and were given an inconvenience allowance for study participation plus additional payment proportional to task performance.

#### **EXPERIMENTAL PROCEDURE**

Participants performed a modified MID task (Knutson et al., 2001) comprising three 15-min sessions, each containing 48 trials split equally between the different experimental conditions. Participants viewed a screen, onto which visual stimuli were projected, using mirrors mounted on the scanner headcoil. Trials were categorized as either: win trials, in which pressing the button within a target time window resulted in the relevant reward (hit), while failure to do so (miss) resulted in no financial change; or loss trials, in which poor performance (miss) led to loss of the relevant monetary value, while good performance (hit) led to avoidance of this loss. Win and loss trials were further categorized according to the magnitude of their potential financial value (£5, £0.50, and £0). This permitted evaluation of graded incentive salience. As trials with a large potential reward/loss have greater financial implications, they should be considered more salient. Each trial began with a cue notifying trial type (win vs. loss; magnitude of reward), followed by a probe indicating when to perform the right index finger button press and then, following a delay, visual feedback indicating trial outcome. On completion of each trial, participants were required to manually report their feeling of subjective contentment using a visual analog scale (VAS), ranging from satisfied (1) to dissatisfied (9). A schematic representation of an example trial, including the timelines of phase 1 (anticipation to act), phase 2 (anticipation of outcome), and phase 3 (outcome), and the stimuli presented in the experiment is provided in **Figure 1**.

## **MRI DATA ACQUISITION**

Four hundred and forty-eight gradient-echo echo-planar BOLD images (TR/TE: 2000/25 ms, flip angle: 75◦, matrix: 64 × 64) were acquired on a 3 Tesla GE Excite II MR scanner (GE Healthcare, USA) during each run of the task. Each whole-brain image contained 38 non-contiguous slices of 2.4-mm thickness separated by a distance of 1 mm, and with in-plane isotropic voxel resolution of 3.4 mm.

## **BEHAVIORAL DATA ANALYSIS**

The time taken to make the button-press response following presentation of probe stimulus (reaction time; RT) was recorded for each trial. To reduce the effects of trials in which no response was made, the within-session median RT was calculated and these values averaged to give subject-specific mean RTs for each condition

over the experiment as a whole. A repeated-measures ANOVA test was performed within SPSS (SPSS Inc., USA) to assess differences related to trial valence (win or loss), salience (£5 or £0.50), and group (healthy or schizophrenia). Similarly, the hit rate (percentage calculated on the basis of number of hits and total trials) was calculated for each condition averaged across sessions. Again, a repeated-measures ANOVA was conducted to assess differences relating to trial valence, salience and group. *Post-hoc T*-tests were performed to assess the specifics of significant main effects and between-factor interactions.

#### **fMRI DATA PREPROCESSING AND ANALYSIS**

fMRI data were preprocessed using SPM5 (Wellcome Department of Imaging Neuroscience, University of London, UK). Data were realigned to the first image of the series, normalized to a standardbrain template and smoothed using an 8-mm FWHM Gaussian kernel.

Spatial ICA was performed on the pre-processed data using the Group ICA fMRI Toolbox (GIFT; http://icatb*.*sourceforge*.*net) within Matlab 7.8 (MathWorks, USA). GIFT uses a temporal concatenation approach during which data reduction is performed via multi-staged principal component analysis (PCA) and aggregation to generate common-group maps. These components are subsequently back-projected onto each individual's data to create subject-specific spatial maps with corresponding time-courses (Calhoun et al., 2008, 2009).

Prior to ICA, data dimensionality was estimated using Minimum Length Description criteria to be 64. Since model order determines network spatial characteristics including subnetwork parcellation, ICA was constrained to produce 64 components. ICA was performed using the Infomax algorithm (Bell and Sejnowski, 1995), and repeated five times with Icasso (Himberg et al., 2004) to maximize the stability of the derived components. Components were also scaled according to percent signal change to facilitate inter-subject comparisons of their timecourses. Back-reconstruction was carried out using GICA3 on the basis of previous empirical support for the accuracy of this method (Erhardt et al., 2011).

In light of previous observations that the dACC and FIC features of the SN are customarily dissociated into separate fMRI spatial components (Sridharan et al., 2008; White et al., 2010a), it was considered appropriate to attempt to identify these SN features independently. To this end, binary masks of (1) the dACC and (2) the FIC, were constructed from a downloadable SN map (http://findlab*.*stanford*.*edu/research; Shirer et al., 2012). Each binary mask was dilated by one voxel to favor components whose outside-mask loadings were greater in regions immediately proximate to the specified masks. Goodness-of-fit (GOF) was then assessed between each of these masks and the 64 whole-sample component maps. GOF was calculated by subtracting the mean *Z*-score of voxel values outside the mask from the mean *Z*-score of voxels within it (Greicius et al., 2004; Seeley et al., 2007) using Matlab 7.8 (MathWorks, USA). Components were ranked according to GOF and the best-fit component for each binary mask selected for subsequent investigation.

Having identified the two best-fit SN whole-sample components, their voxel-wise robustness was assessed statistically for the whole sample using one-sample *T*-tests conducted on wholebrain, back-reconstructed loading images for each participant. This identified the voxels with strongest loadings for these components, although it must be stressed that each component is a whole-brain component. To identify regions of strong positive loading, significance was ascribed according to a cluster-level criterion based on the spatial extent of suprathreshold voxel clusters. Voxel-level inclusion of *P <* 0*.*001 and cluster-level significance of *P <* 0*.*05 family-wise error corrected were used throughout this work. In addition, two-samples *T*-tests were performed to investigate between-group differences in the amplitude of expression of the SN components over the entire dataset. Little difference between individuals with schizophrenia and healthy controls was predicted here according to the hypothesis that SN activity is dysregulated rather than diminished.

To investigate modulation of SN activity by task-related events at outcome, first-level GLMs were applied to the back-projected time-courses of the two SN components, with the hypothesis that these events would predict activity less in individuals with schizophrenia than in healthy controls. This technique permits assessment of distributed, event-related brain activity and has advantages over the conventional voxel-wise, mass-univariate approach including: (1) it reduces the chances of Type-1 error inherent to mass-univariate analyses on account of the large number of tests involved in the latter; (2) it reduces the chances of Type-2 error likely in the latter as a consequence of attempts to stringently correct for these multiple tests; and (3) it presents a readily understandable summary statistic for a distributed feature of brain activity, which has been previously identified by virtue of its temporal congruity. The conjoined use of GLMs and ICA has for these reasons been successfully used in wide-ranging settings (for recent examples, see Caulo et al., 2011; Luckhoo et al., 2012; White et al., in press).

For the current GLM-ICA analyses we modeled the timecourse of the BOLD response associated with the presentation of the visual stimuli throughout the task, by convolving a vector of delta functions for the onset and durations of these stimuli with the canonical haemodynamic response function. Regressors were included in the GLMs for events split by cue value, performance and phase. This resulted in 30 conditions (5 cue values × 2 performance outcomes × 3 phases) for each of the three sessions. When necessary, regressors were included for void trials during which no button-press response was registered. Sessionspecific realignment parameters were also included in the GLMs as covariates of no interest. Resulting, individual-specific GLMs were applied to the time-courses of the best-fit FIC and dACC components. Beta coefficients for responses at trial outcome were then exported into SPSS (SPSS Inc., USA) for statistical appraisal. Repeated-measures ANOVAs were carried out to assess the effects on the beta coefficients of within-subjects factors of reward salience (£5 or £0.50), performance (hit or miss) and trial valence (win or loss), and the between-subject factor of group (healthy or schizophrenia) for each of the best-fit components separately. *Post-hoc* confirmatory *T*-tests were performed to assess the direction of significant effects.

The relationship between psychiatric symptomatology and SN task modulation was assessed in the schizophrenia group using bivariate Pearson correlation between above-calculated beta coefficients and PANNS positive, negative and general psychopathology scores. Similarly, the relationship between antipsychotic medication and SN task modulation was investigated by assessing correlation between beta coefficients and chlorpromazine equivalent dosage in the same individuals. In a further analysis to investigate whether medication class predicted SN modulation, a repeated-measures ANOVA was conducted in the schizophrenia group to assess the effects on beta estimates of salience, performance and trial outcome as in the previous analyses; however, this analysis also included a binary covariate detailing whether each individual had been prescribed typical or atypical antipsychotic medication.

## **RESULTS**

#### **BEHAVIOR**

Hit rate and RT results are summarized in **Figure 2**; statistical findings from the repeated-measures ANOVA conducted on these measures is provided in **Table 2**. There was a significant valence-by-salience interaction [*F(*1*,* <sup>32</sup>*)* = 9*.*02, *P* = 0*.*005] in RT. Subsequent *T*-tests demonstrated that while RT was significantly less for large win trials compared to small win trials [*T(*32*)* = 3*.*76, *P* = 0*.*001], no significant difference was observed between large and small loss trials. A valence-by-group interaction [*F(*1*,* <sup>32</sup>*)* = 6*.*57, *P* = 0*.*015] was also observed in RT. Patients demonstrated significantly shorter RTs averaged across win trials (246*.*03 ± 24*.*07 ms) as compared to loss trials [250*.*42 ± 23*.*93 ms; *T(*19*)* = 2*.*84, *P* = 0*.*011]. By contrast, healthy individuals exhibited non-significant differences in RT between these conditions [win trials: 238*.*82 ± 23*.*98 ms; loss trials: 235*.*26 ± 20*.*58 ms; *T(*12*)* = 1*.*19, *P* = 0*.*255]. There was an additional weak trend toward a group effect in hit rate [*F(*1*,* <sup>32</sup>*)* = 2*.*78, *P* = 0*.*11]. This was observed on account of the increased number of void trials, for which no participant response was recorded, in the schizophrenia group as compared to the healthy group [healthy group: 1*.*33 ± 0*.*96 %; schizophrenia group: 5*.*75 ± 7*.*61 %; *T(*32*)* = 2*.*07, *P* = 0*.*05]. VAS ratings categorized on the basis of trial type and group are presented in **Figure 3**. Main-effect and interaction statistics for VAS measures are shown in **Table 3**.

#### **SN IDENTIFICATION AND CHARACTERISATION**

**Figure 4** displays GOF scores between the FIC mask and each of the 64 whole-sample components. The GOF score for the best-fit component was 2.87. This *Z*-score equates to a *P*-value of 0.004, and as such this component can be confidently declared to focus on FIC regions. A one-sample *T*-test on individual-specific component maps for the best-fit component demonstrated significant positive clusters in bilateral IFG and anterior insula and is displayed in **Figure 5**. Statistical characteristics of its gray-matter foci are presented in **Table 4(A)**. **Figure 6** displays GOF scores between the dACC mask and each of the 64 whole-sample components. The best-fit dACC component, whose whole-sample

#### **Table 2 | Modulation of hit rate and reaction time.**


*Valence, V; Salience, S; Group, G.*

**Table 3 | Modulation of visual analog scale ratings.**


*Valence, V; Salience, S; Performance, P; Group, G.*

GOF with the dACC mask was 1.38 (which equates to a *P*-value of 0.168) and had maximal loadings in medial prefrontal regions. This component is displayed in **Figure 5** and statistics relating to its gray-matter foci are presented in **Table 4(B)**.

#### **BETWEEN-GROUP DIFFERENCES IN SN COMPONENT AMPLITUDE**

Whole-brain examination of between-group amplitude differences in the best-fit FIC and dACC components conducted using two-samples *T*-tests revealed no significant clusters (using either an uncorrected or FWE-corrected cluster level of *P <* 0*.*05 and a voxel-level threshold of *P <* 0*.*001).

#### **SN ACTIVITY AT REWARD OUTCOME**

**Figure 7** and **Table 5** present beta coefficients for the bestfit FIC component at time of trial outcome. As is shown in **Table 6**, there was an overall main effect of group [*F(*1*,* <sup>32</sup>*)* = 4*.*82,

*P* = 0*.*036]. Subsequent independent samples *T*-tests revealed that beta estimates for schizophrenia patients averaged over conditions were significantly smaller than those for healthy individuals. Interestingly, a significant salience-by-performance-by-group interaction was also observed [*F(*1*,* <sup>32</sup>*)* = 4*.*280, *P* = 0*.*047]. Healthy individuals displayed a trend toward greater responses for hits compared to misses (across both valences of conditions) in the high salience trials [*T(*12*)* = 2*.*055, *P* = 0*.*061] and a non-significant effect for low salience trials [*T(*12*)* = −1*.*730, *P* = 0*.*107]; by contrast, performance did not significantly modulate FIC response for either high or low salience trials in the schizophrenia group [high salience: *T(*19*)* = 0*.*483, *P* = 0*.*634; low salience: *T(*19*)* = −0*.*483, *P* = 0*.*966]. There was also a highly significant valence-by-salience interaction on FIC activity at reward outcome [*F(*1*,* <sup>32</sup>*)* = 11*.*353, *P* = 0*.*002]. A follow-up paired-samples *T*-test of the full study sample revealed that, while there was a non-significant difference in FIC modulation between high and low salience conditions for win trials [*T(*32*)* = 0*.*944, *P* = 0*.*352], high salience loss trials evoked greater responses than corresponding low salience trials [*T(*32*)* = 3*.*482, *P* = 0*.*001]. No other main effects or between-factor interactions were significant for this component. For the best-fit ACC component task modulation at reward outcome is shown in **Figure 8**, and beta estimates summarized in **Table 5**. No main effects or between-factor interactions were significant at conventional statistical thresholds, as is shown in **Table 7**.

#### **RELATIONSHIPS WITH SYMPTOM AND MEDICATION**

No statistically significant relationship was observed between SN task modulation and psychiatric symptomatology, medication dosage or medication class.

#### **DISCUSSION**

This salience-focused fMRI study of individuals with schizophrenia and matched control subjects employed spatial ICA to identify components of brain activity with maximal spatial correspondence with two principal features of the SN, namely the FIC and

dACC; modulation of the activity of these components at trial outcome was then investigated to test the hypothesis that SN would be temporally dysregulated in schizophrenia.

The best-fit FIC group component exhibited a high GOF with the FIC mask (*Z* = 2*.*87, *P* = 0*.*004). It was characterized by maximal positive loadings in bilateral regions principally including AI and IFG, and can therefore be confidently described as exhibiting substantial FIC focus. Its hemispherically bilateral distribution is in accord with numerous fMRI ICA characterizations of the SN (for example, Seeley et al., 2007; Sridharan et al., 2008; White et al., 2010b), although it is noted that right FIC has been ascribed particular, influential roles in cognitive switching (Sridharan et al., 2008). GLM analysis of activity in this component at trial outcome produced findings of amplitude modulation suggestive of its role in salience coding.

A significant salience-by-performance-by-group interaction on FIC component activity was evident at trial outcome. In healthy individuals a trend toward greater responses for hits than misses was observed for high salience trials but less robustly for low salience trials. Since the outcome of a specific trial potently

#### **Table 4 | One-sample** *T***-test results for the best-fit salience network components.**


modulates its financial implications, and the differential consequences of the hit or miss are greater in high salience trials, it is unsurprising that the cortical system purported to encode salience exhibits modulation of activity in this manner in healthy individuals. An interesting addendum here is that FIC responses to hits exceed those to misses across high salience trials of both reward valences. Learning from a large-magnitude failure is an important aspect of adaptive behavior, and is contingent on importance being placed on the event in question; as such, it is somewhat surprising that this is not reflected in FIC activity at reward outcome.

Contrary to the healthy individuals, FIC component activity was not significantly modulated by performance as a function of salience in the individuals with schizophrenia. Non-significant performance effects were observed for both the high and low salience trial types. This demonstrates a functional impairment in FIC regulation in these individuals with potential behavioral repercussions. Efficient reinforcement learning must rely on determination of not only the potential salience of environmental events (as would be demonstrated by a main effect of salience) but also how this salience changes contextually (as would be demonstrated in this instance by a salience-by-performance interaction). That this effect is not evident in individuals with schizophrenia provides further physiological support for reinforcement learning deficits in these individuals (Evans et al., 2011; Maia and Frank, 2011).

There was also a significant salience-by-valence interaction on FIC activity at trial outcome. Across the whole sample, responses for large-loss hits exceeded those for small-loss hits, while the corresponding comparisons for win trials were insignificant. These

#### **Table 5 | Beta coefficients for salience network responses at outcome.**


*Group mean values and their standard deviation in brackets.*

*Frontoinsular cortex, FIC; Anterior cingulate cortex, ACC.*

findings provide a cortical correlate of "loss aversion," by which individuals exhibit increased sensitivity for loss compared to gain (Tversky and Kahneman, 1979). Previous behavioral evidence suggests that this phenomenon is reduced in individuals with schizophrenia (Tremeau et al., 2008), representing a failure in the integration of affective and cognitive systems. Thus, while the current data highlight a finely-tuned aspect of SN function, it appears that the FIC does not act as the physiological substrate for the loss-aversion deficits previously observed in individuals with schizophrenia. Nevertheless, these findings of enhanced FIC sensitivity to negative events are broadly concordant with previous observations that right AI is influential in processing emotionally negative stimuli, such as those evoking feelings of disgust (Phillips et al., 1997).

The responses of the FIC component at trial outcome were consistently smaller in individuals with schizophrenia as compared to healthy individuals, as was demonstrated by the main effect of group in the repeated-measures ANOVA. This finding demonstrates that activity at this time was less tightly linked to the environmental stimuli in these individuals. While this is in itself noteworthy, the importance of this finding is magnified by the concomitant observations that contrasting features of FIC activity remain unchanged in the disorder. Despite the betweengroup differences in SN task modulation, voxel-wise comparison revealed no significant between-group difference in amplitude of SN expression over the dataset. This result suggests that the SN is similarly active in schizophrenia and that this network is similarly coherent in the disorder, thus acting as an integrated system (as reported by Woodward et al., 2011), despite exhibiting attenuated task-related responses in schizophrenia. In summary,

**Table 6 | Modulation of frontoinsular cortex component activity by task.**


*Valence, V; Salience, S; Performance, P; Group, G.*

these data suggest that the SN is dysregulated in schizophrenia rather than attenuated *per se*. This work, similarly to EEG reports of increased background oscillatory activity in the face of decreased event-related activity (Winterer et al., 2004), therefore provides evidence of a generalized failure to appropriately recruit task-relevant brain structures in schizophrenia. Unconventional recruitment of SN structures has particular clinical relevance in light of the putative consequences of this aberrance in the formation and shaping of thoughts. According to the aberrant salience hypothesis of psychotic illness (Kapur, 2003; Kapur et al., 2005), salience attribution is not only diminished for events to which salience is usually attributed, but also increased for events to which salience would not normally be assigned. This paper therefore adds a useful extension to the sizeable literature reporting diminished responses during reward (and more generally during salience processing) in regions including FIC and ventral striatum (for review, see Heinz and Schlagenhauf, 2010).

Our results relating to SN activity in ACC are less clear—no main effects or between-factor interactions were significant—but nevertheless raise important methodological issues related to the study of brain networks using ICA. The best-fit ACC component exhibited a GOF of 1.38 with the dACC mask. This value is at the 17th percentile and as such would not permit rejection of the null hypothesis (that the component does not significantly fit the mask) according to conventional statistical thresholds. Nevertheless, this component's global maximal positive loadings were in ACC and its significant positive clusters were limited to ACC and IFG. From this perspective, it can be validly adjudged a reasonable ACC component, albeit one whose ACC focus was

denoting hit and miss trials, respectively. Error bars denote standard error of the mean.

**Table 7 | Modulation of anterior cingulate cortex component activity by task.**


*Valence, V; Salience, S; Performance, P; Group, G.*

more ventral than is conventional for the SN, given its extension into subgenual regions. The use of objective measures such as the GOF index (Greicius et al., 2004; Petrella et al., 2011) to identify components is preferential to visual inspection but is not without potential pitfall. Lability of distributed large-scale networks should be expected in view of variability of contemporaneous task demands and the psychological processes exercised. On these grounds, if activity in independent networks is coordinated over the course of an experiment they might reasonably be expected to be amalgamated into conglomerate spatial components. Despite the SN being initially identified by procedures such as ICA and seed-region connectivity (Seeley et al., 2007), this problem is particularly likely for networks such as the SN, which are responsible for processing stimuli at a fundamental level and hence likely to be coactive with task-specific regions in numerous tasks.

Antipsychotic medication is a potentially critical confounder of the study of reward salience systems in schizophrenia on account of its dopaminergic mode of action. However, several reports suggest that atypical antipsychotics have a normalizing effect on cerebral activity (for example, Lane et al., 2004). Confounding medication effects cannot be totally discounted in

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the present study; however, no significant relation was observed between chlorpromazine equivalent dosage or medication class and SN task modulation. While follow-up study in a drug-naïve sample is required to explicitly discount medication confounding effects, it is relevant that aberrant striatal activity during reward processing in schizophrenia has been shown to predate antipsychotic medication treatment (Schlagenhauf et al., 2009).

The application of GLMs to component time-courses lessens the multiple-comparison problem inherent in mass-univariate assessments of whole-brain activity using fMRI. However, a limitation of this procedure is that it does not permit fine-tuned regional inferences, since resultant beta coefficients relate to the component as a whole. As such, their utility depends on the justified selection of meaningful components of activity for GLM assessment. This criterion is met by the current analysis (in particular for the FIC component), given the spatial concordance of the current SN components with previous characterizations of the SN (Sridharan et al., 2008; Shirer et al., 2012).

A further limitation of this work is the employment of uneven sample sizes between the study groups. However, it is not considered that this difference significantly contributed to the findings presented. Since the healthy group sample was sufficient to detect wide-ranging within-group effects at conventional statistical thresholds, it can be reasonably claimed that the healthy group sample characteristics represent a realistic approximation of the population characteristics. Furthermore, our healthy sample was of comparable magnitude to those employed in previous fMRI investigations of abnormal reward processing in schizophrenia (for review, see Heinz and Schlagenhauf, 2010).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 26 September 2012; accepted: 18 February 2013; published online: 07 March 2013.*

*Citation: White TP, Gilleen J and Shergill SS (2013) Dysregulated but not decreased salience network activity in schizophrenia. Front. Hum. Neurosci. 7:65. doi: 10.3389/fnhum.2013.00065*

*Copyright © 2013 White, Gilleen and Shergill. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial

## *Ariana Anderson\* and Mark S. Cohen*

*Department of Psychiatry and Biobehavioral Sciences, Center for Cognitive Neuroscience, University of California Los Angeles, Los Angeles, CA, USA*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Chaogan Yan, The Nathan Kline Institute for Psychiatric Research, USA*

*Pierre Lafaye De Micheaux, Université de Montréal, Canada*

#### *\*Correspondence:*

*Ariana Anderson, Department of Psychiatry and Biobehavioral Sciences, Center for Cognitive Neuroscience, University of California Los Angeles, 760 Westwood Plaza, Suite 17-369, Los Angeles, CA 90095, USA e-mail: ariana82@ucla.edu*

Functional network connectivity (FNC) is a method of analyzing the temporal relationship of anatomical brain components, comparing the synchronicity between patient groups or conditions. We use functional-connectivity measures between independent components to classify between Schizophrenia patients and healthy controls during resting-state. Connectivity is measured using a variety of graph-theoretic connectivity measures such as graph density, average path length, and small-worldness. The Schizophrenia patients showed significantly less clustering (transitivity) among components than healthy controls (*p <* 0*.*05, corrected) with networks less likely to be connected, and also showed lower small-world connectivity than healthy controls. Using only these connectivity measures, an SVM classifier (without parameter tuning) could discriminate between Schizophrenia patients and healthy controls with 65% accuracy, compared to 51% chance. This implies that the global functional connectivity between resting-state networks is altered in Schizophrenia, with networks more likely to be disconnected and behave dissimilarly for diseased patients. We present this research finding as a tutorial using the publicly available COBRE dataset of 146 Schizophrenia patients and healthy controls, provided as part of the 1000 Functional Connectomes Project. We demonstrate preprocessing, using independent component analysis (ICA) to nominate networks, computing graph-theoretic connectivity measures, and finally using these connectivity measures to either classify between patient groups or assess between-group differences using formal hypothesis testing. All necessary code is provided for both running command-line FSL preprocessing, and for computing all statistical measures and SVM classification within R. Collectively, this work presents not just findings of diminished FNC among resting-state networks in Schizophrenia, but also a practical connectivity tutorial.

**Keywords: fMRI, classification, functional network connectivity, SVM, independent component analysis,** *R***, Schizophrenia, small-world**

## **1. INTRODUCTION**

Functional Magnetic Resonance Imaging (fMRI) is a fourdimensional medical imaging modality that captures changes in blood oxygenation over time, an indirect measure of neuronal activation. Because fMRI scans are large, they are stored in specialized formats that make their direct access and manipulation difficult. Statistical analyses are therefore limited to the software the neuroscientist is able to use; pre-made routines are available to perform general analyses such as linear models, but the techniques and consequently the hypotheses that can be evaluated by them are limited and inflexible. Analyses are dependent upon the ability to create programs that not only can access directly subsets of the data, but also can be tailored to unique statistical analysis based on *a priori* hypotheses of the underlying neurological disorders.

An increasing focus is the classification of either mental disorders or states based on the fMRI signal variations within and among brain networks. One method of accomplishing this is through measurements of functional network connectivity (FNC), which infers differences in temporal brain connectivity that may depend on a disease or mental state (Biswal et al., 1995; van de Ven et al., 2004). FNC investigates temporal connectivity differences among either anatomical brain regions or functionally defined networks. Herein, we present a tutorial to perform FNC in *R* which can be altered easily for a unique hypothesis or dataset (Tabelow et al., 2011; R Development Core Team, 2012).

The methods we discuss here closely follows those presented in Anderson et al. (2010), which describes in full the motivation for, and findings of, using brain connectivity measures to classify between Schizophrenia patients and normal controls during rest. We demonstrate this procedure on a recently released dataset, publicly available for download at http://fcon\_1000.projects.nitrc.org/indi/retro/cobre.html and studied previously in Calhoun et al. (2011), Hanlon et al. (2011), Mayer et al. (2012). This dataset, which we will refer to as the COBRE data, consists of 72 patients with Schizophrenia and 74 healthy controls, ranging in age from 18 to 65 years old. A full demographic table is provided in **Table 1**.

The code contained in this article is available through the Neuroimaging Informatics Tools and Resources Clearinghouse



(NITRC) at http://www*.*nitrc*.*org/projects/fmriclassify/. NITRC is an NIH-sponsored project to categorize, compare, rate and distribute software and data, created by and for neuroimaging researchers. It contains both stand-alone programs and code snippets such as this project. Its usefulness is quite evident given the redundancies in research, where many labs develop independently routines to perform similar analysis techniques such as functional connectivity analysis. It is also useful for determining reproducibility, as users can test another's analysis on their own data to see if similar results are reached. This is particularly appropriate in fMRI analysis, where conclusions are often reached on quite small sample sizes since data are costly and difficult to obtain. The reader is encouraged to download and modify this code snippet from the NITRC website.

We demonstrate this analysis using preprocessing in FSL, which performs brain extraction [bet (Smith, 2002)] to remove non-brain tissue, motion-correction mcflirt (Jenkinson et al., 2002) to correct for subject movement within the scan, and ICA using melodic (Smith et al., 2004) with automatic component estimation. A full FSL tutorial is available at http:// http://fsl*.*fmrib*.*ox*.*ac*.*uk/fslcourse/. We use independent component analysis (ICA) to identify networks within each patient and calculate properties of their temporal-connectivity, demonstrating this within FSL, implemented as "MELODIC", and within *R*. Using packages **vegan** (Oksanen et al., 2011) and **AnalyzeFMRI** (Bordier et al., 2009), we extract possible neurological networks and define distances among them as functional connectivity measures. This distance matrix is then converted into a graph structure, and properties of these connectivity graphs are computed using **igraph** (Csardi and Nepusz, 2006). We use this connectivity for classification with the Support Vector Machines (SVM) algorithm in the package **e1071** (Dimitriadou et al., 2010).

Because this analysis is heavily computational, we also demonstrate how to perform this same process in parallel using the package **parallel** (R Development Core Team, 2012). The ability to code this in *R* with minimal function calls, or changing of the original code, allows users to implement and test computationally intensive analyses efficiently and simply. Parallel computing is a specialized topic, and many researchers are uninterested in learning methods such as MPI to implement their analyses, as troubleshooting can often take as long as the time saved by running in parallel. Because of this, we demonstrate calling fork clusters within *R* to perform parallel analysis, without making major revisions to the code already created to run in serial. This supplementary section is listed in the *Appendix*. We additionally demonstrate in the *Appendix* using R to access fMRI data, including how to perform ICA using the package **AnalyzefMRI** (Bordier et al., 2009).

We begin with a description of our approach, and follow with an applications section where we provide and discuss the code necessary to accomplish these methods. In this tutorial we assume the reader has no specific knowledge of *R*, but does have general knowledge of basic programming techniques. An *R* tutorial is available at http://cran*.*r-project*.*org/doc/manuals/R-intro*.*html. We hard-code as little as possible to ensure minimal changes for a new users' analysis. As this analysis focuses on connectivity within subjects, spatial alignment across subjects is not necessary, although procedures such as motion correction and temporal filtering may be performed beforehand if desired. The **AnalyzeFMRI**, **vegan**, **igraph**, and **e1071** packages are used along with their dependencies, and must be pre-installed. These packages are available at http://cran*.*r-project*.*org/web/packages. The package **parallel** is a base package installed already within the latest *R* release. As the bulk of this code is constructed to classify between distance matrices, these routines can be adapted easily for a region of interest (ROI) analysis where distances are sought not between independent components, but instead between ROIs. More generally, these methods are applicable to longitudinal data analysis where the temporal correlations among units are indicative of a state or condition. Collectively, this article demonstrates code that can be adapted easily to new data for determining if functional connectivity differences exist between groups of fMRI scans, and is meant to serve as a bridge between neuroscientists interested in performing their own connectivity/classification analysis, and statisticians interested in seeing these methods applied to real-world data.

## **2. BACKGROUND**

## **2.1. OVERVIEW OF fMRI**

Function magnetic resonance imaging is a modality that measures brain activity over time. The fMRI Blood Oxygen Level Dependent (BOLD) signal is an indirect reflection of neuronal activity captured during an fMRI scan, and analysis is performed under the assumption that neuronal activity coincides with increased blood flow. The blood flow increase in response to neuronal activity is known as the hemodynamic response (Kim et al., 1999). When activation occurs within a region, oxygenated hemoglobin flows to that area to increase the local oxygen concentration. Deoxyhemoglobin has a faster MR signal decay rate (T2∗) than oxyhemoglobin (Cohen and Bookheimer, 1994), so the signal from well-oxygenated regions results in a stronger MR signal intensity than areas lacking the increased blood flow. Areas with increased neuronal activity therefore give off a greater MRI signal, which indicates potential neural activity.

The four-dimensional fMRI picture can be used to discover anatomical regions specific to certain tasks such as language processing (Bookheimer, 2002), face recognition (Gauthier et al., 1999), or even to diagnose regional impairment specific to cognitive disorders such as Alzheimers disease, traumatic brain injury (TBI), or schizophrenia (Ford et al., 2003; Anderson et al., 2010). Such studies typically analyze regional blood flow to establish areas active during a task, or to compare regional blood oxygenation levels between two groups, such as Alzheimer's patients and normal controls, to find localized variation that could be the cause of cognitive impairment.

FNC is used to test the hypotheses that synchronicity across anatomically-defined brain regions or functionally-hypothesized networks are different, because of age, disease, or the task being performed. Connectivity differences are thought to underly many disorders such as autism (Koshino et al., 2005) and schizophrebeen used to explore directed influences between neuronal populations in fMRI data (Roebroeck et al., 2005) using Granger causality, and to examine differences between schizophrenia patients compared to normal controls (Garrity et al., 2007; Jafri et al., 2008; Anderson et al., 2010; Yu et al., 2011) using crosscorrelation measures. Within Schizophrenia, disrupted smallworld properties were found compared to healthy controls among 90 cortical and subcortical regions (Liu et al., 2008). Increased regional functional connectivity in the 0.06–0.125 Hz interval were found in Schizophrenia, along with decreased strength by Bassett et al. (2008). Within the default mode network, abnormally high functional connectivity and altered temporal frequency have been found (Garrity et al., 2007; Whitfield-Gabrieli et al., 2009). Schizophrenia patients had higher correlations among seven selected resting-state networks than healthy controls (Jafri et al., 2008), and different topological measures were found between resting-state networks identified by group-ICA (Yu et al., 2011). Collectively, these works and others propose that within Schizophrenia, functional connectivity measures can be used to identify traits that are characteristic of the disease itself. nia (Liang et al., 2006). More generally, temporal connectivity has

Because connectivity depends on how networks or regions are defined, and how the graphical properties of regions may be measured (Toppi et al., 2012; Zalesky et al., 2012), it is vital for researchers to be able to tailor connectivity analysis to their own data, to allow pre-existing knowledge or certain hypothesis to be tested. For example, Sato et al. (2010), implemented functional connectivity analysis among regions of interest, while Chu et al. (2011), analyzed connectivity among individual voxels. Similarly, Yu et al. (2011) analyzed FNC among group-ICA components, while we analyzed FNC among within-subject ICA components (Anderson et al., 2010).

## **2.2. fMRI CLASSIFICATION**

The primary challenge of fMRI classification is the abundance of observations within a single scan, many of which are correlated strongly both in space and time. Although many of these voxels will be empty, they are not systematically empty across subjects as a result of differences in brain size and shape. Because many of these datapoints are redundant, dimension reduction techniques are used by creating statistical summaries of individual voxels (*t*-tests, correlation tests), isolating "regions of interests" (ROI) or neural hotspots on which discrimination could be performed, or implementing classical dimension reduction methods such as principal components analysis (PCA) to decompose the entire scan into orthogonal signal sources over time. Newer methods such as ICA (Hyvärinen and Oja, 2000) and Sparse Component Analysis (Georgiev et al., 2005) mimic the approach of PCA into decomposing the scan into a limited number of spatial networks operating over time, but alternatively impose assumptions such as statistical independence or sparsity to estimate the underlying signal sources.

Once the dimension reduction and feature extraction steps are complete, the reduced data are fed into classifiers such as SVM, random forests, and boosting algorithms. These classification techniques have been used previously to discriminate between Positron Emission Tomography (PET) scans of HIV positive and healthy individuals (Liow et al., 2000), to detect deceptive individuals within a group using fMRI (Lee et al., 2002; Fan et al., 2006), to separate drug-addicted patients from healthy controls using fMRI scans (Zhang and Samaras, 2005), and to discriminate between patients with Alzheimer's, schizophrenia, and TBI and healthy controls using fMRI scans (Ford et al., 2003).

In "leave one out" cross-validation, these classifiers often achieve around 90% accuracy, but because methods are constructed uniquely for each dataset they are difficult to validate across different patient groups, or even within the same patient group but with a new population. These studies are often performed on excessively small samples (*n* ≈ 20). The reproducibility of such findings are often unverified, leaving open the criticism that superior classification accuracy is due to mere chance or model-mining, instead of underlying functional or anatomical differences between patient groups. It can be difficult to pool data taken across laboratories, because the scan parameters, resolution, and imaging sequences would have to be nearly equivalent. Because of this, the ability to evaluate models on different datasets would increase confidence in results, since the models acting on the same patient group should produce identical results, holding the scan environment constant.

## **2.3. R FOR fMRI**

The *R* platform has important benefits for fMRI analyses because of its availability and functionality. *R* is free and open source, so licensing costs for research are not prohibitive and any researcher is able to install it easily. Because of this, sharing code to validate methods and reproduce findings is quite simple. *R* contains thousands of packages that can perform cutting-edge statistical and machine learning techniques; analyses and hypothesis are not limited by the available models. *R* allows the user direct contact with their data, with routines for fMRI that can efficiently extract single timeseries, volumes, or planes. This is of particular value because fMRI scans are encoded in specialized formats (ANALYZE or NIfTI) that are otherwise unaccessible. The ability to access directly the data combined with the high-level statistical methods available within the general *R* framework allows the user to construct his own methods unique to his hypothesis.

Finally, *R* contains packages to implement specifically fMRI analysis. There are routines pre-built into *R* for fMRI that can perform methods such as mixed effects analysis **3dMEMA** (Chen et al., 2010, 2012) to estimate the effect, which is implemented indirectly in *R* by sourcing through AFNI (http://afni*.*nimh*.* nih*.*gov/sscc/gangc/MEMA*.*html). The bread and butter of fMRI analysis, the general linear models (GLM) can be implemented in the package **fmri** (Polzehl and Tabelow, 2007), Bayesian multilevel modeling analysis in **cudaBayesreg** (Ferreira da Silva, 2011) and Granger Causality and structural equation modeling in **FIAR** (Roelstraete and Rosseel, 2011). Functional connectivity analysis also can be performed using the package **brainwaver**, where ROIs are analyzed for connectivity using wavelet analysis, and connections are trimmed with a hypothesis test (Achard et al., 2006). We refer the reader to a presentation at the use-R! 2010 conference for a description of packages and options in *R* for fMRI analyses, at http://www*.*r-project*.*org/conferences/ useR-2010/slides/Chen+Saad+Cox*.*pdf. Some packages require installing the most recent versions of gfortran and tcltk available for MacOS at http://cran*.*r-project*.*org/bin/macosx/ tools/.

## **3. METHODS**

In this section, we cover the specific methods used for FNC and fMRI classification presented in this paper. We first use the ICA dimension-reduction technique to decompose each scan into a set of spatial brain networks being modulated over time by associated timecourses. We then create functional connectivity matrices, by measuring the longitudinal correlations of the timecourses for each network. Next, each matrix is converted into a graph structure, and the connectivity properties of each graph are measured. Finally, these connectivity properties are used as features for an SVM classifier. We additionally use a t-test to evaluate whether the small-world connectivity of ICA networks is different between Schizophrenia patients and healthy controls. A flowchart depicting this process is shown in **Figure 1**.

#### **3.1. ICA FOR fMRI**

As fMRI is composed of recordings that are highly-redundant in both space and time, it is desirable to extract meaningful features prior to classification. This serves two purposes. Firstly, it lowers the noise by essentially tossing out signals that have no commonality with other signals. This is based on the assumption that noise is independent across observations; a signal seen only in a single location is more likely to be noise than a signal observed consistently throughout the brain. Secondly, reducing the scan to a manageable number of consistent signals reduces the tendency

**the first step in FNC is to define the scale of connectivity to observe.** In this case, we use whole-brain networks obtained from ICA, but this analysis also can be implemented on the region-of-interest or the voxel scale. The connectivity is defined and measured to identify differences between either groups or conditions.

of overfitting in the classification process. The classification complexity is a function of the number of dimensions (features).

Although there are many methods of extracting common signals across the brain, ICA in particular has gained popularity in fMRI. It can isolate networks corresponding to neurological activity, as well as motion artifacts, where signals that operate most strongly on the peripheral regions along the scalp are taken to be motion. ICA has been validated through bootstrapping and clustering methods, identifying components that exist across subjects and scans that correspond to functionally identifiable brain networks (McKeown et al., 2003; Anderson et al., 2011). In this implementation we run ICA within subjects, rather than implementing a group-ICA which would have identified common networks across all subjects. This is based upon the hypothesis that there are a different set of networks operating within Schizophrenia, and assuming that the same exact networks operate within both patient groups would dampen any between-group differences.

Under the hypothesis that the activity of the brain is constructed of anatomical networks acting together to produce meaningful psychobehavioral cognitive states, the aggregate activity is decomposed into subcomponents in ICA. Prior to this, space is "unrolled" where the four dimensional scan (3 dimensions of space, 1 of time) are transformed into a matrix of dimension space by time, so that a scan array of dimension *(X, Y, Z, T)* would become a matrix of dimension *(T, X*∗*Y*∗*Z)*. An fMRI scan of time length *T* and spatial dimension *S* and can be expressed as a linear combination of *M < T* components and the corresponding timeseries:

$$X\_{ts} = \sum\_{\mu=1}^{M} A\_{\mathfrak{f}\mathfrak{l}} C\_{\mathfrak{l}s}$$

where *Xts* represents the raw scan intensity at timepoint *t* ≤ *T* and spatial location *s* ≤ *S*, *At*<sup>μ</sup> is the amplitude of component μ at time *t*, and *C*μ*<sup>s</sup>* is the spatial magnitude for component μ at spatial location *s*. An example of a spatial map output by *R* is shown in **Figure 2**.

The components *c* are estimated to be statistically independent as possible by solving instead the inverse problem via the FAST-ICA algorithm. To estimate the signal sources *c* in *x* = *Ac*, the inverse problem of *y* = *w x* is solved where *w* is a row of *A*<sup>−</sup>1, or the inverse of the mixing matrix. Then *y* = *w x* ⇒ *y* = *w Ac*. Substituting *z* = *A w*, *y* = *z c*. *W* is optimized such that *y* = *w x* = *z c* is as non-Gaussian as possible, leading to even *less* Gaussian sources *c* because of the Central Limit Theorem. Maximizing the kurtosis, minimizing the entropy, and maximizing the negentropy over *w* are all methods of finding the *least* Gaussian *y* = *w x* = *z c*.

$$\text{Negentropy} = J(\text{y}) = H(\text{y}\_{\text{Gauss}}) - H(\text{y})$$

$$H(\text{y}) = -\sum\_{i} P(\text{y} = a\_i) \log[P(\text{y} = a\_i)]$$

In the continuous case this becomes

$$H(\boldsymbol{\wp}) = -\int f(\boldsymbol{\wp}) \log(f(\boldsymbol{\wp})) d\boldsymbol{\wp}$$

By default *R* and FSL use FAST-ICA. The default parameter setting in *R* is for parallel extraction, and also includes temporal normalization, 1000 maximum iterations for the algorithm using negentropy: *G(u)* = <sup>1</sup> <sup>α</sup> *log cosh(*α*u)* where α ∈ [1*,* 2] is the constant used for the negentropy approximation. An example of a ICA spatial map is shown in **Figure 2**.

We implement here spatial ICA the only option in FSL, which sought statistical independence of the spatial maps. We alternatively could have implemented temporal ICA, which would have maximized independence of the time-courses. A presentation of this using the **AnalyzeFMRI**, and a demonstration of how to implement temporal ICA within R using the **AnalyzeFMRI** is provided in (Bordier et al., 2011). We allowed the number of components to be determined within the data following (Allen et al., 2011).

#### **3.2. CREATING FUNCTIONAL CONNECTIVITY MATRICES**

A temporal interaction plot for a schizophrenia patient and a normal control is shown in **Figure 3**, showing the joint longitudinal activity by two components within each subject, *(A*μ<sup>1</sup> *, A*μ<sup>2</sup> *)*. Since graphical interpretation is subjective, a fixed measure of this joint activity is established by computing a correlation-based distance metric. The distance function is a transformation of the maximal absolute cross-correlation between two timeseries. This computation is done for each possible pair of components within a subject, thus transforming the original fMRI scan into a matrix. This is a measure of the functional connectivity between components for a given subject, but is only one of many possible metrics that can be changed by the end user within this tutorial. This is but one example where *R* allows the user to change the methods according to the hypothesis and data being evaluated.

The cross-correlation function (CCF) between these timeseries is calculated over a range of temporal lags. We subtract the maximal absolute cross-correlation from 1 to create a pseudo distance measure, *d(A*μ*<sup>i</sup> , A*μ*<sup>j</sup> )*, given by

$$d(A\_{\mu\_i}, A\_{\mu\_j}) = 1 - \max\{|CCF(A\_{\mu\_i}, A\_{\mu\_j}, b)|\}$$

where

$$\text{CCF}(A\_{\mu\_i}, A\_{\mu\_j}, l) = \frac{E[(a\_{\mu\_i, t+l} - \overline{A\_{\mu\_i}})(a\_{\mu\_j, t} - \overline{A\_{\mu\_j}})]}{\sqrt{E[(a\_{\mu\_i, t} - \overline{A\_{\mu\_i}})^2]}E[(a\_{\mu\_j, t} - \overline{A\_{\mu\_j}})^2]} \tag{1}$$

**FIGURE 3 | Temporal activity plot of two primary components within a subject, depicting the relationship between two components over time.** This phase space transition between pairs of components are measured for the functional connectivity analysis, to calculate the similarity of the components' behavior.

where *l* is the time lag separating the two timeseries *A*μ*<sup>i</sup>* and *A*μ*<sup>j</sup>* , and *A*μ*<sup>i</sup>* is the mean of the entire timeseries *A*μ*<sup>i</sup>* = *(a*μ*i,*1*, a*μ*i,*2*,..., a*μ*i,T)*. The timeseries are calculated at lags ranging from 0 to 3 points (6 seconds), as higher lags results in fewer time points to calculate the correlation and a more noisy estimate, and also lacks biological plausibility given our current understanding of neurological coupling. Within *R*, the lag parameter is specified using lag.max.

The matrices by themselves are uninterpretable, since they are merely representations of a set of connected objects. An example of this is shown in **Figure 4**. Moreover, the rows and columns of these matrices, representing unique independent components within subjects, are themselves not comparable across subjects. Our ultimate goal is to measure this connectivity; not only how closely connected they are, but also how it changes with respect to patient diagnosis. For example, do all networks interact with all other networks? Are there subgraphs that are fragmented from the original graph? Does the number of steps to travel among nodes differ? Are some graphs more densely connected than others? To answer these questions, we must convert the connectivity matrices to graph objects, so we can use *R* packages designed purposefully for graph connectivity analysis.

#### **3.3. GRAPH CREATION AND MEASUREMENT**

Each matrix represents a structure of completely connected points on a high-dimensional manifold, where each point is an independent component and the distance between two points measures the similarity of their temporal activity. Every point is linked to every other point regardless of similarity. To create graphs out of the connectivity matrices, we prune weak connections among points and then embed the simplified structure into a lowerdimensional space using the ISOMAP procedure (Tenenbaum et al., 2000). Then, we measure the graph-theoretic connectivity to summarize the connections between resting-state networks.

#### *3.3.1. Graph creation*

Conceptually, any set of points contained in a distance matrix of dimension *d* can be embedded into a space of dimension *d* − 1 without any information loss (preserving all the distances between points). Usually such a transformation assumes the space on which the points lie is linear. This, however, may not be the case. Consider if you were trying to measure the distance from Sacramento, California to Shanghai, China, using only the *(x, y,z)* grid coordinates of each city. The linear distance between the cities, while calculable, would assume that the correct path from Sacramento to Shanghai went through the core of the earth. Instead, a more reasonable way to measure the distance would be to travel along the flight-paths, from Sacramento to Los Angeles, Los Angeles to Tokyo, and finally from Tokyo to Shanghai. This method of measuring distance is known as the geodesic distance, or path-distance among points. It assumes that travel among distant points usually requires routing through intermediate nodes, as shown in **Figure 5**. It is this concept we will now use to sever weak connections and create graphs out of the matrices.

We transform each matrix into a graph structure using an initial geodesic distance calculation implemented in the function isomap in the library **vegan**. Weak ties among points are then severed; points can be connected if they are within a certain distance, *-*, of each other (|*x* − *y*| ≤ *-*), or they can be connected if they are within a set of *k*-nearest neighbors. The distances are recomputed after pruning, where the distance between connected points is the same as it was originally, but the new distance between unconnected points is computed as the shortest path through intermediary connecting nodes. Combined with multidimensional scaling to obtain a coordinate system for embedding, this procedure is called ISOMAP (Tenenbaum et al., 2000). An example of such a graph created by a geodesic distance transformation and a multidimensional scaling embedding is shown in **Figure 6**.

The two definitions of connectivity (nearest-neighbor vs. *-* distance) can lead to different results; establishing connectivity by *-*-distance, also called edge density (sparsity), may lead to the graph becoming fragmented, with some portions of the graph having no connections, direct or indirect, to other subgraphs. This would be caused by some point(s) being too distant to others to maintain a connection with the main graph. This is an instance where the *a priori* knowledge about the disease may inform the parameter choices of the methods. In diseases such as Schizophrenia or autism, a hypothesis of disconnectivity may be tested directly by computing, for example, the fragmentation

**FIGURE 5 | Geodesic distance calculation.** The distance between A and C is calculated as the manifold path distance from A to B to C, instead of the direct path from A to C. This eliminates the assumption that the points occupy a linear space when using a Euclidean distance.

**by converting the distance matrices for each subject into a structure where each node represents a component, and the distance between nodes represents the connectivity or similarity of their behaviors.** Nodes close together demonstrate a higher functional connectivity measure. This map is obtained by recalculating the connectivity matrices using geodesic distances, and then embedding the points in a two dimensional space for plotting. Dim 1 and Dim 2 represent the weightings on the two primary dimensions, similar to multi-dimensional scaling.

rate of the patients versus controls. By allowing the user to choose these parameters within *R*, specific theories of neuropathology may be tested.

We threshold points as being connected using a modified nearest neighbor method. We select the k-nearest neighbors of all nodes to be connected by defining k as 10% of total components for that subject, or 2, whichever is greater. This enforces the graph be completely connected, unlike the edge density method. We select this parameter choice because we are using weighted graphs; edge density methods typically binarize the adjacency matrix by assigning weights above a given threshold a unit value, and a zero value to all below such as in Rubinov and Sporns (2010). Since we are using weighted graphs, we allow sufficiently "close" points to retain their given weights, and prune all other points which are not sufficiently close. This parameter could be investigated further, but because we are using these metrics for performing classification then optimizing the adjacency pruning method would lead to biased estimates of the accuracy.

## *3.3.2. Graph measurement*

At this point, each brain scan has been transformed into a graphical structure, where each node represents a brain network and the connectivity between nodes represents the similarity in the activity of these networks. Each graph can then be summarized by its connectivity properties. There are many such measurements available within *R* within the package **igraph**. A tutorial by Gabor Csardi on Network Analysis with the package **igraph** is at http://igraph*.*sourceforge*.*net/igraphbook/. A description of network measures of brain connectivity is available at Rubinov and Sporns (2010), which describes in detail the graph-theoretic measures discussed only briefly here, and additionally describes other connectivity measures such as modularity. An additional connectivity measure which we used in a previous study to discriminate between Schizophrenia patients and healthy controls (Anderson et al., 2010) is the "eigenvector centrality," which can be computed here using the command eigen(d)\$values.

Creating graphs out of each matrix using a non-linear distance metric such as the geodesic distance not only allows for a more efficient low-dimensional projection of the matrix, but also encourages the graph to be connected more efficiently by trimming poor connections while maintaining stronger ones. This fragmentation allows us to determine how many strong connections are within the subject, how many subnetworks (subgraphs) exist, what the sizes of these subnetworks are, and how efficiently the points are connected overall. These properties, all interrelated, give quantitative measurements of the connectivity that can be used to fingerprint the networking differences associated with different disorders. These individual metrics can be compared directly between groups if multiple comparisons are adjusted for.

Some of these available measures within **igraph** are:


We use two of these measures to compute the *small-world* property generated by the Erdos-Renyi game ( ˝ Erdos and Rényi, ˝ 1961). Alternative methods of computing this are presented by Rubinov and Sporns (2010). The small-world measure σ is computed as

$$
\sigma = \frac{\lambda}{\lambda}
$$

where γ is the ratio of the clustering coefficient of the real network to the mean of the clustering coefficient of *n* random networks with an equivalent number of edges and weights as the real (data-derived) network but randomly rewired. *λ* is the similar to γ but uses characteristic path length. The variable *n* is usually somewhere between 500 and 5000. Typically, biological networks have:


Any network with σ *>>* 1 is considered to be "small world" (Humphries and Gurney, 2008).

#### **3.4. CLASSIFICATION USING SVM**

We have transformed each subject's fMRI scan into a graph, where the nodes of the graph represent functional networks and the distances between nodes represents the similarity of the activity of the nodes. We have measured the connectivity of these graphs, or the FNC. We now wish to use these connectivity features for classification. *R* has an immense number of libraries available for classification. Packages are continually being added that implement new machine learning algorithms, and packages for specific algorithms can be found at http://www*.*rseek*.*org. In this paper we use the basic SVM algorithm, included in the package **e1071**.

The SVM algorithm attempts to find a hyperplane that best separates different classes, using only the points contained in the margin (or region of overlap.) For a set of points *(xi, yi)* where *(xi)* ∈ *Rn* is the set of graph measurements for the graph *Gi* corresponding to subject *i*, a member of class *yi* ∈ *(*−1*,* 1*)* (patient or control), SVM will learn the hyperplane which best divides the classes *(*−1*,* 1*)*. If a hyperplane is modeled by *w* · *xi* − *b* where *w* are the vectors normal to the hyperplanes, the parallel hyperplanes separating the observations can be defined by *w* · *xi* − *b* ≥ 1 for *yi* = 1 and *w* · *xi* − *b* ≤ −1 for *yi* = −1. The optimization problem becomes to maximize the distance between planes, <sup>2</sup> *<sup>w</sup>* , such that *yi(w* · *xi* − *b)* ≥ 1

Using the graph properties (path length, clique number, etc...) as features, we can then perform classification between schizophrenia patients and healthy controls.

### **4. APPLICATIONS**

#### **4.1. PREPROCESSING AND COMPONENT EXTRACTION IN FSL**

Our first step was to perform motion correction and skullstripping on the fMRI data, and to run ICA within each scan to extract the networks of interest. We specifically use automatic component estimation in FSL because our previous research has suggested differences in the number of independent components for Schizophrenia patients and healthy controls. An example of an ICA map produced by FSL is shown in **Figure 7**.

Assuming the COBRE data has been downloaded and installed, then we can run FSL from the command line to process all scans, which includes motion correction, skull-stripping, smoothing with a 6 mm filter, high-filtering at 100 Hz, and finally running Melodic with automatic component estimation within each subject. This script is tailored to the COBRE data, and requires that the variable STUDY\_DIR be changed for the users' specific path. Following this script the *melodic\_mix* files containing the patients' ICA timecourses will all be located in the folder ./COBRE/COBRE\_MELODIC. To create a similar script for a new dataset, one can simply run Melodic from the GUI, and copy the command-line input from the *log* file.


```
0040095 0040100 0040105 0040110 0040115 0040120 0040125 0040130 0040135 0040140
0040145 0040001 0040006 0040011 0040016 0040021 0040026 0040031 0040036 0040041
0040046 0040051 0040056 0040061 0040066 0040071 0040076 0040081 0040086 0040091
0040096 0040101 0040106 0040111 0040116 0040121 0040126 0040131 0040136 0040141
0040146 0040002 0040007 0040012 0040017 0040022 0040027 0040032 0040037 0040042
0040047 0040052 0040057 0040062 0040067 0040072 0040077 0040082 0040087 0040092
0040097 0040102 0040107 0040112 0040117 0040122 0040127 0040132 0040137 0040142
0040147 0040003 0040008 0040013 0040018 0040023 0040028 0040033 0040038 0040043
0040048 0040053 0040058 0040063 0040068 0040073 0040078 0040083 0040088 0040093
0040098 0040103 0040108 0040113 0040118 0040123 0040128 0040133 0040138 0040143
0040004 0040009 0040014 0040019 0040024 0040029 0040034 0040039 0040044 0040049
0040054 0040059 0040064 0040069 0040074 0040079 0040084 0040089 0040094 0040099
0040104 0040109 0040114 0040119 0040124 0040129 0040134 0040139 0040144";
umask 0002;
######################
# PROCESSING COMMANDS #
######################
# Change to STUDY_DIR
cd $STUDY_DIR;
mkdir COBRE_MELODIC
# Loop through subjects
for i in $subjects; do
       if [ ! -f "$STUDY_DIR/COBRE_MELODIC/${i}_melodic_mix_new" ]; then
       cd $STUDY_DIR/${i}/session_1/rest_1
       rm -r *.ica*
       rm rest_mcf*
           rm prefiltered*
           rm filtered*
           mcflirt -in rest.nii.gz -out prefiltered_func_data_mcf -mats -rmsrel -rmsabs
           fslmaths prefiltered_func_data_mcf -Tmean mean_func
           bet2 mean_func mask -f 0.3 -n -m; immv mask_mask mask
           fslmaths prefiltered_func_data_mcf -mas mask prefiltered_func_data_bet
           fslstats prefiltered_func_data_bet -p 2 -p 98
           fslmaths prefiltered_func_data_bet -thr 100.8095459 -Tmin -bin mask -odt char
           fslstats prefiltered_func_data_mcf -k mask -p 50
           fslmaths mask -dilF mask
           fslmaths prefiltered_func_data_mcf -mas mask prefiltered_func_data_thresh
           fslmaths prefiltered_func_data_thresh -Tmean mean_func
           susan prefiltered_func_data_thresh 614.340225 2.12314225053 3 1 1
                       mean_func 614.340225 prefiltered_func_data_smooth
           fslmaths prefiltered_func_data_smooth -mas mask prefiltered_func_data_smooth
           fslmaths prefiltered_func_data_smooth -mul 12.2082189881 prefiltered_func_
                       data_intnorm
           fslmaths prefiltered_func_data_intnorm -bptf 25.0 -1 prefiltered_func_
                       data_tempfilt
           fslmaths prefiltered_func_data_tempfilt filtered_func_data
           fslmaths filtered_func_data -Tmean mean_func
           melodic -i filtered_func_data --nobet --bgthreshold=3 --tr=2.0000000000 -d 0
                       --mmthresh=0.5
           cp filtered_func_data.ica/melodic_mix
                       $STUDY_DIR/COBRE_MELODIC/${i}_melodic_mix_new
fi
done #END "Loop through subjects..."
echo "Processing complete.";
```
### **4.2. GRAPH CREATION AND MEASUREMENT IN R**

## *4.2.1. Computing functional network connectivity using lagged correlations*

FNC requires breaking down the original temporal scans into a series of units modulated by time-series, where the correlations of the timeseries determines their similarity. We begin by calling the necessary libraries for this analysis:

```
> library(igraph)
```

For the COBRE data we can create the list of filenames using:

```
> setwd("/path/to/my/directory")
> filenames <-
             dir (pattern="melodic_mix_new")
```
Otherwise, we can read in the filenames from a text document using the scan() command.

With FSL the number of ICA components was determined uniquely for each subject. We first determine the number of components within each file to store within the vector, **s**.

```
> num_subjects <- length(filenames)
> s <- c(rep(0,num_subjects))
> for (i in 1:num_subjects)
{ s[i] <- dim((read.table(as.character
                 (filenames[i]))))[2] }
```
We next read in each melodic\_mix file and use these to create a distance matrix. We define the distance matrix as the maximal normed cross-correlation for a lag distance of 3. The distance between the timeseries for each component is calculated and stored in data\_array\_distance. The mapply function is used to apply the distance function to all elements in the upper-triangular part of the distance matrix, instead of using a nested for loop to calculate each item individually.

the temporal connectivity among components within each subject. Every *melodic\_mix* file has been converted into a functional connectivity distance matrix.

## *4.2.2. Graph creation and analysis*

Next, we transform each matrix into a graph structure and measure the connectivity properties of each graphs. We first use the ISOMAP embedding algorithm to compute the distances among elements using the geodesic framework, and prune weak connections with package **vegan**. We then create a graph structure whose connectivity can be measured using functions in **igraph**. Because the data type output in **vegan** is different than the type needed for **igraph**, we create an internal conversion function named makegraph. This graphical structure uses weighted edges in a dissimilarity matrix, where '0' indicates that two points are not connected. Because of this, we use the inverse of the distance to define the weights between two vertices when the ISOMAP algorithm has computed they are connected.

```
> makegraph <- function(my_iso)
{ ##dim is dimension of matrix
        my_dist <- as.matrix(dist(my_iso
                               $points[]))
        k <- dim(my_dist)[1]
        my_net <- matrix(0, nrow = k,
                              ncol = k)
        which.rows <- my_iso$net[,1]
        which.cols <- my_iso$net[,2]
        for(j in 1:length(which.rows))
        { my_net[which.rows[j],
                 which.cols[j]]
                 <- 1/my_dist[which.rows[j],
                 which.cols[j]]
               my_net[which.cols[j],
                 which.rows[j]]
                 <- 1/my_dist[which.cols[j],
                 which.rows[j]]
                        }
        my_net }
```

```
> data_array_distance <- array(NA, c(num_subjects, max(s), max(s)))
> for (i in 1:num_subjects)
{ ##Read in ICA results
       temp <- as.matrix(read.table(as.character(filenames[i])))
       for(j in 1:s[i])
       { for(k in j:s[i])
                { data_array_distance[i,k,j] <- 1-max(abs(ccf(temp[,j], temp[,k],
                                        plot = FALSE, lag.max = 3)$acf))
                        data_array_distance[i,j,k] <- data_array_distance[i,k,j] }}
      diag(data_array_distance[i,,]) <- 0 }
```
At this point the fMRI scans of each subject have been converted: first by decomposing them into independent components, and then creating a functional connectivity matrix measuring

We next analyze the properties of these graphs. We create a function to calculate the coefficients γ and *λ* of a random graph. This is used to compute the *small worldness* of the actual graph proposed by the data. We call this function smallworld; it takes in 2 parameters: *n* = the number of vertices, and *m* = the number of edges. The value γ is a transitivity measure, of the probability that adjacent vertices are connected. This is sometimes called the *clustering coefficient*. This function is called later to compute the values γ and *λ* later for the randomly reconnected graph, and is averaged across 5000 random graphs. These values are used to form the ratio to calculate σ, the small-worldness measure.

```
> smallworld <- function(n,m)
{ smallworld <- matrix(nrow=5000,ncol=2)
    for(k in 1:5000)
    { g <- erdos.renyi.game
                         (n,m, type="gnm")
        smallworld[k,1] <- transitivity(g)
        smallworld[k,2] <- average.path.
                              length(g) }
    colMeans(smallworld) }
```
We next run our geodesic-distance pruning procedure (ISOMAP) within **vegan**, convert the data structure using our function makegraph, and then measure the connectivity using **igraph**. We compute separately the small-world measure, σ, which is a vector output, by the routine called my\_small\_worldness. We threshold points as being connected using the k-nearest neighbors method.

We began with functional connectivity matrices, turned each matrix into a graph, and measured the connectivity of each graph. We used a total of 13 connectivity measures, include the smallworld calculations. The feature vectors collectively form a feature matrix that will be used for classification in the following section.

#### **4.3. SVM CLASSIFICATION IN R**

In this section we demonstrate SVM Classification using the package **e1071** that contains an interface to the libsvm *C++* package by Chih-Chung Chang and Chih-Jen Lin. The *R* vignette (http://cran*.*r-project*.*org/web/packages/e1071/ vignettes/svmdoc*.*pdf) details the functionality of this package, which includes many other classification routines besides SVM. The SVM method within this package has an optional benefit of cross-validation, which simplifies coding dramatically by implementing the training and testing steps within a single function call. In the following code we demonstrate classification of our feature vector using 10-fold cross-validation, but this is an adjustable parameter. There are many options within the svm() method that can be specified such as kernel choices, but we use the default parameters here ("radial basis function") for the sake of conciseness and clarity.

We create a vector my\_cat with the response variables, in the case the patient diagnosis of each scan. This can alternatively be read in using the function read.table(). Because within the COBRE data two patients were disenrolled, we exclude those

```
> my_small_worldness <- matrix(NA, nrow = num_subjects, ncol = 1)
> my_feature_matrix <- matrix(NA, nrow = num_subjects, ncol = 12)
> randomsmallstore <- matrix(NA, nrow = num_subjects, ncol = 2)
> for(i in 1:num_subjects)
{ d <- matrix(data_array_distance[i,1:s[i],1:s[i]], nrow = s[i])
    my_iso <- isomap(d[1:s[i],1:s[i]],axes=3, k=max(floor(s[i]/10),2), ndim = 15,
        fragmentedOK=TRUE)
    my_net <- makegraph(my_iso)
    d2 <- graph.adjacency(my_net )
    transitivity(d2)
    n=vcount(d2)
    m = ecount(d2)
    randomsmall <- smallworld(n,m)
    sigma <- (transitivity(d2)/randomsmall[1])/(average.path.length(d2)/randomsmall[2])
    randomsmallstore[i,] <- c(randomsmall)
    my_small_worldness[i,] <- sigma
    my_net <- makegraph(my_iso)
    d2 <- graph.adjacency(my_net, weighted = TRUE )
    my_feature_matrix[i,] <-c(average.path.length(d2),clique.number(d2),graph.density(d2),
        edge.connectivity(d2),median(closeness(d2)),median(graph.coreness(d2)),
        max(degree(d2)),median(degree(d2)),min(degree(d2)),vcount(d2),ecount(d2),
        transitivity(d2)) }
> my_feature_matrix <- cbind(my_feature_matrix,my_small_worldness)
```
Finally, we label the columns.

```
> colnames(my_feature_matrix) <- c("Average Path Length", "Clique Number","Graph Density",
   "Edge Connectivity", "Median Closeness", "Median Graph Coreness","Max Degree",
   "Median Degree", "Min Degree", "Vertex Count", "Edge Count","Transitivity",
   "Small Worldness")
```
patients from this analysis. As R read in the list of files alphabetically and the COBRE demographic spreadsheet has patients entered in order of age, we reorganize the file formats to ensure that our patient labels read in from the spreadsheet match up with the data matrix already created.

```
> cobre <- read.csv
           ("COBRE_phenotypic_data.csv")
> cobre <- cobre[order(cobre[,1]),]
> my_cat <- cobre[,5]
> my_data_matrix <- my_feature_matrix
                   [my_cat!="Disenrolled",]
> my_cat <- my_cat[my_cat!="Disenrolled"]
> my_data_matrix <- cbind
                (my_cat,my_data_matrix)
```
Finally, the library is loaded and the model is trained and tested using 10-fold cross-validation.

```
> my_svm <- svm(as.factor(my_cat)~.,
  data = as.data.frame(my_data_matrix),
  cross=10)
> pred <- fitted(my_svm)
```
The structure my\_svm contains many details of the model. We can see the average cross-validation accuracy within each *kth*-fold using my\_svm\$tot.accuracy.

### **4.4. HYPOTHESIS TESTING**

Alternatively, we can test for between-group differences using formal hypothesis testing. For example, if we wished to test the individual metrics between patients and controls, we could do so using:

```
> for(i in 2:14)
{ print(t.test(na.omit(my_data_matrix
                    [my_cat=="Patient",i]),
        na.omit(my_data_matrix[my_cat=
                      ="Control",i]))) }
```
All the computed graph-connectivity measures are correlated; for example, a graph with a low *median closeness* measure would imply that there is a short distance between two vertices, thus increasing the *transitivity*. Because of this, to compare the 13 measures we would have to adjust for multiple comparisons. Using a Bonferroni correction, only *p*-values below 0*.*05*/*13 = 0*.*0038 would be considered significant.

#### **5. RESULTS AND DISCUSSION**

We briefly present here the results of this analysis.

Patients had a significantly lower clustering coefficient than healthy controls (*p <* 0*.*05, corrected). Lower clustering implies networks are less likely to be connected to each other in Schizophrenia, indicating that the networks are themselves less synchronized and more independent of each other. Patients had lower small-world measures of connectivity than healthy controls, although both groups exhibited small-world connectivity among independent components. This difference was not statistically significant when using unweighted graphs, but was statistically significant when using weighted graphs.

Using just the scripts provided here, our 10-fold crossvalidation accuracy is 65%, compared to a chance accuracy of 50.7%. There are quite a number of things we could do to improve this accuracy, which we omitted intentionally here because they are outside the scope of this tutorial. We performed no quality-control on this data to exclude scans with excessive motion or scanner artifacts. We also took no measures to identify and exclude ICA networks that were related to motion, scanner noise, or physiological artifacts. We did not use any of the demographic information (patient gender, age, etc...) which would likely have improved accuracy, both by controlling for functional brain changes and also by controlling for sampling variation. For example, in this sample males were more likely to be Schizophrenia patients than females, so knowing this information would have permitted classification based upon this information, which is parallel to the actual functional connectivity analysis. Finally, we implemented only the basic SVM algorithm without any parameter tuning, and similarly did not optimize the definition of "connectivity" among points. Connectivity definitions have been shown previously to affect the final results, with different thresholds for connectivity having significant effects on the final graph-theoretic measurements (Toppi et al., 2012). Given the simplicity of our methods, it is perhaps somewhat remarkable that we were able to achieve the classification accuracy realized here and significant small-world differences between patients and controls.

## **6. CONCLUSION**

Collectively, we have provided methods to determine whether functional connectivity differences exist between groups, and to demonstrate that the resting-state functional connectivity differences in schizophrenia can be useful for automated patient diagnosis. Functional connectivity measures can be used to discriminate between patients and controls, and schizophrenia patients show lowered clustering of networks than healthy controls, indicating that networks within Schizophrenia are more disconnected.

The analysis outlined here is intended to be adjusted and altered by the end user, even those who aren't regular users of *R*. The user has flexibility in altering parameters such as distance metrics, classification machines, and feature selection choices. For example, another method of implementing functional connectivity is through Granger causality among ROIs such as in Sato et al. (2010), whereas this presentation implements functional connectivity through correlations among functional networks determined by ICA. Other distance metrics could have been used, which would be optimal given the recent finding that using correlation metrics to compute distance automatically leads to non-random graphical structures (Zalesky et al., 2012). We performed SVM classification in *R*, a "black-box" model which ironically is remarkably simple to implement with a single function call to both train and test the model using cross-validation. Because *R* is an established package in the statistics research community, many newer machine learning procedures can easily be implemented to compare with more established classification machines.

This analysis and tutorial is not without limitations; primarily, we took no steps to identify and discard artifacts in the ICA data, which almost certainly would have increased the classification "accuracy" we obtained. This omission was intentional given the intentions of this tutorial; manually identifying artifacts within ICA is outside the scope of this manuscript, and other tutorials to perform to identify fMRI artifacts and clean data further are available elsewhere. We wish here to illustrate how functional connectivity can be measured in a graphtheoretic approach, and to provide a working framework for other researchers to alter and improve. Moreover, there are scripts available at http://www*.*nitrc*.*org/plugins/mwiki/index*.*php/fcon1000: ScriptUse to process this data and compute a variety of connectivity measures outside of the ICA-based measures presented here. These could be easily integrated with the methods outlined here to measure the connectivity properties once the connectivity matrices are established.

## **REFERENCES**


brain using echo-planar MRI. *Magn. Resona. Med.* 34, 537–541. doi: 10.1002/mrm.1910340409


Although this analysis was created for analysis of fMRI data, more generally it applies to problems where the relationship among signal sources may determine the category to which an object belongs. The joint behavior of the signal sources (independent components) was observed as a graph object, where the distances between the sources represented the similarity of their behavior. Although second order measures were used to assess the functional connectivity (correlations), it is possible that as much discriminatory power exists using higher-order measurements that take into account the cohesiveness of triplets of components, or even more. Functional connectivity is one technique, of many, that should be assessed from multiple angles.

## **ACKNOWLEDGMENTS**

We thank Dr. Xavier Quintana for his guidance during the original analysis, Dr. Ryan Rosario for an excellent tutorial on parallel analysis in *R* and Dr. Jesse Brown for helpful feedback, and NIH grant number R33DA026109 to Mark S. Cohen and the Burroughs Wellcome Fund Collaborative Research Travel Award to Ariana Anderson.

*Neuroimage* 60, 747–765. doi: 10.1016/j.neuroimage.2011.12.060


HIV positive and healthy individuals. *J. Nuclear Med.* 41, 612–621. doi: 10.1093/brain/awn018


between regions of interest: an approach based on cluster Granger causality for fMRI data analysis. *Neuroimage* 52, 1444–1455. doi: 10.1016/j.neuroimage.2010.05.022


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 28 March 2013; accepted: 13 August 2013; published online: 02 September 2013.*

*Citation: Anderson A and Cohen MS (2013) Decreased small-world functional network connectivity and clustering across resting state networks in schizophrenia: an fMRI classification tutorial. Front. Hum. Neurosci. 7:520. doi: 10.3389/fnhum.2013.00520*

*This article was submitted to the journal Frontiers in Human Neuroscience. Copyright © 2013 Anderson and*

*Cohen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## **APPENDIX**

## **R FUNCTIONS AND FEATURES TUTORIAL**

We begin by calling the library **AnalyzeFMRI** which must be pre-installed. When installing any package within the GUI, the option of "install dependencies" should be selected to install packages called by the main package. Once this package is installed, it is called, and the proper directory is navigated to. The current working directory can be obtained using getwd(). This directory needs to contain a list of scan names, filenames.txt, and the set of scans. We use here a scan produced by the FSL script supplied on the data available for download from NITRC.

```
> library(AnalyzeFMRI)
> rm(list = ls())
> setwd("/path/to/my/directory")
```
A scan can be read directly into *R*, and consequently held in memory, using

> myimage <- f.read.nifti.volume("rest\_mcf\_brain.nii")

We can get preliminary statistics using

```
> summary(myimage)
```


We can access the header information using

```
> f.nifti.file.summary("rest_mcf_brain.nii")
```

```
File name: rest_mcf_brain.nii
  Data Dimension: 4-D
     X dimension: 64
     Y dimension: 64
     Z dimension: 33
  Time dimension: 150 time points
Voxel dimensions: 3.75 x 3.75 x 4.55000019073486
       Data type: (32 bits per voxel)
```
We can alternatively verify the dimensions of the scan using the command dim, since the image is held in memory.

> dim(myimage) [1] 64 64 33 150

This image is stored as an array, which we can verify using

> is.array(myimage) [1] TRUE

Functions such as f.read.nifti.volume, f.read.nifti.ts, and f.read.nifti.slice.at.all.timepoints can allow direct access to specific pieces of data. For example, if we wanted to access a single timeseries from the 4D scan at voxel location (30,30,10) *without* holding the entire scan in memory, we can do so by:

> f.read.nifti.ts("rest\_mcf\_brain.nii",30,30,10)

where the input parameters are the filename and the (x,y,z) coordinates of the voxel desired. We can also access this from the scan held in memory using myimage[30,30,10,] which is noticeably faster.

We next wish to perform ICA within *R*, but this is available only for Analyze format scans. We write out our scan as Analyze format with:

```
> f.write.analyze(myimage,file="rest_mcf_brain")
```
If we wish to see all the parameter options available for ICA, we can access this using

> help(f.ica.fmri)}

We next run ICA on the example fMRI scan.

```
> g <- f.ica.fmri("rest_mcf_brain.img", n.comp=20, alg.type = "deflation")
```
This performs ICA on the scan called "rest\_mcf\_brain.img", extracting 20 components and storing the results in the object g. It is necessary to state the number of components to be extracted with the parameter ncomp. The number of components is somewhat arbitrary; the ICA extraction is performed here using the deflation approach where components are individually estimated and then subtracted out, so limiting the number of components theoretically should not change the structure of the components that are extracted. We set the default value, (num\_components), to 20 according to (**?**). Use help(f.ica.fmri) to see the full list of options within the function

There is also the option of using a GUI to run this analysis, using the command f.ica.fmri.gui(). The object g contains several attributes:

```
> attributes(g)
$names
[1] "A" "S" "file" "mask"
```
The timeseries for the components are contained in g\$A:

```
> dim(g$A)
[1] 150 20
```
The spatial maps are contained in the array g\$S, the filename in g\$file, and the mask (if used) in g\$mask. We can view a single component as shown in **Figure 2**, in this case the second of the set, by using

```
> f.plot.ica.fmri(g,2,cols=rainbow(100))
```
This image isn't thresholded, but can be roughly thresholded by adjusting the color options within rainbow(), by changing the number of colors to display to 3 and adjusting the saturation.

> f.plot.ica.fmri(g,2,cols=rainbow(3,alpha=.8))

We can also manually threshold using the spatial map g\$S[, , ,2]. Here, we threshold the second component into the 10-th and 90-th percentile:

```
> g_thresholded <- g
> g_thresholded$S[,,,2][g$S[,,,2]>quantile(g$S[,,,2],probs=c(.1,.9))[2]] <- 3
> g_thresholded$S[,,,2][g$S[,,,2]<quantile(g$S[,,,2],probs=c(.1,.9))[2]] <- 2
> g_thresholded$S[,,,2][g$S[,,,2]<quantile(g$S[,,,2],probs=c(.1,.9))[1]] <- 1
```
The image can be written out using:

> f.write.analyze(g\_thresholded\$S[,,,2],file="MyThresholdedImage")

creating files called "MyThresholdedImage.img" and "MyThresholdedImage.hdr" in the working directory.

This concludes our example of the package **AnalyzefMRI**. This is not an exhaustive list of the functions available in this package, but a brief tutorial showing direct fMRI data access in R. For a full list of functions within this package, please see the manual at http:// cran*.*r-project*.*org/web/packages/AnalyzeFMRI/AnalyzeFMRI*.*pdf.

#### **ICA FOR fMRI IN R USING PREPROCESSED DATA**

R can also perform ICA on Analyze format scans, so we demonstrate doing this in a loop. ICA within R uses the FAST-ICA algorithm, which is similar to FSL. This implementation will not work directly on the COBRE data since the function f.ica.fmri requires Analyze format data and the COBRE data is NIfTI, although the FSL command fslchfiletype will convert between the two formats. The filenames ("myfilename1.img") are written on a text file called filenames.txt, where each line contains a separate filename. The lines that will need to be changed for the user are as follows:

```
> setwd("/path/to/my/directory")
> filenames <- read.table("filenames.txt")
> filenames <- filenames[i,1]
We can alternatively create the list of filenames using:
> filenames <- dir(pattern="melodic_mix")
```
We then create a loop to read in our files, perform ICA on each scan using the function f.ica.fmri() within the library **AnalyzeFMRI**, extract the timeseries associated with the components, and create a distance matrix for each component.

```
> library(AnalyzeFMRI)
> num_subjects <- dim(filenames)[1]
> num_components <- 30
> data_array_distance <- array(NA, c(num_subjects, num_components, num_components))
> my_distance <- function(x,y)
{ 1-max(abs(ccf(x, y, plot = FALSE, lag.max = 3)$acf)) }
> for (i in 1:num_subjects)
{ #Perform ICA on each file
    temp <- f.ica.fmri(as.character(filenames[i,1]), n.comp = num_components,
                                                        alg.type="deflation")
    for(j in 1:num_components)
    { j.vals <- rep(j,(num_components-j+1))
        k.vals <- (j:num_components)
        data_array_distance[i,j,j:num_components] <- c(mapply(x=j.vals,
            y=k.vals, function(x,y) my_distance(temp$A[,x],temp$A[,y])))
        data_array_distance[i,j:num_components,j]
                                  <- data_array_distance[i,j,j:num_components] }
    diag(data_array_distance[i,,]) <- 0 }
```
This performs ICA on this *i th* scan listed in filenames.txt, and stores the results into an object called temp. This object has several attributes, one of which is the timeseries associated with each component called temp\$A.

#### **PARALLEL ANALYSIS IN R**

We now demonstrate parallel computing by using the base package **parallel** in R 2.1.4 as presented by http://www*.*bytemining*.*com/ files/talks/larug/hpc2012/HPCinRrev2012*.*pdf which provides an extensive explanation of parallel data analysis using *R*. The package **cudaBayesreg** also provides parallel analysis of fMRI data using multi-level Bayesian modeling.

The package **parallel** wraps together packages **multicore** and **snow**. We assume the computing is being done on the local machine here with multiple cores, but these methods can easily be extended to run on a cluster. The number of available cores can be detected using Windows will report the number of logical CPUs, which may exceed the number of physical cores.

```
> library(parallel)
> mc <- detectCores()
> mc
[1] 2
```
To perform the parallel analysis, we create a new function func\_network\_connectivity which takes as an argument the file number, and performs within each scan the FNC analysis.

```
> data_array_distance <- array(NA, c(num_subjects, num_components, num_components))
> num_components <- 20
> func_network_connectivity <- function(i){
    temp <- f.ica.fmri(as.character(filenames[i,1]), n.comp = num_components)
    for(j in 1:num_components)
    { j.vals <- rep(j,(num_components-j+1))
        k.vals <- (j:num_components)
        data_array_distance[i,j,j:num_components] <-
             c(mapply(x=j.vals,y=k.vals, function(x,y)
             1-max(abs(ccf(x, y, plot = FALSE, lag.max = 3)$acf)))
        data_array_distance[i,k.vals,j.vals] <- 1/data_array_distance[i,j,
             j:num_components] }
    diag(data_array_distance[i,,]) <- 0
    d <- (data_array_distance[i,,])
    my_iso <- isomap(d,axes=1, ndim = 10,epsilon=median(d),
          fragmentedOK=TRUE)
    my_net <- makegraph(my_iso)
    d2 <- graph.adjacency(my_net, weighted = TRUE )
    c(as.character(filenames[i,1]),average.path.length(d2),clique.number(d2),
         graph.density(d2),edge.connectivity(d2),median(closeness(d2)),
         median(graph.coreness(d2)),max(degree(d2)),median(degree(d2)),
         min(degree(d2)),vcount(d2),ecount(d2),transitivity(d2)) }
```
The new func\_network\_connectivity function is called by the parallel function parLapply, which is similar to mapply but operates in parallel. Using this, we perform ICA, establish functional connectivity and measure the graph structures in parallel. The function makeForkCluster is called to create multiple identical R processes on the same machine with a copy of the master workspace. This function will not work in Windows since Windows does not have a fork system call, so sockets must be used instead.

```
> cl <- makeForkCluster()
> clusterSetRNGStream (cl, 123)
> res <- parLapply(cl, seq_len(4), func_network_connectivity)
> stopCluster(cl)
```
## Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations

## *Marie-Christine Ottet 1,2\*, Marie Schaer 1,3, Martin Debbané1,4, Leila Cammoun2, Jean-Philippe Thiran2 and Stephan Eliez 1,5*

*<sup>1</sup> Departement of Psychiatry, Office Médico-Pédagogique (OMP), University of Geneva School of Medicine, Geneva, Switzerland*

*<sup>2</sup> Signal Processing Laboratory (LTS5), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland*

*<sup>3</sup> Department of Psychiatry, Stanford Cognitive and System Neuroscience Laboratory, Stanford University, Palo Alto, CA, USA*

*<sup>4</sup> Adolescence Clinical Psychology Research Unit, University of Geneva, Geneva, Switzerland*

*<sup>5</sup> Department of Genetic Medicine and Development, University of Geneva School of Medicine, Geneva, Switzerland*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

*Reviewed by: Qingbao Yu, The Mind Research Network, USA Alex Fornito, University of Melbourne, Australia*

#### *\*Correspondence:*

*Marie-Christine Ottet, Department of Psychiatry, Office Médico-Pédagogique, University of Geneva School of Medicine, Rue David-Dufour 1, Case postale 50, 1211 Geneva 8, GE, Switzerland e-mail: marie-christine.ottet@ unige.ch*

Schizophrenia is postulated to be the prototypical dysconnection disorder, in which hallucinations are the core symptom. Due to high heterogeneity in methodology across studies and the clinical phenotype, it remains unclear whether the structural brain dysconnection is global or focal and if clinical symptoms result from this dysconnection. In the present work, we attempt to clarify this issue by studying a population considered as a homogeneous genetic sub-type of schizophrenia, namely the 22q11.2 deletion syndrome (22q11.2DS). Cerebral MRIs were acquired for 46 patients and 48 age and gender matched controls (aged 6–26, respectively mean age = 15*.*20 ± 4*.*53 and 15*.*28 ± 4*.*35 years old). Using the Connectome mapper pipeline (connectomics.org) that combines structural and diffusion MRI, we created a whole brain network for each individual. Graph theory was used to quantify the global and local properties of the brain network organization for each participant. A global degree loss of 6% was found in patients' networks along with an increased *Characteristic Path Length*. After identifying and comparing hubs, a significant loss of degree in patients' hubs was found in 58% of the hubs. Based on Allen's brain network model for hallucinations, we explored the association between local efficiency and symptom severity. Negative correlations were found in the Broca's area (*p <* 0*.*004), the Wernicke area (*p <* 0*.*023) and a positive correlation was found in the dorsolateral prefrontal cortex (DLPFC) (*p <* 0*.*014). In line with the dysconnection findings in schizophrenia, our results provide preliminary evidence for a targeted alteration in the brain network hubs' organization in individuals with a genetic risk for schizophrenia. The study of specific disorganization in language, speech and thought regulation networks sharing similar network properties may help to understand their role in the hallucination mechanism.

#### **Keywords: DTI, small-world, network, Broca, psychosis, schizophrenia, human connectome, Wernicke**

## **INTRODUCTION**

22q11.2 deletion syndrome (22q11.2DS), also known as velocardio-facial syndrome (Shprintzen et al., 1978), is a wellestablished neurogenetic model for studying the pathogenesis of schizophrenia (Bassett and Chow, 1999). The prevalence rate of the 22q11DS population for developing schizophrenia is about 30%, making it the third highest risk rate after having an affected monozygotic twin (50% risk) or both parents being affected (46% risk) (McGuffin et al., 1995). Furthermore, 30–50% of nonschizophrenic 22q11DS individuals demonstrate sub-threshold symptoms of psychosis (Feinstein et al., 2002). Considering the genetic 22q11.2DS model as a homogenous sub-type may highlight the presence of neurodevelopmental biomarkers underlining the schizophrenic disorders.

As schizophrenia is a heterogeneous disorder previous literature on white matter has revealed highly variable alterations throughout the brain and a few replicated findings (see Fitzsimmons et al., 2013 for a recent review). Due to confounding factors such as duration of illness, medication, age sample or methodology, it remains unclear whether schizophrenia demonstrates localized alterations or a whole brain dysconnection. Graph theory provides promising tools to analyze both whole brain phenomenon using global network measurements, and specific properties using local network measurements (Bassett and Bullmore, 2009; He and Evans, 2010; Rubinov and Sporns, 2010). Few studies in schizophrenia have used the graph theory in structural magnetic resonance imaging. Bassett et al. (2008) characterized the cerebral gray matter volumetric covariation in a large sample (*>*200) of patients with schizophrenia compared to healthy controls. Although the small-world properties were preserved, a reduced network hierarchy with a loss of hubs was found in individuals with schizophrenia and more specifically in frontal (bilateral dorsolateral prefrontal cortex) areas (Bassett et al., 2008). Graph theory was also used in studies of functional connectivity in schizophrenia. Evidence of global rather than focal functional dysconnectivity grows in schizophrenia literature. Disruption of the small-world properties has been found in patients (Liu et al., 2008) as well as weaker connectivity and lower clustering coefficient (Yu et al., 2012), and hubs alteration (Yu et al., 2012, 2013). The functional connection of several specific regions has also been found such as the temporal lobe, the parietal lobe, the thalamus, hippocampus but more interestingly, functional integration between sub-networks (such as the semantic network, the default network) is impaired (see Zhang et al., 2012; Alexander-Bloch et al., 2013). Using diffusion images for reconstructing brain networks, van den Heuvel et al. (2010) replicated the observation that individuals with schizophrenia have preserved small-world properties and increased path length in frontal, temporal and occipital areas. Reduction of frontal hubs has also been demonstrated, involving the superior frontal and the anterior cingulate (van den Heuvel et al., 2010). Following Latora and Marchiori's conception of global and local efficiency measuring how well information is exchanged over the network (Latora and Marchiori, 2001), Wang et al. (2011) demonstrated that individuals with schizophrenia have a reduced global efficiency [reduced after controlling for the effect of age, gender and brain size (Wang et al., 2011)]. However, it remains unclear whether the network alterations are caused by the emergence of the schizophrenia disorder or if there is a predetermined network configuration that acts as a vulnerability factor for the later development of schizophrenia.

Amongst all the psychotic symptoms in the 22q11DS, hallucinations are often considered the most clinically salient signs of risk for psychosis (Debbané et al., 2006). Moreover, hallucinations constitute a valid early risk indicator for the development of schizophreniform disorders during adulthood (Poulton et al., 2000). As complex cognitive functions rely on a cerebral network involving several regions, (Sporns, 2010; Bassett and Gazzaniga, 2011) dysfunctions such as hallucination may result from abnormal topological connectivity between these areas (Lo et al., 2011). Structural and functional studies in individuals with schizophrenia suggest that several key regions play a role in the apparition of hallucinations and their severity [(see Allen et al., 2008) for a review]. In schizophrenic patients, reduction in the superior temporal gyrus (STG) gray matter volume has been associated with the severity of hallucinations (Flaum et al., 1995; Gaser et al., 2004; Onitsuka et al., 2004). Loss of gray matter volume in Broca's area has also been associated with this symptom (Gaser et al., 2004; Sumich et al., 2005). Several other brain areas, such as the insula (Shapleske et al., 2002), the thalamus (Neckelmann et al., 2006) and the supramarginal gyrus (Gaser et al., 2004), have been associated with the presence of hallucinations but have failed to show consistent volumetric reduction. Two network studies have explored the relationship between clinical symptoms of schizophrenia and brain network properties. Although van den Heuvel et al. (2010) found no significant association between the Positive and Negative Symptoms Scale (PNASS) (Kay et al., 1987) and topological features, Wang et al. (2011) showed a negative correlation between the PANSS (positive, negative and total scores) and global and local efficiency, meaning that the more severe the symptoms, the lower are both the local and the global topological efficiencies. Lower global efficiency and longer path length has also been related to higher score on the negative PANSS scale (Yu et al., 2011a,b).

The purpose of this present work is to study the global and local network features in a population at high genetic risk for schizophrenia (22q11.2DS) by focusing on the hierarchical structure of the brain network (hub topological configuration). Furthermore, we aim to explore the specific relationship between hallucination symptoms and the local efficiency of the related brain areas. According to Allen's model of brain regions involved in hallucinations (Allen et al., 2008), we suggest that the topological connectivity of the following regions—DLPFC, dorsal anterior cingulate, Broca's area, ventral anterior cingulate, orbitofrontal gyrus, and STG—will be associated with the severity of hallucinations in schizophrenia.

## **MATERIALS AND METHODS PARTICIPANTS**

All the participants underwent the same protocol, which included an MRI session for collecting a structural T1-weigthed image and a diffusion image along with an IQ measure with the Wechsler Intelligence Scale for Children-Third Edition revised (Wechsler, 1991) or the Wechsler Adult Intelligence Scale-III for adults (Wechsler, 1997). The participant or their parents signed consent forms containing information about the study and its purpose. The detailed protocol of the study was previously reviewed and accepted by the Institutional Review Board of Geneva University School of Medicine.

## **22q11.2DS GROUP**

Forty-six participants with a 22q11.2DS aged between 6 and 26 (mean = 15*.*20 ± 4*.*53), (23 males and 22 females) were recruited through parent associations in French speaking European countries. The 22q11.2 deletion was confirmed by a blood sample analyzed with the Quantitative Fluorescent Polymerase Chain Reaction (QF-PCR) performed on the deleted region. The average IQ was of 77*.*5 ± 16*.*6. All the patients were assessed by an experienced psychiatrist using the *Brief Psychiatric Rating Scale* (BPRS) (Leucht, 2005), the *Diagnostic Interview for children and adolescents* (DICA) (Reich, 2000) and the *Structured Clinical Interview for DSM-IV AXIS I Disorders* for adults (SCID) (First et al., 1996). Only one patient fulfilled the criteria for schizophrenia. The average BPRS hallucination subscale for the forty-six participants was 1*.*63 ± 1*.*12 and among them fourteen individuals reported to have verbal hallucination.

## **CONTROL GROUP**

The participants from the control group were recruited among the siblings of the patients and in the community. The 48 control participants comprised 25 males and 24 females, aged from 7 to 24 (mean = 15*.*28 ± 4*.*35). The average IQ was of 107 ± 18*.*12. None of the controls had present or past history of psychiatric or neurological disorders.

## **MRI CHARACTERISTICS**

Using a Siemens Trio 3 Tesla scanner, we acquired a set of two cerebral MRIs for each participant. A T1-weighted sequence with a 3D volumetric pulse was collected using the following sequence: *TR* = 2500 ms, *TE* = 3 ms, flip angle = 8◦, acquisition matrix of 256 × 256, field of view = 22 cm, slice thickness = 1.1 mm, 192 slices. The second MRI was a Diffusion Tensor Imaging (DTI) with the following parameters: number of directions = 30, *b* = 1000 s/mm2, *TE* = 82 ms, *TR* = [8300–8800] ms, flip angle = 90◦, acquisition matrix of 128 × 128, field of view 25.6 cm, slice thickness = 2 mm.

## **IMAGE PROCESSING**

The two acquired scans were processed for each participant using the Human Connectome Mapper (http://connectomics*.* org, Daducci et al., 2012). The software is a pipeline of several other software programs, combining each dedicated package for the purpose of creating an individual's connectome. For the T1-weigthed image, FreeSurfer software starts to remove all nonbrain tissue, segmenting the image in order to extract the white matter, the sub-cortical gray matter volume and the cortical surface (Dale et al., 1999; Fischl et al., 1999). This step is performed using both intensity and continuity data through the whole 3D volume. The surfaces and volumes generated have been validated against histological studies (Rosas et al., 2002). However, these automatic steps need verification and manual correction if necessary. At the end of this process, three-dimensional volumes or surfaces and a cortical segmentation are available For the diffusion images, first we use a correction for the effect of head motion and distortion of eddy currents through an affine alignment using the FLIRT tool of the FSL-FDT software (Jenkinson and Smith, 2001). The realigned images are used to reconstruct the white matter macroscopic bundles using the streamline deterministic tractography of Diffusion Toolkit (http://trackvis*.*org/). The registration of the T1-weighted onto the diffusion images is an affine transformation using the *intensity-based linear registration* tool FLIRT. When combined, the intersection of the estimated fibers and the segmented regions creates a connectome which is represented by the connection matrix.

#### **NETWORK MEASURES**

Graph theory describes the human connectome as a network of nodes (in our case cortical regions) and edges (in our case white matter bundles) connecting two nodes. This network can be described either with weighted edges (where edges contain the information about the strength of the two nodes) or with binary edges (where only the existence of a link is represented). Therefore, two kinds of measurements are applicable: measurements on a binary network or on a weighted network. Following the purpose of the present study, which is to try to delineate the core organization of individuals with 22q11.2DS, we decided to explore the configuration of the binary network. Furthermore, binary network analyses have the advantage of showing a low variability in the network measures (Cheng et al., 2012).

In the present study, we used the tools for measuring network properties included in the brain connectivity toolbox developed for Matlab (https://sites.google.com/site/bctnet/) (see Rubinov and Sporns, 2010) for the description and mathematical formula of each measure. The first measurement step is the analysis of the global characteristics of the patients' and controls' networks, using the *Characteristic Path Length*, the *Mean Clustering* *Coefficient*, the *Global Transitivity*, the *Global Efficiency* and the *Global Degree*. In the context of the binary networks, the *Global Degree* represents the total number of edges (existing connections between two nodes) in the network. The *Mean Clustering Coefficient* measures the potential for functional segregation of the network and is calculated as the mean of the *clustering coefficient*, which is the fraction of the number of neighbors of a node that are also neighbors of each other (Watts and Strogatz, 1998). The *Characteristic Path Length* represents the average of the short path lengths of the network. The short path length is the number of edges that have to be crossed to go from one node to another. The *Characteristic Path Length* therefore measures the functional integration potential of a network. The normalized ratio between the *Mean Clustering Coefficient* and the *Characteristic Path Length* of a network gives the *Smallworldness* measure of the network (Watts and Strogatz, 1998; Humphries and Gurney, 2008). The *Smallworldness* measures the optimality between rapid communication throughout the network (functional integration) and the capacity to process locally based information (functional segregation) (Sporns and Honey, 2006). The optimal balance between the *Characteristic Path Length* and the *Mean Clustering Coefficient* can also be estimated by the *Global Efficiency* and the *local efficiency* (Latora and Marchiori, 2001). The *Global* and *local efficiency* measure how efficiently information is exchanged over the network, and respectively play similar roles to the *Characteristic Path Length* and the *clustering coefficient*.

The second analysis focuses on the hub configuration by ranking all of the nodes in the healthy control network on three local network measures, the *local degree*, the *clustering coefficient* and the *betweenness centrality*. The *local degree* measures a node's number of edges or neighbors. The *clustering coefficient* highlights a node's surrounding configurations by analyzing how many of its neighbors are also connected to each other. The *betweenness centrality* measures how many short path lengths pass through the node and is therefore a measure of node influence on the network (Rubinov and Sporns, 2010). By definition, the 20% highest ranking nodes for all 3 values are considered to be the network hubs (Sporns et al., 2007; Sporns, 2010; van den Heuvel et al., 2010; van den Heuvel and Sporns, 2011). The analysis then continues by comparing the hubs and node degrees between the 22q11.2DS group and the healthy control group.

The third step consists of exploring the relationship between Allen's model's network characteristics (*local efficiency)* and the clinical measures such as the presence and severity of hallucinations (BPRS). According to Allen's model of hallucinations, the DLPFC, dorsal anterior cingulate, Broca's area, ventral anterior cingulate, orbitofrontal gyrus, the supplementary motor area and STG are the brain regions involved in hallucinations. These regions are not represented in the same way in the Freesurfer Desikan parcellation scheme: region that corresponds the most to the DLPFC is the rostral middle frontal parcel; for the superior temporal gyrus it is the superior temporal parcel and the transverse temporal parcel; for the ventral and dorsal anterior cingulated it is respectively the rostral anterior parcel and the caudal anterior cingulate parcel; the inferior frontal gyrus including Broca's area refers to the pars triangularis parcel, the pars opercularis parcel and the pars orbitalis parcel; and the orbito frontal gyrus corresponds to the lateral orbito-frontal parcel and the medial orbito-frontal parcel (see **Figure 1**). We decided not to include the supplementary motor area, as it is equally spread over three Freesurfer's regions (the caudal middle frontal, the superior frontal and the precentral).

As Latora and Marchiori (2001) demonstrated, the local efficiency measures the functional segregation of one node when this particular node is removed from the sub-network. This measure represents the level of local information-processing. More precisely the local efficiency measures the functional segregation which means the capacity of locally processed information. In the case of hallucination, Allen et al. postulated a neuroanatomical model, composed of several cortical regions where their dysfunctional interplay fosters the hallucination emergence. Therefore, we wanted to see if the local capacity of this sub-network to process the information was related to the emergence and the severity of the hallucination.

In order to analyze the efficiency of communication in Allen's theoretical network, we compared the local efficiency value of each node between patients and healthy participants. Then, in the patients' networks, we analyzed the relationship between each node's efficiency and the BPRS hallucination subscale. For the ten regions, an outlier analysis was applied on the efficiency

medial orbitofrontal is fuchsia, the rostral anterior cingulate is dark purple,

measurement and the correlation was controlled for age and gender.

#### **SIMULATION METHOD**

As previous literature in the 22q11.2DS demonstrated that the brains of patients with 22q11.2DS show a 10% volumetric reduction (Eliez et al., 2000; Kates et al., 2001), which has an impact on the number of fibers (∼10% less fibers) (Ottet et al., 2013), in the current study we simulated a random 10% reduction on the controls' connectome. This simulation enables a determination of whether the network measures applied for comparing both groups are biased by the reduction of the number of fibers or not. Using Matlab, we randomly subtracted one fiber at a time until 10% of the total number of fibers in the network were removed. This procedure was replicated for each healthy control connectome before applying the global network measurements.

## **RESULTS**

#### **GLOBAL NETWORK MEASURES**

All the global network results comparing patients, controls and simulated networks can be found in **Table 1**.

The one tailed *t*-test comparison between the number of fibers contained in the patients' network and the controls' network revealed a significant loss of 10% of fibers (*p <* 0*.*001). The same test was applied between the patients' and the simulated network revealing that the significance was no longer preserved (*p* = 0*.*59). Although the simulated network didn't show any difference in the number of fibers compared to the patient group, the number of edges demonstrated a connectivity reduction of 6% (*p <* 0*.*005). Similarly, the *Global Efficiency* was significantly reduced in the brain network of the patients (*p* = 0*.*0117). No difference was observed in *the Mean Clustering Coefficient* between the two groups. Although the *Characteristic Path Length* was increased for the patient group (*p* = 0*.*04), the *Smallworldness* measure did not differ significantly between the two groups (*p* = 0*.*368).

#### **HUB ANALYSIS**

To determine which of the 83 nodes are hubs in the healthy control network, each node was ranked on 3 local network measures: the *local degree*, the *betweenness centrality* and the *clustering*

**Table 1 | Mean, standard deviation and significant differences in the brain network global measures for participants with 22q11.2DS, healthy participants and simulated networks.**


*The first p column from the left reports in bold the significant value (p < 0.05) of the comparison between the 22q11.2DS and control network. The second reports in bold the significant values (p < 0.05) when comparing the 22q11.2DS with the simulated network.*

and the caudal cingulate is parme.

*coefficient.* According to the literature, hubs are defined as nodes that demonstrate a high degree, a high centrality and a low clustering coefficient. A ranking score was attributed to each of the nodes for the three measures previously discussed and averaged for the 48 healthy controls. The highest connected and the most central node scored 83 and the least connected and least central node scored 1. Inversely the least clustered node scored 83 and the most clustered scored 1. The final classification is the addition of the three scores, in which the top 20% are considered as the connector hubs of the network (see yellow bars in **Figure 2**). Although the stem node was the highest node on the final classification, we did not consider it as a hub because it is not a gray matter region (therefore all subsequent analyses considered 82 regions and not 83). Twelve out of 17 hubs were represented on both hemispheres in the superior frontal, the hippocampus, the superior parietal, the precuneus, the precentral and the putamen. In the right hemisphere two supplementary hubs are found, the rostral middle frontal and the lateral orbito-frontal. In the left hemisphere, three supplementary hubs were found, the superior temporal the lateral occipital and the thalamus.

#### **DEGREE ANALYSIS**

The results demonstrated that 26 nodes out of 82 (33%) in the patients' networks have a significantly reduced degree after FDR correction (pFDR = 0.0149). The names of the brain regions affected are listed in **Table 2**. Only one node showed an increased degree in the patients' networks: the right supramarginal region.

When analyzing how many of the hubs are altered, we noted that 10 out of 17 hubs (58%) were reduced in the patients network (see **Figure 3**). Conversely only 16 non-hubs out of 65 (25%) were reduced (see **Table 2**). Therefore, the percentage of affected hubs is more than double that of affected non-hub nodes. The 10 affected hubs are the bilateral hippocampus, superior parietal and precentral regions, right rostral middle frontal, superior frontal, precuneus and left thalamus.

## **EFFICIENCY AND HALLUCINATIONS**

Following Allen's brain network model, the efficiency of ten nodes of the left hemisphere was correlated with the BPRS hallucination subscale. After controlling for age and gender, three of them showed a significant association with the presence and/or severity of the symptoms. Both the pars triangularis parcel and the transverse temporal parcel demonstrated a negative correlation with

**FIGURE 3 | Graph representation of the mean brain network for patients and controls using Gephi (http://gephi***.***org/) to produce optimal visualization of all the nodes and connections embedded in the networks.** The circled nodes are the hubs of the network. The red circles are altered hubs and the black circles are preserved hubs. Every nodes contained in the same lobe or cerebral structure has the same color, blue for the node of the frontal lobe, magenta for the cingulate areas, green for the parietal lobe, yellow for the occipital lobe, brown for the temporal lobe and gray for the subcortical areas. The size of the nodes indicates their degree level.

the symptom's scale (respectively *R* = −0*.*312, *p* = 0*.*04, *uncorrected* and *R* = −0*.*354, *p* = 0*.*023, *uncorrected*). Inversely, the rostral middle frontal shows a positive correlation with the symptom's scale (*R* = 0.373, *p* = 0*.*014, *uncorrected*) (see **Figure 4**).


**Table 2 | List of the 26 nodes out of 82 that are significantly different in the patients' network after FDR correction, and indication as being hubs or not.**

**FIGURE 4 | Correlations between the BPRS hallucination subscale and the network efficiency in individuals with 22q11.2DS after age and gender correction.** On the left hemisphere, the red regions (pars triangularis and transverse temporal) represent a negative correlation and the blue region (rostral middle frontal) represents a positive correlation.

The level of IQ, an additional controlling variable, was introduced, but nevertheless the significance for the three correlations survived.

## **DISCUSSION**

Using white matter deterministic tractography and gray matter surface-based parcellation to reconstruct the brain connectome, the present work is the first study that analyzes the brain connectome in the 22q11.2DS in the light of the graph theory framework. In line with previous white matter studies in the 22q11DS, we found a 10% reduction in the number of fibers in the brain connectome of patients with 22q11DS (Eliez et al., 2000; Kates et al., 2001; Gothelf et al., 2011; Ottet et al., 2013). We also observed a 6% reduction of connectivity in patients, which means that 6% of the edges of the patients' network were missing. The simulation analysis demonstrated that the initial 10% fiber loss was not the cause of the 6% connectivity loss. This global dysconnection found in our analyses sustains the dysconnection hypothesis in schizophrenia (Friston and Frith, 1995; Stephan et al., 2009).

Despite the observed global disconnection, graph theoretical analysis comparing individuals with a high risk of developing schizophrenia (22q11.2DS) and healthy controls revealed that the smallworldness property of the patients' brain network was still preserved. However, a longer path length, similar to a lower global efficiency, demonstrates that the functional integration [capacity to transmit information more directly, with less interference or attenuation (Latora and Marchiori, 2001)] is reduced in 22q11.2 deletion syndrome. Although the global clustering coefficient is not significantly different from the healthy controls, the smallworld brain organization of patients with 22q11.2DS tends to be closer to a regular network organization, in which the functional segregation (capacity to process specialized information, organized in clusters) is preserved but the functional integration is not optimum. Our study is in line with the previous network analysis on non-syndromic schizophrenia patients where a longer *Characteristic Path Length* has been systematically found (van den Heuvel et al., 2010; Zalesky et al., 2010b; Wang et al., 2011).

In the present work, local connectivity analysis allowed localization of the network nodes that were significantly altered in the patients' network. Every lobe and sub-cortical structure had one or more disconnected nodes sustaining the hypothesis that there is a widespread impact on the brain network in schizophrenia (Fornito et al., 2012). However, among the nodes that had a lower connectivity in patients, the proportion of affected hubs compared to non-affected hubs (58%) is interesting. Similarly in schizophrenia, previous findings shows a loss of hub connectivity specifically in frontal lobes (van den Heuvel et al., 2010). Our finding shed light on a possible targeted alteration of cerebral hubs in the 22q11.2DS that may also have some relevance for schizophrenia. Network hubs have special integrative or control functions as their privileged position in the hierarchical organization is postulated to be a key element for large-scale cognitive abilities. Because of their high centrality and influence any perturbation in a hub would heavily impact brain network function (Sporns et al., 2007). The major hub connectivity alteration in individuals with 22q11.2DS may explain their mild cognitive impairments, but also the cognitive collapse seen in schizophrenia during and after their first psychotic episode.

Schizophrenia is a disease with multifactorial etiologies. However, among the multiple causes, genetic factors play an important role (Stephan et al., 2006). Studying brain alterations in populations with high risk or ultra-high risk of developing schizophrenia could break out the predetermining cerebral organization leading to the development of psychosis (Cannon et al., 2007). To the best of our knowledge, no study to date has measured the network properties of white matter connectivity using graph theory in adolescents at risk for schizophrenia. However, a recent study by Shi et al. (2012) demonstrates a significant reduction of global efficiency, an increased global Characteristic Path Length, less hub nodes and lower edge "global efficiency" in neonates' brain networks having a mother with schizophrenia or a schizoaffective disorder. These results suggest that the topological abnormalities in individuals carrying a familial genetic risk for schizophrenia can already be observed a few weeks after birth (Shi et al., 2012). As the 22q11.2 deletion syndrome is commonly considered as a high risk population for schizophrenia (Bassett and Chow, 1999), our study adds some evidence to the hypothesis that early alterations in a cerebral network organization due to genetic factors may partially drive the development of schizophrenia and psychotic symptoms such as hallucinations.

Indeed, the graph theory of large-scale brain networks postulates that cognitive abilities arise from several cerebral regions interacting together (Bressler and Menon, 2010). In our graph network study, we explored the relationship between the alterations of a topological network property and a cognitive dysfunction. Our analyses revealed that the local efficiency value within three parcels of the left hemisphere, namely Broca's area (pars triangularis), Wernicke's area (transverse temporal) and the dorsolateral prefrontal cortex (DLPFC) (rostral middle frontal), correlated significantly with clinical ratings of hallucination severity. Efficiency values in Broca and Wernicke's areas suggested that, as local connections within these parcels decreased, the severity of hallucinatory phenomena increased in our 22q11DS sample. Given the implication of these areas in language production and comprehension, our results suggest that impairments in the different components of language processing in 22q11DS may significantly contribute to the expression of hallucinations. These findings are consistent with previously reported associations between a decrease in verbal IQ and psychotic symptoms (Gothelf et al., 2005; Debbané et al., 2006), and further suggest that local network connectivity in key language areas of the brain contributes to hallucinations in this deletion syndrome (Gothelf et al., 2011).

Hallucination severity further correlated positively with efficiency within the dorsolateral prefrontal parcel. The DLPFC region is one of the most consistently examined regions in MRI studies involving individuals with schizophrenia (Lewis et al., 2004) because of its implications at different levels of impairments, from working memory and executive functions to dopaminergic system malfunction (Tanaka, 2006). Neuromodulation of the DLPFC activity and its role in regulation of thought content is hypothesized to depend on its connectivity patterns with surrounding regions (Arnsten et al., 2012). In this perspective, our results may suggest that an atypically high local efficiency in 22q11DS works against the DLPFC's connectivity with surrounding parcels that modulate its activity. This impairment may thus increase the propensity to experience hallucinations. Future functional MRI studies should examine the connectivity dynamics of the DLPFC more specifically to evaluate its contributions to symptoms such as hallucinations. Overall, our results add important information about the relevance of brain topological network organization to previous longitudinal investigations in 22q11DS that have linked cerebral integrity of gray matter in the DLPFC to the development of psychosis (Gothelf et al., 2005; Schaer et al., 2009; Gothelf et al., 2011). Further research is necessary to understand the maturational dynamics between gray and white matter, and how these may interact to increase the potential for psychotic symptoms in 22q11DS.

## **LIMITATIONS**

In this study, Graph theory principles were applied for the first time on a population with 22q11.2DS bringing new insight on the alterations in their brain organization that could in turn lead to schizophrenia symptoms. Nevertheless, the present work shows some limitations.

Although we choose the recommended ratio between scan timing, voxel size and the number of directions, the same limitations found in every DTI and tractography study remain present. Listed in detail in (Bammer et al., 2003), the major limitations are the absence of *in vitro* validation studies and the distance-related effect of the tractography [see the limitation section in (Ottet et al., 2013)]. This distance related effect biases the number of fibers included in a bundle. Nevertheless, as the present work used unweighted (binary) network analysis, we do not take in consideration the number of fibers, therefore the latter issue is not expected to influence our analyses.

The comparison of brain networks when assessing their properties with graph theory measures, is very sensitive with regard to two constitutive elements, first the choice of the nodes and second the connection density. The first limitation relies on the choice of the cortical parcellation and its scale. Indeed, the choice of a network node is a critical step (He and Evans, 2010). Zalesky et al. (2010a,b) demonstrated the strong influence on network measures of different choices of parcellation scales and diffusion imaging, which impairs the comparability and the constituency across studies (Zalesky et al., 2010a). A wider discrepancy arises when comparing two different parcellation scales (80 vs. 4000 nodes) and/or when comparing two different diffusion imaging techniques [High angular (HARDI) diffusion vs. DTI]. The analyses in our study do not suffer from this issue as we compared our patients' cerebral diffusion image with the same sequence in healthy control cerebral images. Furthermore, for both patients and controls we used the same connectome processing pipeline including the parcellation scheme, which demonstrated a good to excellent test-retest reliability on graph network measures (Owen et al., 2013). Therefore, the comparison between the two populations of the global and local network measures does not suffer from this kind of issue.

Nevertheless, the Desikan parcellation scheme we chose for processing our participants' brain may yield an issue. Although this parcellation is based on the primary and secondary sulci delineation which confers a very high reliability across humans brains (Desikan et al., 2006), the size of the parcels differ importantly. Thus, when delineating the hubs of the network, the very large parcels display a higher degree value, which is one of the three measures used to rank the nodes onto the hub scale. This concern is valid for the two other measures used for delineating the hubs (clustering coefficient and the centrality). However, the hubs we found were highly consistent with previous literature. Indeed, Li et al. (2011) evaluated as the highest reliable hubs the

#### **REFERENCES**


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bilateral putamen, bilateral superior frontal and left precuneus among three tractography methods (Li et al., 2011). van den Heuvel and Sporns (2011) found that bilaterally, the precuneus, superior frontal and superior parietal, hippocampus, putamen and thalamus were hubs of structural derived networks (van den Heuvel and Sporns, 2011). Amongst all the hubs delineated in our study, only the bilateral precentral could have been elected because of its large size.

The second limitation concerns the difference in the number of connections that exist between the control and patient networks. This discrepancy may bias the topological measurements and may result in the significant findings. As van Wijk et al. shows in 2010, there is no way to rule out these differences without introducing another bias.

## **CONCLUSION**

In the present study, the targeted dysconnectivity of the hubs in a population considered as a model for schizophrenia (22q11.2DS) suggests the existence of an early alteration in the cerebral network organization that is due to genetic factors which may partially drive the development of schizophrenia and psychotic symptoms. Furthermore, altered local efficiency in areas responsible for language processing (Broca and Wernicke) sheds light on the implication of structural network organization in the severity of hallucinations. Further research is needed to understand the interaction between structural networks and psychotic symptoms.

#### **ACKNOWLEDGMENTS**

This research was funded by the Swiss National Research Fund, with funds to Dr. Stephan Eliez (3200-063135.00/1, 3232-063134.00/1, PP0033-102864 and 32473B-121996), and by the National Center of Competence in Research (NCCR) "SYNAPSY—The Synaptic Bases of Mental Diseases" financed by the Swiss National Science Foundation (nu 51AU40\_125759) and by the Center for Biomedical Imaging (CIBM) of the Geneva-Lausanne Universities and the EPFL, as well as the foundations Leenaards and Louis-Jeantet. The authors would also like to warmly thank all the participants and their family for volunteering in this study. We also greatly thank Sarah Menghetti for organizing and collecting data, and Jason Last for proof reading this manuscript. Our thank goes also to François Lazeyras, Frank Henri, Yohann Ouvrier-Buffet.


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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 08 April 2013; accepted: 09 July 2013; published online: 05 September 2013.*

*Citation: Ottet M-C, Schaer M, Debbané M, Cammoun L, Thiran J-P and Eliez S (2013) Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations. Front. Hum. Neurosci. 7:402. doi: 10.3389/fnhum.2013.00402*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Ottet, Schaer, Debbané, Cammoun, Thiran and Eliez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Shifted intrinsic connectivity of central executive and salience network in borderline personality disorder

## *Anselm Doll1,2, 3 , 4 †, Christian Sorg1,2, 3 \*†, Andrei Manoliu1,2, 3 , AndreasWöller1, Chun Meng2, 3 , Hans Förstl 1, Claus Zimmer <sup>2</sup> , Afra M.Wohlschläger 2, 3 ,5 and Valentin Riedl 2,3, 6*

*<sup>1</sup> Department of Psychiatry, Klinikum rechts der Isar, Technische Universität München, Munich, Germany*

*<sup>4</sup> Munich Center for Neurosciences – Brain and Mind, Ludwig-Maximilians-Universität München, Martinsried, Germany*

*<sup>6</sup> Department of Nuclear Medicine, Klinikum rechts der Isar, Technische Universität München, Munich, Germany*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Björn H. Schott, Leibniz-Institut für Neurobiologie, Germany Shaozheng Qin, Stanford University, USA*

#### *\*Correspondence:*

*Christian Sorg, Department of Neuroradiology and Psychiatry, Klinikum rechts der Isar, Technische Universität München, Ismaninger Strasse 22, 81675 München, Germany e-mail: c.sorg@lrz.tum.de*

†*Anselm Doll and Christian Sorg have equally contributed to the study.*

Borderline personality disorder (BPD) is characterized by "stable instability" of emotions and behavior and their regulation.This emotional and behavioral instability corresponds with a neurocognitive triple network model of psychopathology, which suggests that aberrant emotional saliency and cognitive control is associated with aberrant interaction across three intrinsic connectivity networks [i.e., the salience network (SN), default mode network (DMN), and central executive network (CEN)]. The objective of the current study was to investigate whether and how such triple network intrinsic functional connectivity (iFC) is changed in patients with BPD. We acquired resting-state functional magnetic resonance imaging (rs-fMRI) data from 14 patients with BPD and 16 healthy controls. High-model order independent component analysis was used to extract spatiotemporal patterns of ongoing, coherent blood-oxygen-level-dependent signal fluctuations from rs-fMRI data. Main outcome measures were iFC within networks (intra-iFC) and between networks (i.e., network time course correlation inter-iFC). Aberrant intra-iFC was found in patients' DMN, SN, and CEN, consistent with previous findings. While patients' inter-iFC of the CEN was decreased, inter-iFC of the SN was increased. In particular, a balance index reflecting the relationship of CEN- and SN-inter-iFC across networks was strongly shifted from CEN to SN connectivity in patients. Results provide first preliminary evidence for aberrant triple network iFC in BPD. Our data suggest a shift of inter-network iFC from networks involved in cognitive control to those of emotion-related activity in BPD, potentially reflecting the persistent instability of emotion regulation in patients.

**Keywords: resting-state functional connectivity, brain networks, central executive network, default mode network, salience network, brain connectivity, large-scale networks, triple network hypothesis**

## **INTRODUCTION**

Borderline personality disorder (BPD) is characterized by "stable instability" (Schmideberg, 1959) of emotions, impulsivity, social relationships, and self-image. Additionally most patients suffer from chronic feelings of emptiness, complex dissociations, self-injury, and suicidal tendencies with a suicide rate of 10% (Oldham, 2006). BPD, which often co-occurs with other psychiatric disorders (about 85% of patients with BPD fulfill criteria for having at least one Axis I disorder; Lenzenweger et al., 2007), is common with a prevalence of more than 20% for psychiatric inpatients (Torgersen, 2005). Behavioral and emotional dysregulation is suggested as critical factors underlying this variety of symptoms (Leichsenring et al., 2011). We suggest that the stability of fluctuating symptoms across time and different situations might be related to consistent and profound functional alterations in the patient's brain intrinsic functional architecture, particularly in brain regions involved in behavior/emotion regulation.

Previous functional neuroimaging studies revealed context specific patterns of altered brain activity in BPD patients during

emotion- or self-related tasks. For example, negative emotional pictures or fearful/angry faces evoke stronger activity in the extrastriate, posterior cingulate, and frontal cortices, as well as weaker activity in the amygdala (Minzenberg et al., 2007; Koenigsberg et al., 2009a; Niedtfeld et al., 2010; Hazlett et al., 2012). In healthy subjects, self-distancing of negative pictures activates parietal regions overlapping with the so-called default mode network (DMN) including the medial prefrontal, medial and lateral parietal cortex (Koenigsberg et al., 2009b). Patients with BPD, however, fail to activate the DMN but show increased activity in the amygdala. On the contrary, memories of unresolved life events activate regions of the DMN in addition to amygdala, insula, and occipital cortices in patients (Beblo et al., 2006). Overall, emotional and self-related context increasingly activates an aberrant distributed pattern of brain regions including the DMN, insula, amygdala, and occipital cortices in BPD patients.

The measure of intrinsic functional connectivity (iFC), i.e., coherence of ongoing blood-oxygenation-level-dependent (BOLD) signal fluctuations in resting-state functional magnetic

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*<sup>2</sup> Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany*

*<sup>3</sup> TUM-Neuroimaging Center, Technische Universität München, Munich, Germany*

*<sup>5</sup> Department of Neurology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany*

resonance imaging (rs-fMRI) data, is a surrogate for organized intrinsic brain activity (Fox and Raichle, 2007). At a large-scale level, coherent BOLD activity across remote brain areas forms consistent intrinsic connectivity networks (ICNs) in humans (Damoiseaux et al., 2006). Importantly, ICNs show strong spatial correspondence in independent analyses of resting-state and task-related activity patterns (Smith et al., 2009; Laird et al., 2011), suggesting that certain intrinsically coupled functional networks are also systematically engaged during cognition and behavior. Moreover, direct evidence exists that ongoing activity in ICNs serves as a scaffold for patterns of evoked neuronal activity (Keller et al., 2011), supporting the idea that the intrinsic architecture maintains and updates the brain's repertoire of functional responses.

A recently proposed neurocognitive framework identified ICNs related to self-, emotion-, and cognitive control processing as neurocognitive "core" networks to study higher cognitive function and dysfunction (Menon and Uddin, 2010; Menon, 2011). In more detail, the anterior and posterior DMN (a/pDMN) covering the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and precuneus consistently activate during self-related and social cognitive functions (Buckner et al., 2008; Andrews-Hanna et al., 2010). The salience network (SN) covers anterior and posterior parts of the insula (AI, PI) and the anterior cingulate cortex (ACC) is critically involved in emotions, pain, and interoception (Seeley et al., 2007; Taylor et al., 2009; Legrain et al., 2011). Finally, left and right lateralized fronto-parietal networks (central executive network, CEN) are robustly associated with cognitive and executive control processes during goal-directed behavior (Seeley et al., 2007; Dosenbach et al., 2008; Habas et al., 2009). The consistent involvement of these three networks does not exclude other areas or networks to be also relevant for these functions particularly in specific contexts. However, it seems that these networks critically contribute (like a "core") to self-, emotion-, and cognitive control-related processes (Menon, 2011), which are impaired in patients with BPD.

Several studies reported aberrant iFC within and across these ICNs in various neuropsychiatric diseases such as major depression (MD) or schizophrenia (Greicius, 2008; Hamilton et al., 2011; Uddin et al., 2011; Manoliu et al., 2013a,b) indicating the largescale brain impact of these diseases on basic intrinsic functional network architecture and associated functions (for review, see also Menon, 2011; Palaniyappan and Liddle, 2012; Hamilton et al., 2013). Due to both the persistent nature of BPD and its "stable instability" in emotion-, self-, and control-related functions, we suggest altered iFC among DMN, SN, and CEN in BPD. In the so far only previous study focusing on iFC in BPD, Wolf et al. (2011) found aberrant (i.e., increased and decreased) iFC within the DMN and CEN of patients with BPD; but this did not yield information about the SN and the intrinsic connectivity across networks. To test our hypothesis about aberrant iFC within and across SN, DMN, and CEN in BPD, we acquired rsfMRI data from patients with BPD and matched healthy controls (HC). We applied data-driven, high-model-order independent component analysis (ICA) to the rs-fMRI data to extract ICNs of coherent ongoing BOLD activity (Calhoun et al., 2001; Allen et al., 2011). We then examined the relationship, i.e., iFC, within (intra-iFC) and between (inter-iFC) ICNs-of-interest and provide a new measure capturing the balance across these neurocognitive networks.

## **MATERIALS AND METHODS**

#### **SUBJECTS**

Fourteen right-handed patients and 16 age-, sex-, and handednessmatched HC participated in the study after signing the informed consent form in accordance with the Human Research Committee guidelines of the Klinikum Rechts der Isar, Technische Universität München (**Table 1**). Patients were recruited from the Department of Psychiatry, Klinikum rechts der Isar, Technische Universität München. Controls were recruited by word-of-mouth advertising from the larger Munich area. Participants' examination included medical history, psychometric assessments [i.e., Beck Depression Inventory (BDI; Beck et al., 1961), Hamilton Depression Scale (HDS; Hamilton, 1960), short version of the Borderline Symptom List (BSL; Bohus et al., 2001), and Global Assessment of Functioning (GAF) Scale (Endicott et al., 1976)] and a structured psychiatric interview for patients only [Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I; First et al., 1996b) and Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II; First et al., 1996a), German version]. All participants were examined by their psychiatrists (Andreas Wöller, Christian Sorg), professionally trained for SCID-based interviews with an inter-rater reliability of more than 95%. Psychiatric diagnoses were based on Diagnostic and Statistical Manual of Mental Disorders-IV (DSM IV).

Patients with BPD constitute a heterogeneous group of patients, who vary in diagnostic subcategories (e.g., with/without feeling of emptiness or stress-related paranoid ideation), comorbidity (e.g., with/without MD or post-traumatic stress disorder, PTSD), and degree of medication (e.g., with/without neuroleptica; Skodol et al., 2002). We adopted selection criteria for a representative group of patients recommended by Skodol et al. (1999) based on a longitudinal examination of 240 patients with BPD. BPD was the primary diagnosis for all patients. We excluded patients with current psychosis, intoxication, or confusional states, with a

#### **Table 1 | Demographics and psychometric scores.**


*Data are presented as mean* ± *SD. HC, healthy controls; BPD, borderline personality disorder; GAF, Global Assessment of Functioning, HDS, Hamilton Depression Scale; BDI, Beck Depression Inventory; BSL, Borderline Symptom List; \*p* < *0.05 (two-sample t-tests).*

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history of schizophrenia, schizoaffective disorder or bipolar disorder but we allowed co-occurrence of Axis I disorders MD or PTSD and psychotropic medication (Skodol et al., 1999). Additional exclusion criteria were an age below 18 or above 60 years, pregnancy, neurological or internal systemic diseases, and general contraindications for MRI assessment. A detailed description of each patient's current comorbidity and medication can be found in **Table 2**. All control subjects were free of any current or past neurological or psychiatric disorder or psychotropic medication.

All participants in this study underwent 10 min of rs-fMRI with the instruction to keep their eyes closed and not to fall asleep. We verified that subjects stayed awake by interrogating via intercom immediately after the rs-fMRI scan. Before and after scanning, a medical examination of patients validated their stable condition and investigated whether they hadfeelings of odd situations during the scanning. No patient dropped out during the scanning session.

### **MRI DATA ACQUISITION**

Magnetic resonance imaging was performed on a 3-T whole body MR scanner (Achieva, Philips, Netherlands) using an eightchannel phased-array head coil. For co-registration of functional data, T1-weighted anatomical data were obtained from each subject by using a magnetization-prepared rapid acquisition gradient echo sequence [time to echo (TE) = 4 ms, repetition time (TR) = 9 ms, time for inversion (TI) = 100 ms, flip angle = 5◦, field of view (FoV) = 240 mm × 240 mm, matrix = 240 × 240, 170 slices, voxel size = 1 mm × 1 mm × 1 mm]. fMRI data were collected using a gradient echo planar imaging (EPI) sequence (TE = 35 ms, TR = 2000 ms, flip angle = 82◦, FoV = 220 mm × 220 mm, matrix = 80 × 80, 32 slices, slice thickness = 4 mm, and 0 mm interslice gap; an fMRI run of 10 min results in 300 volumes).

## **fMRI DATA ANALYSIS**

#### *Preprocessing*

For each participant the first three functional scans of each fMRI-session were discarded due to magnetization effects. SPM5<sup>1</sup> (Wellcome Department of Cognitive Neurology, London) was used for motion correction, spatial normalization into the stereotactic space of the Montreal Neurological Institute (MNI) with resampling of voxel size to 3 mm × 3 mm × 3 mm, and spatial smoothing by applying an 8 mm ×8 mm ×8 mm Gaussian kernel. None of the participants had to be excluded due to excessive head motion (linear shift <3 mm across run and on a frame-to-frame basis, rotation <1.5◦). Two-sample *t*-tests between groups yielded no significant results regarding translational and rotational movements of any direction as well as voxel-wise signal-to-noise ratio of fMRI data calculated with DPARSFA toolbox<sup>2</sup> (*p* < 0.05).

## *Independent component analysis of fMRI data*

Following a recent approach (Manoliu et al., 2013b), we applied high-model-order ICA to the preprocessed data by using the

1http://www.fil.ion.ucl.ac.uk/spm/

<sup>2</sup>http://www.restfmri.net


*BPD, borderline personality disorder; PTSD, post-traumatic stress disorder; MDD, major depressive disorder.*

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Group ICA of fMRI Toolbox (GIFT)-toolbox<sup>3</sup> (version 1.3h) with the infomax algorithm implemented in Matlab (Calhoun et al., 2001). Data were decomposed into 70 spatial independent components (ICs), correspondent with a recently suggested framework for high-model-order decomposition (Abou Elseoud et al., 2011; Allen et al., 2011). High-model-order ICA approaches yield ICs, which are in accordance with large-scale functional networks from low-order approaches but offer a more detailed and particularly robust decomposition of sub-networks (Damoiseaux et al., 2006; Kiviniemi et al., 2009; Smith et al., 2009). Before volumes were entered into ICA analysis, voxel-wise *z*transformation on time course data *yijk*(*t*) was applied by subtracting the mean *yijk* and dividing by the standard deviation σ*ijk* {*y*ˆ*ijk*(*t*) = [*yijk*(*t*) − y*ijk*]/σ*ijk*}, *t* time, *i*,*j*,*k* directions in space; Sorg et al., 2007). The sensitivity of the multivariate ICA algorithm for correlation of variance between voxels, i.e., functional connectivity, was thereby rendered independent of the original BOLD signal magnitude across subjects. Data were concatenated and reduced by two-step principal component analysis (PCA), followed by IC estimation with the infomax algorithm. We subsequently ran 40 ICAs (ICASSO) to ensure stability of the estimated components (Himberg et al., 2004). This results in a set of average group components, which are then back reconstructed into single subject space employing a dual regression analysis (group ICA (GICA) back-reconstruction approach (GICA-3) in GIFT; Erhardt et al., 2011). Each thus reconstructed IC results in a spatial map of *z*-scores reflecting the within-network iFC (intra-iFC) of a voxel within this component and an associated time course of BOLD signal fluctuations representative for this IC. We then reintegrated the initially calculated scaling factor σ*ijk* into the data by voxelwise multiplication in order to preserve each individual's profile of variance magnitude while leaving the normalized time course component unchanged.

## *Network selection*

As previously described (Manoliu et al., 2013b), we ran a multiple spatial regression with a previously established baseline set of functionally relevant ICNs as regressors of interest (Allen et al., 2011) to automatically identify DMN, SN, and CEN in our dataset. From this publication, we selected the posterior (IC 53) and anterior (IC 25) DMN (a/pDMN), left and right lateralized fronto-parietal networks (ICs 34 and 60) reflecting left and right CEN, and an insular network (IC 55) reflecting the SN. The template for the insular network revealed a second component covering PI and bilateral amygdala and hippocampus [which we called posterior SN (pSN) in contrast to the anterior SN (aSN); see also Seeley et al., 2007; Taylor et al., 2009; Legrain et al., 2011]. Due to the importance of insular structures in BPD we also selected this component for further analyses.

#### *Statistical analysis*

To evaluate the spatial consistency of ICNs (intra-iFC), we calculated voxel-wise one-sample *t*-tests on participants' reconstructed spatial maps using SPM5 for each ICN and group (*p* < 0.05, corrected for false discovery rate, FDR). We then examined group differences of intra-iFC. The individual *z*-maps were entered into voxel-wise two-sample *t*-tests and a conjunction map of the one-sample *t*-test image (*p* < 0.001 uncorrected) was applied as a mask to the analysis. In order to control for antipsychotic medication we added chlorpromazine (CPZ)-equivalent doses (Woods, 2003) as covariate-of-no-interest in all imaging analyses. The resulting SPMs were thresholded at *p* < 0.001 (voxel level) and *p* < 0.05 [corrected for family wise error (FWE) at cluster level].

In order to investigate group effects of inter-iFC *between* ICNs, we extracted each subject's IC-timecourse of a/pDMN, l/r CEN, and a/pSN, calculated pairwise Pearson's correlation coefficients between the time course of all ICNs for each subject, transformed the correlation matrix into *z*-values via Fisher *r*-to*z*-transformation and tested differences between the two groups (two-sample *t*-tests with CPZ as covariate-of-no-interest, *p*<0.05, Bonferroni-corrected for 15 pairwise correlations).

## *CEN/SN-inter-iFC index*

Finally, we calculated the ratio (*r*) of overall inter-iFC for SN and CEN within the intrinsic functional architecture of DMN, SN, and CEN for each group controlling for effects of antipsychotic medication (two-sample *t*-test, *p* < 0.05): *r* = interiFCsum(CEN)/inter-iFCsum(SN). Here, the inter-iFCsum reflects the inter-network connectivity of CEN and SN, and is calculated as the summarized absolute *z*-values of each network from the between ICN analysis. This integrated score is motivated by the idea that both SN and CEN interact with the DMN and among each other during emotion regulation, and that they are involved in cognitive control processes (task-positive networks; Seeley et al., 2007) with stronger representation of motivational/emotional aspects by the SN and of attentionrelated aspects by the CEN (Dosenbach et al., 2008; Menon, 2011; Hamilton et al., 2013).

## **RESULTS**

Psychometric assessment revealed significant differences between patients and controls for GAF (two-sample *t*-test, *t* = 17.3, *p* < 0.05), HDS (*t* = −7.1, *p* < 0.05), BDI (*t* = −3.1, *p* < 0.05), and BSL (*t* = −5.8, *p* < 0.05) between the two groups (**Table 1**).

### **INTRA-iFC**

Automated component selection, which was based on spatial templates representing subsystems of the DMN, SN, and CEN (see Figure 4 in Allen et al., 2011 for spatial templates), revealed six IC of interest from high-model-order analysis of fMRI data for each individual. The SN was represented in an anterior and posterior insular network (a/pSN), the DMN in an a/pDMN, and the CEN in left and right (l/r) CEN. Selected components were spatially consistent across groups and matched previous results of SN, DMN, and CEN (Allen et al., 2011; see **Figure 1** and **Table 3** for detailed description of intra-iFC within selected ICNs, *p* < 0.05, FDR-corrected).

Group comparisons of networks' intra-iFC revealed regionally increased intra-iFC in each ICN of patients and decreased intraiFC in only two ICNs (i.e., pSN, lCEN; *p* < 0.05 FWE-corrected cluster level and Bonferroni-corrected for six ICNs; **Figure 2**;

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<sup>3</sup>http://icatb.sourceforge.net

in healthy controls (HC) and patients with borderline personality disorder (BPD). Maps and time courses are derived from independent component represents normalized signal amplitude. First to third row: anterior and posterior (a/p) DMN, anterior and posterior SN, left and right (l/r) CEN.

**Table 4**). Increased intra-iFC in the BPD group covered various brain regions (*midline structures*: ACC, PCC, medial frontal gyrus; *parietal lobe*: bilateral SPL; *insula*: posterior part), decreased intra-iFC occurred in right hippocampus and left superior frontal gyrus.

## **INTER-iFC**

To explore inter-iFC across DMN, SN, and CEN, we calculated the pairwise correlation between network time courses and tested significance of correlations and their potential group differences by using one- and two-sample *t*-tests controlling for effects of medication (CPZ covariate-of-no-interest). In HC,wefound significant inter-iFC for 9 of 15 network pairs, while only four significant

correlations occurred in BPD (*p* < 0.05, Bonferroni-corrected, black lines in **Figure 3A**; **Table 5**). The analysis of group differences revealed specific changes in the intrinsic functional architecture of patients (*p* < 0.05, Bonferroni-corrected for 15 connections; **Table 5**). More specifically, absent inter-network connectivity was found mainly for interactions concerning the CEN where four of six connections significantly decreased. Contrary to this overall decrease of iFC in patients, two additional intrinsic inter-network connections occurred in the patients group for the SN (red lines in **Figure 3A**).

Interestingly, in our correlation analysis of ICA-derived network time courses we found increased connectivity between the r/lCEN and a/pDMN in HC. This finding might be

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### **Table 3 | Spatial intra-iFC maps of DMN, SN, and CEN in controls and patients.**


*(Continued)*

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#### **Table 3 | Continued**


*One-sample t-test (corrected for medication), p* < *0.05 corrected for false discovery rate. HC, healthy controls; BPD, borderline personality disorder; aDMN, pDMN, anterior and posterior default mode network; aSN, pSN, anterior and posterior salience network; lCEN, rCEN, left and right central executive network. Coordinates are presented in MNI standard space.*

counterintuitive, since CEN and DMN are usually found anti-correlated (e.g., Fox et al., 2005). However, our findings for CEN and DMN sub-networks are perfectly in line with those of Allen et al. (2011), suggesting that such sub-networks are positively related among each other. This result might be explained by recent findings of Smith et al. (2012) based on a combination of high-model order spatial and temporal ICA; these authors demonstrated that the DMN can be subdivided into several functionally distinct sub-networks, each with its own characteristic patterns of correlations and anticorrelations with other intrinsic networks.

Finally, the observed global "shift" of inter-iFC among SN and CEN in patients was reflected by an altered CEN/SNinter-iFC index *r* (**Figure 3B**). This ratio reflects the relative intrinsic impact of the CEN in comparison to the SN within the global intrinsic functional architecture of SN, CEN, and DMN. We found a significant difference between *r* (controls) = 1.64 ± 0.80 and *r* (BPD) = 0.99 ± 0.52 with *p* = 0.015 (two-sample *t*-test), potentially indicating a relative shift from

cognitive control to emotion processing in patients with BPD (**Figure 3B**).

## **DISCUSSION**

The aim of this study was to investigate iFC among SN, DMN, and CEN in patients with BPD. This aim was motivated by previous findings demonstrating that interactions within and between these three networks contribute critically to behavior and emotion regulation; impaired emotion/behavior regulation, in turn, is suggested as an essential property of BPD. In a sample of 14 patients, we found aberrant intra-iFC in all three networks. While patients' inter-iFC of the CEN was generally decreased, only inter-iFC of the SN was increased. In particular, a "balance" index reflecting the relationship of CEN- and SN-inter-iFC across networks was strongly shifted from CEN to SN connectivity in patients. This result provides first preliminary evidence for aberrant intrinsic connectivity among the DMN, SN, and CEN in BPD. Data suggest that patients' impaired emotion/behavior regulation may rely on

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**FIGURE 2 | Aberrant intrinsic functional connectivity within DMN, SN, and CEN (intra-iFC) of patients.** SPMs of group differences in intra-iFC for the DMN, SN, and CEN (voxel-wise two-sample *t*-tests) controlled for

antipsychotic medication. SPMs are thresholded at *p* < 0.05, FWE-corrected at cluster level and superimposed on a single subject high resolution T1 image. Color coding (red > yellow) represents *t*-values ranging from 4 to 11.

anomalous iFC among intrinsic networks that is centered on the SN.

## **ABERRANT INTRA-iFC IN SALIENCE, DEFAULT MODE, AND CENTRAL EXECUTIVE NETWORK IN BPD**

In patients, we found increased intra-iFC in the DMN, SN, and CEN with increases covering midline structures such as frontal and parietal cingulate cortices, prefrontal cortices (PFC), parietal lobes, and insular regions (**Figure 2**; **Table 4**). Decreased intraiFC was found in right hippocampi and in the left dorsolateral frontal cortex (**Figure 2**; **Table 4**). Identified group differences were not due to a disintegration of investigated networks in patients, since basic spatial maps of networks were both largely consistent across groups (**Figure 1**; **Table 3**) and in line with previous findings (Damoiseaux et al., 2006; Allen et al., 2011). Patients' counter-intuitively increased and decreased intra-iFC in intrinsic networks particularly in one and the same network (such as lCEN) has been observed also in other neuropsychiatric disorders such as schizophrenia (Manoliu et al., 2013b) or Alzheimer's disease (Zhou et al., 2010) and – in line with our findings – in BPD (for the DMN and CEN; Wolf et al., 2011); however, the functional significance of the direction of intra-iFC changes in brain disorders is still unclear (e.g., iFC decreases are suggested to reflect connectivity disruptions while iFC-increases might reflect compensatory processes; but also a loss of desynchronization and therefore system complexity may play a role; Zhou et al., 2010). Previous imaging studies, which explored the neural correlates of impaired self- or emotion-processing in BPD, revealed aberrant task-related activity in areas similar to those of aberrant intraiFC we found (Minzenberg et al., 2007; King-Casas et al., 2008; Driessen et al., 2009; Koenigsberg et al., 2009a; Smoski et al., 2011; Holtmann et al., 2013). For example, patients with BPD, who had to engage with emotional stimuli, had aberrant levels of activity in ACC, dorsolateral PFC, and amygdala (Minzenberg et al., 2007; Koenigsberg et al., 2009a; Holtmann et al., 2013); the insula was found to be the key region distinguishing BPD patients from HC in a more complex setting of a gambling task (King-Casas et al., 2008); in healthy subjects, self-distancing of negative pictures activates parietal regions overlapping with DMN (Koenigsberg et al., 2009b), while patients with BPD fail to activate the DMN. Furthermore, so far limited literature of resting-state imaging data in BPD supports the spatially widespread pattern of functional changes in BPD. A study using 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) found aberrant brain metabolism in prefrontal and cuneal regions (Juengling et al., 2003). Importantly, the only rs-fMRI study in BPD reported altered intra-iFC of prefrontal, cuneal, and insular regions within the DMN and CEN (Wolf et al., 2011), in line with our results. Taken together, our result demonstrates regionally specific iFC changes within DMN, SN, and CEN, which fit spatially previous findings of aberrant activity during tasks involved in emotion- and self-related processing.

#### **ABERRANT INTER-iFC AMONG DMN, SN, AND CEN IN PATIENTS**

In addition, we found altered inter-iFC among DMN, SN, and CEN in patients (**Figure 3**; **Table 5**). More specifically, we observed an overall decrease of inter-iFC (with only two significant exceptions); this decrease of inter-iFC concerned mainly the CEN while increases were only found in the SN (**Figure 3A**; **Table 5**). The "shift" from a rather evenly spread inter-iFC pattern among the three networks in HC (**Figure 3A**) to a SNcentered pattern in patients (**Figure 3A**) was further indicated

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## **Table 4 | Group differences of intra-iFC maps for DMN, SN, and CEN.**


*Two-sample t-test (corrected for medication), p* < *0.05 corrected for family wise error at cluster level and Bonferroni-corrected for six comparisons; green indicates increased intra-iFC in patients, red reduced intra-iFC. HC, healthy controls; BPD, borderline personality disorder; aDMN, pDMN, anterior and posterior default mode network; aSN, pSN, anterior and posterior salience network; lCEN, rCEN, left and right central executive network. Coordinates are presented in MNI standard space.*

by a strongly reduced CEN-/SN-inter-iFC index (**Figure 3B**). The strong impairment of coordinated activity among these networks appears to be in line with a previous EEG study that found strongly impaired gamma-band synchrony in the parietal lobes of BPD patients during a cognitive control task (Williams et al., 2006). The most prominent cognitive model of BPD suggests that patients have deficits in emotion regulation due to impaired interactions between (pre-)frontal and limbic areas (Skodol et al., 2002; Mauchnik and Schmahl, 2010; Malhi et al., 2013). This is supported by several above-mentioned task-fMRI studies of either emotion processing (Minzenberg et al., 2007; Koenigsberg et al., 2009a) or cognitive control (Driessen et al., 2009; Koenigsberg et al., 2009b; Lang et al., 2012). Since these prefrontal–limbic areas largely overlap with the DMN, CEN, and SN, our results suggest an integrative model of altered intrinsic connectivity between emotion- and cognitive controlrelevant intrinsic networks in BPD, which may be related to prefrontal–limbic regulatory deficits. This model implicates that neither system nor brain region alone is responsible for the various and stable behavioral symptoms in BPD. Future studies combining rs-fMRI and task-fMRI are necessary to test explicitly the relationship between aberrant iFC and emotion-evoked activity in BPD.

## **PARALLELS WITH OTHER NEUROPSYCHIATRIC DISORDERS**

Our result of aberrant iFC among DMN, SN, and CEN is largely consistent with the more general triple network hypothesis of psychopathology (Menon, 2011). This hypothesis states that psychopathological symptoms are associated with specifically altered coordinated activity across SN, DMN, and CEN; particularly, aberrant SN control function of DMN and CEN might underlie specific mental dysfunctions (Palaniyappan and Liddle, 2012). For example patients with schizophrenia with and without psychotic symptoms demonstrate distinctive changes of intra- and inter-iFC in the insular SN that are associated with impaired DMN/CEN interactions and positive and negative symptoms of patients (Manoliu et al., 2013a,b); in depressive patients, rumination is associated with aberrant coordination of intrinsic SN, DMN, and CEN activity (Hamilton et al., 2011). Concerning BPD, our data suggest that impaired behavior/emotion

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*p* < 0.05, Bonferroni-corrected for 15 correlations). Thickness of lines reflects absolute values of Fisher-*z*-normalized correlation coefficients. In patients with BPD, red lines indicate increased inter-iFC compared to healthy controls, while missing lines indicate significantly reduced and absent connections in

CEN was calculated by *r* = inter-iFCsum(CEN)/inter-iFCsum(SN), with inter-iFCsum for CEN and SN, respectively, reflecting summarized absolute *z*-values of inter-iFC. We found significantly reduced *r* in patients (two-sample *t*-test, \*\**p* = 0.007).

regulation might be associated with SN-centered inter-iFC reorganization of triple network functional architecture; however, more explicit evidence for such specific link between network interaction changes and behavioral deficits in BPD is necessary (for more detailed discussion of this point see below "limitations"). Furthermore, in comparing among different disorders one has to pay attention to potential confounding effects of psychotropic medication, which might be used in both compared disorders, e.g., antipsychotics in BPD and schizophrenia. Based on these findings, three basic questions about the specificity of aberrant triple network iFC in BPD arise: how specific are iFC changes for distinct psychopathological symptoms such as emotional response style or impulsivity in BPD? Beyond symptoms, how specific are iFC changes for comparisons with other neuropsychiatric disorders? Beyond triple network, which further brain changes outside the triple network such as subcortical or neurochemical changes are critical for distinct symptoms or differences with other disorders? To disentangle such questions, future studies, which may include different psychiatric disorders and brain measures beyond iFC, are necessary.

## **LIMITATIONS**

First, although comparable with previous studies in BPD, the sample size of our study is small (*n* = 14; e.g.,Koenigsberg et al., 2009a; Wolf et al., 2011; Lang et al., 2012). In general, a small sample size reduces the power of effects, and increases the likelihood of false positive results (Button et al., 2013). Therefore the presented results are preliminary and warrant further replication with higher sample sizes. Second, our patient sample is heterogeneous due to gender, comorbidity, and medication status. This heterogeneity is due to clinically based inclusion criteria, which provided a clinical representative patient sample (Skodol et al., 1999). On the one hand this heterogeneity together with small sample size precluded us to link brain changes with specific behavioral changes; in such groups, the distribution of symptom severity is too heterogeneous to allow for brain–behavior relationship analysis. On the other hand, our results are independent of specific BPD subgroups, suggesting that observed changes of triple network iFC are a general feature of BPD. Nevertheless, studies in more homogeneous sub-groups of BPD might be helpful to specify aberrant network iFC due to BPD sub-groups. Third, patients of the study were therapeutically treated with psychotropic substances

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#### **Table 5 | Inter-iFC between DMN, SN, and CEN.**

*One-sample and two-sample t-tests (\*p* < *0.05 uncorrected, \*\*p* < *0.05, Bonferroni-corrected for 15 tests) including CPZ-equivalent doses as covariate-of-no-interest, for inter-iFC between intrinsic networks in healthy controls and patients with BPD (mean and standard error of Fisher r-to-z-transformed Pearson's correlation coefficient among network time courses). aDMN, pDMN, anterior and posterior default mode network; aSN, pSN, anterior and posterior salience network; lCEN, rCEN, left and right central executive network.*

(**Table 2**). While we did control for antipsychotic medication, we did not control for antidepressant medication because no appropriate numerical procedure (comparable to CPZ conversion) is available for antidepressants. Previously, antidepressant effects on brain activity and functional connectivity have been discussed for the BOLD signal (Miller et al., 2001; Phillips et al., 2008; Heller et al., 2013). Although recent studies suggest that antidepressants normalize brain function (Anand et al., 2005; Fu et al., 2007; Heller et al., 2013), we cannot exclude antidepressant medication effects on our results. Future studies of non-medicated patients are necessary. Forth, some limitations concerning the use of ICA to identify ICNs have to be considered. Our selection of a model order 70 was empirical; although a model order of about 75 components seems to be an optimal choice (Abou-Elseoud et al., 2010), no clear computational or objective criterion for that number is available. Furthermore, the selection of ICNs of interest from ICA-derived components is intricate, particularly due to subjective bias; to account for this problem, we performed maximally controlled spatial regression analysis of all ICs on ICN templates as previously described (Manoliu et al., 2013b), which stem from a previous study using a very similar approach (Allen et al., 2011).

#### **CONCLUSION**

The current study provides evidence for aberrant iFC within and across DMN, SN, and CEN in patients with BPD. Data suggest a "shift" of inter-network iFC from networks of cognitive control to those of emotion-related activity, potentially reflecting the persistent instability of emotion regulation in patients.

## **ACKNOWLEDGMENTS**

This work was supported by the Bayerisches Eliteförderungsgesetz (BayEFG, Anselm Doll), the German Federal Ministry of Education and Research (BMBF 01EV0710 to Afra M. Wohlschläger, BMBF 01ER0803 to Christian Sorg) and the Kommission für Klinische Forschung, Technische Universität München (KKF 8765162 to Christian Sorg). We are grateful to the participants of the study and the staff of the Department of Psychiatry and Neuroradiology for their help in recruitment and data collection.

## **REFERENCES**


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pictures in borderline personality disorder. *Biol. Psychiatry* 72, 448–456. doi: 10.1016/j.biopsych.2012.03.027


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borderline personality disorder. *Personal. Disord.* 2, 230–241. doi: 10.1037/ a0022228


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 29 May 2013; accepted: 12 October 2013; published online: 30 October 2013.*

*Citation: Doll A, Sorg C, Manoliu A, Wöller A, Meng C, Förstl H, Zimmer C, Wohlschläger AM and Riedl V (2013) Shifted intrinsic connectivity of central executive and salience network in borderline personality disorder. Front. Hum. Neurosci. 7:727. doi: 10.3389/fnhum.2013.00727*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Doll, Sorg, Manoliu, Wöller, Meng, Förstl, Zimmer, Wohlschläger and Riedl. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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## Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder

*Andrei Manoliu1,2,3,4\*, Chun Meng2,3,5, Felix Brandl 2,3, Anselm Doll 2,3,5, Masoud Tahmasian2,3, Martin Scherr 1,6, Dirk Schwerthöffer 1, Claus Zimmer 2, Hans Förstl 1, Josef Bäuml 1, Valentin Riedl 2,3,5,7, Afra M. Wohlschläger 2,3 and Christian Sorg1,2,3*

*<sup>1</sup> Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany*

*<sup>2</sup> Department of Neuroradiology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany*

*<sup>3</sup> TUM-Neuroimaging Center, Technische Universität München, Munich, Germany*

*<sup>4</sup> Department of Radiology, University Hospital Zürich, Zürich, Switzerland*

*<sup>5</sup> Munich Center for Neurosciences Brain & Mind, Ludwig-Maximilians-Universität München, Munich, Germany*

*<sup>6</sup> Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University Salzburg, Salzburg, Austria*

*<sup>7</sup> Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Rajeev Krishnadas, University of Glasgow, UK Yuan Zhou, Chinese Academy of Science, China*

#### *\*Correspondence:*

*Andrei Manoliu, Department of Radiology, University Hospital Zürich, Rämistrasse 100, CH 8091 Zürich, Switzerland; Institute for Biomedical Engineering, University and ETH Zürich, Gloriastr. 35, 8092 Zürich, Switzerland e-mail: andrei.manoliu@usz.ch*

Major depressive disorder (MDD) is characterized by altered intrinsic functional connectivity within (intra-iFC) intrinsic connectivity networks (ICNs), such as the Default Mode- (DMN), Salience- (SN) and Central Executive Network (CEN). It has been proposed that aberrant switching between DMN-mediated self-referential and CEN-mediated goal-directed cognitive processes might contribute to MDD, possibly explaining patients' difficulties to disengage the processing of self-focused, often negatively biased thoughts. Recently, it has been shown that the right anterior insula (rAI) within the SN is modulating DMN/CEN interactions. Since structural and functional alterations within the AI have been frequently reported in MDD, we hypothesized that aberrant intra-iFC in the SN's rAI is associated with both aberrant iFC between DMN and CEN (inter-iFC) and severity of symptoms in MDD. Twenty-five patients with MDD and 25 healthy controls were assessed using resting-state fMRI (rs-fMRI) and psychometric examination. High-model-order independent component analysis (ICA) of rs-fMRI data was performed to identify ICNs including DMN, SN, and CEN. Intra-iFC within and inter-iFC between distinct subsystems of the DMN, SN, and CEN were calculated, compared between groups and correlated with the severity of symptoms. Patients with MDD showed (1) decreased intra-iFC within the SN's rAI, (2) decreased inter-iFC between the DMN and CEN, and (3) increased inter-iFC between the SN and DMN. Moreover, decreased intra-iFC in the SN's rAI was associated with severity of symptoms and aberrant DMN/CEN interactions, with the latter losing significance after correction for multiple comparisons. Our results provide evidence for a relationship between aberrant intra-iFC in the salience network's rAI, aberrant DMN/CEN interactions and severity of symptoms, suggesting a link between aberrant salience mapping, abnormal coordination of DMN/CEN based cognitive processes and psychopathology in MDD.

**Keywords: intrinsic functional connectivity, intrinsic networks, central executive network, default mode network, salience network, triple network hypothesis, anterior insula, major depressive disorder**

#### **INTRODUCTION**

Major depressive disorder (MDD) is a severe mental disorder defined by the presence of at least one major depressive episode (MDE), which is primarily characterized by depressed mood, diminished interest, loss of energy, impaired cognition, and suicidal tendency (American Psychiatric Association, 2000). MDEs have been demonstrated to be associated with both structural (Savitz and Drevets, 2009) and functional brain anomalies including aberrant functional connectivity (FC) of remote brain areas' activity (Greicius et al., 2007; Sheline et al., 2010; Northoff et al., 2011). Altered intrinsic FC (iFC, i.e., synchronous ongoing brain activity) has been found in intrinsic connectivity networks (ICNs) particularly in the Default Mode Network (DMN), Salience Network (SN), and Central Executive Network (CEN), suggesting a critical role of these neurocognitive "core" networks (Uddin et al., 2011) in mediating pathophysiological mechanisms in MDD (Menon, 2011; Hamilton et al., 2013).

The DMN comprises mainly the ventromedial prefrontal cortex, the posterior cingulate cortex, bilateral inferior parietal cortex and the middle temporal lobe and is involved in selfreferential/internally oriented processes (Buckner et al., 2008). Within the DMN, aberrant deactivation during goal-directed tasks (Sheline et al., 2009) as well as increased FC during rest [(Greicius et al., 2007), see also Broyd et al. (2009) for review] have been demonstrated in patients with MDD, suggesting that DMN-mediated processes bias patients for increased self-referential thoughts even during external tasks in MDD (Hamilton et al., 2013). The CEN comprises primarily the dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex and is involved in control processes during goaldirected/externally oriented tasks (Fox and Raichle, 2007) and regulation of emotional responses, particularly mediated via the DLPFC (Phillips et al., 2008). Heterogeneous alterations in iFC during both task (Fitzgerald et al., 2008b) and rest (Fitzgerald et al., 2008a; Diener et al., 2012) have frequently been reported in MDD, supporting the assumption of aberrant cognitive regulation of emotional processing in patients with MDD (Pizzagalli et al., 2009). The SN comprises the anterior insular cortex (AI) and dorsal anterior cingulate cortex and is involved in detecting and orienting to both external and internal salient stimuli and events (Seeley et al., 2007). The AI within the SN is critically involved in maintaining and updating representations of current and predictive salience (Singer et al., 2009; Palaniyappan and Liddle, 2012). Particularly, the right AI has been suggested to critically contribute to appropriate behavioral responses to salient stimuli via switching between DMN-related self-referential and CEN-related goal directed cognitive activity (Sridharan et al., 2008). In MDD, increased activation in response to negative stimuli (Strigo et al., 2008) as well as aberrant iFC at rest [see Sliz and Hayley (2012) and Diener et al. (2012) for extensive review] have been reported in the AI, possibly indicating heightened SN response selectively to negative stimuli. Taken together, these findings suggest a reorganization of iFC within DMN, SN and CEN in MDD, potentially contributing to characteristic symptoms in MDD, such as rumination (DMN), emotional overreactivity (SN), and emotional disinhibition (CEN) (Hamilton et al., 2013).

Recent large-scale neurocognitive models of MDD point at the prominent role of aberrant PFC-limbic interactions underlying impaired emotion processing including its regulation in MDD (Mayberg, 1997; Drevets et al., 2008; Disner et al., 2011). In terms of intrinsic networks, these models suggest aberrant interactions among DMN, CEN, and SN, which cover large parts of the prefrontal and limbic brain (Hamilton et al., 2013). Correspondingly, Hamilton and colleagues found that increased dominance of DMN activity over the activity of the task positive network ("TPN," which corresponds in its spatial pattern widely to the CEN) during rest is associated with the severity of self-focused rumination in depressive patients, therefore giving a first hint toward aberrant inter-network interaction and its relevance for symptoms in MDD. In line with this finding, they demonstrated further that aberrant right AI activity in patients appeared to be involved in this network interaction failure: more specifically, increased AI activity was found in patients when CEN activity increased, while in controls increased AI activity was present when DMN activity increased. This result corresponds well with right AI's critical role in modulating interactions between the DMN and CEN in healthy subjects (Sridharan et al., 2008), suggesting maladaptive interaction between right AI and DMN/CEN in patients with MDD. However, direct evidence for such aberrant inter-network interaction centered on the right AI in depression is still missing. Beyond previously reported structural and functional alterations in the AI [(Strigo et al., 2008; Sprengelmeyer et al., 2011); see also (Diener et al., 2012) for review], we hypothesized that rAI's intrinsic connectivity is aberrant and associated with both aberrant iFC between DMN and CEN (inter-iFC) and severity of symptoms in major depression.

The current study aimed to test this hypothesis by addressing the following questions: Is there a relationship between aberrant intra-iFC within and aberrant inter-iFC between intrinsic brain networks (i.e., SN, DMN, CEN) in patients with MDD? Is, as recently proposed (Menon, 2011), aberrant intra-iFC in the right AI within the SN linked to aberrant inter-iFC between the DMN and CEN? And are aberrant intra-iFC and inter-iFC not only linked to selective symptoms, such as rumination, but also to the global severity of symptoms in patients with MDD?

To investigate these questions, we performed resting-state functional magnetic resonance imaging (rs-fMRI) measuring the ongoing blood-oxygenation-level-dependent (BOLD) fluctuations, structural imaging as well as psychometric assessment in 25 patients with MDD and 25 age- and sex matched healthy controls. According to a previously reported approach (Manoliu et al., 2013b), rs-fMRI data were decomposed by highmodel-order independent component analysis (ICA) into spatially independent z-maps of functionally coherent brain areas and corresponding time courses of component activity (Calhoun et al., 2001). From these spatial maps (SM), we selected those representing the DMN, SN, and CEN. Main outcome measures were Pearson's correlation between network time courses, reflecting inter-network intrinsic functional connectivity (inter-iFC) and components' z-maps, reflecting the intra-network intrinsic functional connectivity (intra-iFC). We controlled our analyses for effects of age, sex and structural anomalies.

## **METHODS**

## **PARTICIPANTS**

Twenty-five patients with recurrent MDD and 25 healthy control subjects participated in this study (see **Table 1** for detailed presentation of demographical and clinical characteristics). Participant's data have also been used in a previous study investigating the topology of the brain's connectivity patters in patients with MDD (Meng et al., 2013). All participants provided informed consent in accordance with the Human Research Committee guidelines of the Klinikum rechts der Isar, Technische Universität München. Patients were recruited from the Department of Psychiatry, healthy controls by word-ofmouth advertising. Participants' examination included medical history, psychiatric interview, psychometric assessment, and blood tests for patients. Psychiatric diagnoses were based on DSM IV (American Psychiatric Association, 2000). The Structured Clinical Interview for DSM-IV (SCID-I, Spitzer et al., 1992) was used to assess the presence of psychiatric diagnoses. Severity of clinical symptoms was measured with the Hamilton Rating Scale for Depression (HAM-D, Hamilton, 1960) as well as the Beck Depression Inventory (BDI, Beck et al., 1961). The global level of social, occupational, and psychological functioning was measured with the Global Assessment of Functioning Scale (GAF, Spitzer

**Table 1 | Demographic and clinical characteristics.**


*aTwo-sample t-test.*

*<sup>b</sup>*χ2*-test.*

*\*significant for p < 0.05, Bonferroni-corrected for multiple comparisons.*

*Abbreviations: MDD, major depressive disorder; HC, healthy controls; SD, standard deviation; EHI, Edinburgh handedness inventory; GAF, Global Assessment of Functioning Scale; HAM-D, Hamilton Depression Rating Scale; BDI, Beck Depression Inventory.*

et al., 1992). Psychiatrists Martin Scherr and Dirk Schwerthöffer performed clinical-psychometric assessment and have been professionally trained for SCID interviews with inter-rater reliability for diagnoses and scores of more than 95%.

MDD was the primary diagnosis for all patients. All patients met criteria for a current MDE with an average current episode length of 16.56 weeks (*SD* = 6*.*62), an averaged HAM-D score of 22.12 (*SD* = 7*.*06) and an average BDI score of 24.08 (*SD* = 6*.*31). The average GAF-score was 49.80 (*SD* = 10*.*53). The mean duration of MDD was 16.72 years (*SD* = 10*.*20), the mean number of episodes 5.56 (*SD* = 2*.*47). Fourteen out of twenty-five patients with MDD had a psychiatric co-morbidity, including generalized anxiety disorder (*n* = 6), somatization disorder (*n* = 3), and avoidant or dependent personality disorder (*n* = 5). Patients with psychotic symptoms, schizophrenia, schizoaffective disorder, bipolar disorder, and substance abuse were excluded from this study. Additional exclusion criteria were pregnancy, neurological or severe internal systemic diseases, and general contraindications for MRI. One patient was free of any psychotropic medication during MRI assessment. Seven patients received mono-therapy [including citalopram 30 mg/d (mean dose, *n* = 3), sertraline 200 mg/d (*n* = 3), mirtazapine 30 mg/d (*n* = 1)]. Twelve patients received dual-therapy [including citalopram 37.5 mg/d and mirtazapine 30 mg/d (*n* = 5), citalopram 40 mg/d and venlafaxine 225 mg/d (*n* = 2), citalopram 30 mg/d and quetiapine 200 mg/d (*n* = 1), sertraline 200 mg/d and mirtazapine 30 mg/d (*n* = 1), venlafaxine 225 mg/d and mirtazapine 30 mg/d (*n* = 3)]. Five patients received triple therapy [including citalopram 30 mg/d, venlafaxine 187.5 mg/d and amisulprid 200 mg/d (*n* = 2), citalopram 30 mg/d, mirtazapine 30 mg/d and quetiapine 200 mg/d (*n* = 2), venlafaxine 22 mg/d, mirtazapine 30 mg/d and quetiapine 200 mg/d (*n* = 1)]. All healthy controls were free of any current or past neurological or psychiatric disorder or psychotropic medication and had no family history of affective or psychotic mental disorders in first-degree relatives.

All participants underwent 10 min of rs-fMRI with the instruction to keep their eyes closed and not to fall asleep. We verified that subjects stayed awake and had no odd feelings during the scanning session by interrogating via intercom immediately after the rs-fMRI scan. No patient dropped out during the scanning session.

## **MRI DATA ACQUISITION**

MRI was performed on a 3 T MR scanner (Achieva, Philips, Netherland) using an 8-channel phased-array head coil. For co-registration and volumetric analysis, T1-weighted anatomical data were obtained by using a magnetization-prepared rapid acquisition gradient echo sequence (*TE* = 4 ms, *TR* = 9 ms, *TI* = 100 ms, flip angle = 5◦, FoV = 240 × 240 mm2, matrix = 240 × 240, 170 slices, voxel size = 1 × 1× 1 mm3). FMRI data were obtained by using a gradient echo EPI sequence (*TE* = 35 ms, *TR* = 2000 ms, flip angle = 82◦, FoV = 220 × 220 mm2, matrix = 80 × 80, 32 slices, slice thickness = 4 mm, and 0 mm interslice gap; 300 volumes).

## **fMRI DATA ANALYSIS**

## *Preprocessing*

For rs-fMRI data, SPM8 (Wellcome Department of Cognitive Neurology, London) was used for motion correction, spatial normalization into the stereotactic space of the Montreal Neurological Institute (MNI) and spatial smoothing with a 6 × 6 × 6 mm Gaussian kernel. To control for potential differences in motion between groups and potential bias on function connectivity (Van Dijk et al., 2012), several parameters have been investigated and compared between patients with MDD and healthy controls as reported previously [see (Meng et al., 2013) for extensive presentation of the applied procedures and analyses]. Briefly, excessive head motion (cumulative translation *>* 3 mm and rotation *>* 3◦ as well as mean point-to-point translation *>*0.15 mm or rotation *>* 0*.*1◦) has been applied as exclusion criteria for all participants. Furthermore, two-sample *t*-tests were performed to investigate potential between-group differences in cumulative and/or mean point-to-point motion, both yielding no significant between-group differences, respectively (*p >* 0*.*05). Moreover, signal-to-noise ratio of fMRI data was not different between healthy subjects and patient group (two-sample *t*-test, *p >* 0*.*05).

#### *Independent component analysis*

Independent Component Analysis (ICA) is a computational technique for identifying statistically independent sources from multivariate data and can therefore be used to explore functional connectivity patters in the context of resting-state fMRI (Beckmann, 2012). In contrast to seed-based approaches, ICA analyzes the data in a data-driven way (Calhoun et al., 2001). Therefore, no a-prior assumptions, such as the manual selection of regions-ofinterest (ROIs), is necessary, making ICA a powerful tool to investigate the complete picture of the functional hierarchy within the human brain (Cole et al., 2010), which we aimed to investigate in patients with MDD and healthy controls in the present study. As recently proposed by Allen et al. (2011) and previously reported (Manoliu et al., 2013b), preprocessed data were decomposed into 75 spatial independent components within a group-ICA framework (Calhoun et al., 2001), based on the infomax-algorithm and implemented in the GIFT-software (http://icatb*.*sourceforge*.* net). High-model-order ICA approaches yield independent components, which are in accordance with known anatomical and functional segmentations (Allen et al., 2011). FMRI data were concatenated and reduced by two-step principal component analysis, followed by independent component estimation with the infomax-algorithm. We subsequently ran 20 ICA (ICASSO) to ensure stability of the estimated components. This results in a set of average group components, which are then back reconstructed into single subject space via GICA3, a back-reconstruction algorithm based on PCA compression and projection [see (Allen et al., 2011) for detailed discussion of advantages of the GICA3 algorithm]. Each back-reconstructed component consists of a spatial z-map reflecting component's functional connectivity pattern across space (intra-iFC) and an associated time course reflecting component's activity across time.

### *Selection of model-order and networks-of-interest*

Although ICA-based analyses of rs-fMRI data are often reported, the selection of the optimal ICA model-order to analyze rs-fMRI data is still a subject of ongoing debate [see Manoliu et al. (2013b) as well as Manoliu et al. (2013a) for detailed discussion]. However, it has been demonstrated that a model-order around 70 components may represent an optimal level to detect between-group differences and to avoid false positive results (Abou-Elseoud et al., 2010). Bearing this in mind and exactly following a recently proposed approach of Allen et al. (2011), we decomposed our data into 75 independent components. The congruence with Allen's approach enables greater comparability of results across studies and reduced subjective bias for ICN selection. In more detail, Allen and colleagues used an ICA model-order of 75 to decompose rs-fMRI data of 603 subjects within a group-ICA framework based on the infomaxalgorithm and implemented in the GIFT-software (http://icatb*.* sourceforge*.*net) (Calhoun et al., 2001). Authors provided T-maps of 28 components, which reflect canonical ICNs online (http://mialab.mrn.org/data/hcp/RSN\_HC\_unthresholded\_tmap s.nii; Allen et al., 2011). To select components, which reflect networks-of-interest, in an automated and objective way, we chose from these T-maps those representing subsystems of the SN, DMN, and CEN (7 of 28 maps, see **Figure 1**), and performed multiple spatial regression analyses of our 75 independent components' spatial maps on these templates. We selected components of highest correlation coefficient with the templates, resulting in 7 ICNs of interest: 1 component reflecting the SN, 3 reflecting subsystems of the DMN or CEN, respectively. In the end, this approach yielded for each subject and ICN a component's z-map and time course, which reflect network's coherent activity.

#### *Outcome measures and statistical analysis*

*Intra-iFC.* To statistically evaluate intra-iFC of selected ICs, we calculated voxel-wise one-sample *t*-tests on participants' reconstructed spatial maps for each group, using SPM8 [*p <* 0*.*05, family-wise-error (FWE)-corrected for multiple comparisons]. To analyze group differences, participants' spatial maps were entered into two-sample *t*-tests with age, sex, and total gray matter (GM) volumes within the area covered by the 7 networks-ofinterest [see section Voxel-Based Morphometry (VBM) Analysis for detailed presentation of calculation of total gray matter] as covariate-of-no-interest (*p <* 0*.*05 FWE-corrected).

*Inter-iFC.* To statistically evaluate inter-iFC, subject specific ICN time courses (TCs) were detrended, despiked, filtered using a fifth-order Butterworth low-pass filter with a high frequency cutoff of 0.15 Hz, and pairwise correlated by Pearson's correlation, following the approach of Jafri et al. (2008) as reported in Manoliu et al. (2013b). To assess group differences, correlation coefficients were transformed to z-scores using Fisher's z-transformation and entered into two-sample *t*-tests with age, sex, and total GM volumes of the areas covered by the 7 networks-of-interest [see section Voxel-Based Morphometry (VBM) Analysis for details regarding the calculation of total gray matter] as covariate-of-no-interest (*p <* 0*.*05, Bonferronicorrected for multiple comparisons).

*Correlation analyses.* Insula dysfunction has been suggested to be associated with the severity of symptoms in patients with MDD (Menon, 2011; Hamilton et al., 2013). Accordingly, the total scores of both HAM-D and BDI were selected for further correlation analyses. To evaluate relationships between insula network connectivity (SN's insular intra-iFC) and both between-network interactions (inter-iFCs) and severity of symptoms in patients, we first calculated voxel-wise one-sample *t*-test on patients' reconstructed intra-iFC maps for the SN and masked the result with a mask derived from the two-sample-*t*-test contrasting patients from healthy controls. Subsequently, we extracted principle eigenvariates of left and right AI within patient's masked SN spatial map, respectively. Then we used eigenvariate-scores for partial correlation analyses of Fisher-z-transformed inter-iFC scores and both HAM-D scores and BDI scores, respectively, including age, sex, and total GM within the brain areas covered by the 7 networks-of-interest as covariates of no interest [see section Voxel-Based Morphometry (VBM) Analysis for detailed description of the calculation of total gray matter]. To study the relationship between inter-iFCs and severity of depressive symptoms in patients, we used Fisher-z-transformed inter-iFC scores for partial correlation analyses of both HAM-D scores and BDI scores, respectively, including age, sex, and total GM within the brain areas covered by the 7 networks-of-interest as covariates of no interest. Results of partial correlation analyses were thresholded at *p <* 0*.*05, Bonferroni-corrected for multiple comparisons.

#### **VOXEL-BASED MORPHOMETRY (VBM) ANALYSIS**

The VBM analysis followed the description provided in Meng et al. (2013). The functional connectivity of intrinsic brain networks depends on widespread structural integrity of polysynaptic pathways (Lu et al., 2011). Since we focus on alterations of functional interactions among 7 distinct networks, we included total GM scores of the brain areas covered by the 7 networksof-interest as covariate-of-no-interest in above-mentioned FC

provided online (http://mialab.mrn.org/data/hcp/RSN\_HC\_unthresholded\_ tmaps.nii). As previously reported (Manoliu et al., 2013b), we chose the T-maps of ICs representing the default mode network, salience network and central executive network, and performed multiple spatial regression analyses of our 75 independent components' spatial maps on these

single-subject high resolution T1 image (color scale representing *t*-values mode network (spDMN); **(D)** salience network (SN); **(E)** left ventral central executive network (lvCEN); **(F)** right ventral executive network (rvCEN); **(G)** dorsal central executive network (dCEN). Modified from Manoliu et al., 2013a.

analyses to control for the influence of structural variations. As described recently (Sorg et al., 2013), we used the VBM8 toolbox (http://dbm*.*neuro*.*uni-jena*.*de/vbm*.*html) to analyze brain structure. T1-weighted images were corrected for bias-field inhomogeneity, registered using linear (12-parameter affine) and non-linear transformations, and tissue-classified into gray matter (GM), white matter (WM), and cerebro-spinal fluid (CSF) within the same generative model (Ashburner and Friston, 2005). The resulting GM images were modulated to account for volume changes resulting from the normalization process. Here, we only considered non-linear volume changes so that further analyses did not have to account for differences in head size. Finally images were smoothed with a Gaussian kernel of 8 mm (FWHM). Since we were interested specifically in investigating aberrant iFC within and between 7 distinct ICNs, we constructed a binarized mask representing all 7 ICNs of interest and extracted the total GM scores within this mask for each group following the procedure reported in Manoliu et al. (2013b). Subsequently, we used these scores as covariate of no interest in all further analyses of both intra-iFC and inter-iFC within and between the 7 ICNs of interest. Furthermore, between group comparisons have been reported in a previous study (Meng et al., 2013).

#### **RESULTS**

### **INTRINSIC CONNECTIVITY NETWORKS: INTRA- AND INTER-iFC**

Regarding both intra-iFC and inter-iFC, current results matched almost perfectly reported findings of Allen et al. (2011) and previously reported studies investigating the iFC within and between the DMN, SN, and CEN (Manoliu et al., 2013b), demonstrating the presence of the basic functional architecture of the DMN, SN, and CEN in both investigated groups (see **Figure 1** for presentation of spatial templates, **Figure 2** and **Table 2** for detailed presentation of intra-iFC within ICNs of interest and **Figure 3**, and **Table 4** for detailed presentation of inter-iFC between ICNs of interest).

## *Intra-iFC*

As previously described (Manoliu et al., 2013b), automated component selection, which was based on spatial templates representing subsystems of the DMN, SN, and CEN, revealed 7 components of interest for each participant [see **Figure 1** for presentation of spatial templates according to Allen et al. (2011)]: The SN was represented in one component (SN, corresponding with Allen-IC 55). The DMN was represented in 3 components [anterior DMN (aDMN, corresponding with Allen-IC 25), inferior posterior DMN (ipDMN, corresponding with Allen-IC 53), superior posterior DMN (spDMN, corresponding to Allen-IC 50)]. The CEN was represented in 3 components [left ventral CEN (lvCEN, corresponding to Allen IC 34), right ventral CEN (rvCEN, corresponding to Allen-IC 60), dorsal CEN (dCEN, corresponding to Allen-IC 52)]. All selected components were spatially consistent across groups and matched previous results of SN, DMN, and CEN (Allen et al., 2011) (see **Figure 2** and **Table 3** for detailed description of intra-iFC within selected ICNs, *p <* 0*.*05, FWE-corrected).

T1 image (color scale representing *t*-values from 5 to 25; only maps of healthy controls are shown). (2) To analyze between-group differences, patients' and controls' ICs of the DMN, SN, and CEN were entered into voxel-wise two-sample-*t*-test with age, sex, and GM volume of the brain

*Inter-iFC*

Calculated inter-iFC between ICNs of interest matched results of Allen et al. (2011), (see **Figure 3** and **Table 4** for detailed presentation of inter-iFC between all ICNs of interest). In accordance with previous findings (Manoliu et al., 2013b), we found positive correlations between subsystems of the DMN and CEN in both groups. Despite the incongruity with previously described patterns of anti-correlation between the DMN and CEN (Fox and Raichle, 2007), it is well in line with findings presented in recent studies using high-model order ICA (Allen et al., 2011).

salience network (SN); **(E)** left ventral central executive network (lvCEN); **(F)** right ventral executive network (rvCEN); **(G)** dorsal central executive network (dCEN). Abbreviations: MDD, group of patients with major depressive disorder; HC, healthy control group (see also **Tables 2**, **3**).

**Table 2 | Intrinsic connectivity networks in healthy controls.**


*\*One-sample-t-test, significant for p < 0.05, FWE-corrected for multiple comparisons, cluster-threshold >10 voxel.*

*aMNI, Montreal Neurological institute; L, left hemisphere; R, right hemisphere; Bi, bilateral (see Figure 2).*

In particular, Smith et al. (2012) demonstrated by applying high temporal resolution resting-state fMRI that distinct subnetworks within the DMN are associated with distinctive pattern of between-network connectivity, possibly underlying the constantly shown heterogeneous connectivity pattern between subsystems of the DMN and CEN.

## **INTRA-iFC OF THE SN IS DISRUPTED IN BILATERAL ANTERIOR INSULA IN PATIENTS WITH MAJOR DEPRESSIVE DISORDER**

Compared to healthy controls, patients demonstrated altered intra-iFC within the DMN, SN, and CEN. (**Figure 2** and **Table 3**; *p <* 0*.*05 FWE-corrected with age, sex, and total GM of the brain areas covered by the 7 networks of interest as covariates of no-interest). Regarding the SN, patients showed decreased intra-iFC within the bilateral AI. Furthermore, intra-iFC was increased in bilateral ACC within the SN (see **Figure 2D**). Regarding the DMN, patients showed increased intra-iFC in bilateral ACC within the aDMN (see **Figure 2A**), increased intra-iFC within the in the bilateral precuneus within the ipDMN (see **Figure 2B**) as well as both increased and decreased intra-iFC in distinct parts of the precuneus within the spDMN (see **Figure 2C**).

Regarding the CEN, patients showed heterogeneous alterations, including increased intra-iFC in the right angular gyrus and decreased intra-iFC in both the left precuneus and left middle temporal gyrus within the lvCEN (see **Figure 2E**) as well as increased intra-iFC in the right postcentral gyrus within the dCEN (see **Figure 2G**). No between-group differences were observed within the rvCEN (see **Figure 2F**).

## **INTER-iFC BETWEEN DMN AND CEN IS DECREASED IN PATIENTS WITH MAJOR DEPRESSIVE DISORDER**

Compared to healthy controls, patients with major depressive disorder showed both increased and decreased inter-iFC (see **Figure 4**, **Table 4**; *p <* 0*.*05, corrected for age, sex and GM volume of the brain areas covered by the 7 networks of interest, Bonferroni-corrected for multiple comparisons). Patients showed decreased inter-iFC between ipDMN and dCEN as well as between spDMN and dCEN, suggesting a decreased functional connectivity between the DMN and CEN. Furthermore, patients showed increased inter-iFC between SN and ipDMN, indicating increased functional connectivity between the SN and DMN.

## **RIGHT ANTERIOR INSULA'S ABERRANT SN CONNECTIVITY IS ASSOCIATED WITH SEVERITY OF SYMPTOMS IN PATIENTS WITH MAJOR DEPRESSIVE DISORDER**

To study the influence of insular SN activity on the severity of symptoms in patients with MDD, we correlated eigenvariates of SN's left and right AI group difference clusters with both HAM-D and BDI total scores (**Figure 5**, **Table 5**; *p <* 0*.*05, partial correlations with age, sex, and GM of the brain areas covered by the 7 networks of interest as covariates of no-interest, Bonferronicorrected for multiple comparisons). In patients, SN's right AI's intra-iFC correlated negatively with the severity of symptoms as measured by both HAM-D (*r* = −0*.*554, *p* = 0*.*008) and BDI (*r* = −0*.*556, *p* = 0*.*007), suggesting an association between aberrant connectivity within the right anterior insular cortex and the severity of symptoms in patients with MDD. The altered intra-iFC within the left AI did not show any significant correlation with total scores assessed by HAM-D and BDI, respectively.

## **RIGHT ANTERIOR INSULA'S ABERRANT SN CONNECTIVITY IS ASSOCIATED WITH ALTERED DMN-CEN INTERACTION IN PATIENTS WITH MAJOR DEPRESSIVE DISORDER**

To study the influence of insular SN activity on altered internetwork connectivity in patients, we correlated eigenvariates of SN's left and right AI group difference clusters with Fisherz-transformed correlation coefficients of each pair of network time courses (**Figure 5**, **Table 6**, *p <* 0*.*05, partial correlations with age, sex, and GM of the brain areas covered by the 7 networks of interest as covariates of no-interest, Bonferronicorrected for multiple comparisons). In patients, SN's right AI's intra-iFC correlated positively with inter-iFC between ipDMN and dCEN (*r* = 0*.*472, *p* = 0*.*026) as well as between spDMN and dCEN (spDMN—dCEN: *r* = 0*.*541, *p* = 0*.*009). Furthermore, the SN's right AI's intra-iFC correlated positively with the aberrant inter-iFC between the rvCEN and dCEN (*r* = 0*.*605, *p* = 0*.*003). These results suggest a relation between altered connectivity within the right anterior insular cortex and aberrant inter-network connectivity in patients with MDD. However, it is to note that these associations were not significant when corrected for multiple comparisons (*n* = 21). Furthermore, the altered intra-iFC in the left AI within the SN did not show any significant correlation to inter-network connectivity.

**Table 3 | Altered intra-iFC in patients with major depressive disorder compared to healthy controls.**


*\*Two-sample-t-test with age, sex, and total GM volume within brain areas covered by the 7 ICNs of interest as covariates of no-interest, significant for p < 0.05, FWE-corrected for multiple comparisons. cluster-threshold > 10 voxel.*

*aMNI, Montreal Neurological institute; L, left hemisphere; R, right hemisphere, Bi, bilateral (see Figure 2).*

## **ALTERED INTER-NETWORK CONNECTIVITY WAS NOT ASSOCIATED WITH THE SEVERITY OF SYMPTOMS**

To study the relationship between inter-network connectivity and severity of symptoms, we correlated inter-iFC scores with total scores as assessed with both HAM-D and BDI, respectively (**Figure 5**, **Table 7**; *p <* 0*.*05, partial correlations with age, sex and GM of the brain areas covered by the 7 networks of interest as covariates of no-interest, Bonferroni-corrected for multiple comparisons). There was no association between inter-network connectivity and severity of symptoms in patients with major depressive disorder.

## **DISCUSSION**

To investigate the relationship between anterior insular dysfunction within the SN, altered between-network interaction, and severity of symptoms in MDD, we analyzed intrinsic functional


**Table 4 | Inter-network intrinsic functional connectivity in patients with major depressive disorder and healthy controls.**

*aTwo-sample t-test controlled for age, sex and total GM volume within brain areas covered by the 7 ICNs of interest.*

*Italics indicate p < 0.05; \*significant for p < 0.05, Bonferroni-corrected for multiple comparisons (n* = *21).*

*Abbreviations: MDD, group of patients with major depressive disorder; HC, healthy control group; inter-iFC, inter-network intrinsic functional connectivity; a/ip/spDMN, anterior/inferior-posterior/superior-posterior DMN; lv/rv/dCEN, left-ventral/right-ventral/dorsal CEN; SN, salience network (see also Figures 3, 4).*

connectivity within (intra-iFC) and between (inter-iFC) the DMN, SN, and CEN by the use of resting-state fMRI and ICA in patients with MDD and healthy controls. We found aberrant intra-iFC in DMN, SN and CEN, including decreased intraiFC in the right AI within the SN in patients. Furthermore, we found decreased inter-iFC between subsystems of the DMN and CEN as well as increased inter-iFC between the SN and DMN. Remarkably, decreased intra-iFC in the right AI within the SN correlated significantly with the severity of symptoms. Furthermore, we found a correlation between the decreased intra-iFC in the SN's rAI and aberrant inter-iFC between subsystems of the DMN and CEN in patients with major depressive disorder but significance did not survive correction for multiple comparisons. These results extend our knowledge about aberrant iFC within intrinsic networks in MDD by revealing altered iFC across networks, more specifically the link between right AI dysfunction within the SN, aberrant betweennetwork interaction and severity of symptoms in patients with MDD. Together with previously reported findings (Hamilton et al., 2011) and in accordance with recently suggested models (Menon, 2011; Hamilton et al., 2013), our data suggest aberrant insular control of DMN-CEN interactions, potentially contributing to depressive negative bias in attention and thought in MDD.

## **THE LINK BETWEEN ANTERIOR INSULAR DYSFUNCTION WITHIN THE SN AND ABERRANT INTERACTIONS BETWEEN DMN AND CEN IN MDD**

In bilateral anterior insula, intra-iFC was decreased within the SN, while SN's inter-iFC with the ipDMN was increased. Furthermore, we found decreased inter-iFC between the ipDMN and dCEN, indicating both insular dysfunction and altered connectivity between subsystems of the DMN and CEN in patients with MDD. In addition, rAI's decreased intra-iFC correlated both negatively with the severity of symptoms, suggesting a link between insular dysfunction and severity of symptoms in major depression, and positively with the decreased connectivity between ipDMN and dCEN, suggesting a link between rAI dysfunction and aberrant DMN/CEN interactions. However, it is to note that the latter correlation lost significance after correction for multiple comparisons (*n* = 21). All these results were controlled for effects of age, sex and total GM of the brain areas covered by the networks of interest. Thus, it is unlikely that these possible confounders explain the reported results.

These findings support the hypothesis that rAI dysfunction might be associated with abnormal interactions between DMN and CEN in MDD, likely via impaired AI-mediated control of network interactions (Menon, 2011). Previous findings support this assumption: (1) The rAI has been demonstrated to play a pivot role in modulating interactions between DMN and CEN

**FIGURE 4 | Between-group differences of inter-network intrinsic functional connectivity.** Based on network time courses, inter-network intrinsic functional connectivity (inter-iFC) was calculated by the use of Pearson's correlation between subject specific ICN timecourses (TCs). The red arrows indicates increased inter-iFC in patients with major depressive disorder compared to healthy controls (two-sample *t*-test, *p <* 0*.*05, Bonferroni-corrected for multiple comparisons); The blue arrows indicates decreased inter-iFC in patients with major depressive disorder compared to healthy controls (two-sample *t*-test, *p <* 0*.*05, Bonferroni-corrected for multiple comparisons). Spatial maps indicate the anterior/inferior-posterior/superior-posterior default mode network (a/ip/spDMN), left-ventral/right-ventral/dorsal central executive network (lv/rv/dCEN), and salience network (SN). All tests were corrected for age, sex, and GM volume of the brain areas covered by the 7 networks of

interest. Abbreviations: MDD, group of patients with major depressive

disorder; HC, healthy control group (see also **Table 4**).

in healthy controls (Sridharan et al., 2008) and to show aberrant activity at the onset of increases in DMN and CEN activity, while aberrant relationship between the DMN and CEN was associated with severity of rumination in patients with MDD (Hamilton et al., 2011). Moreover, a recent meta-analysis performed by Diener et al. (2012) demonstrated, that the right AI consistently showed hypoactivity during affective switching and cognitive control tasks in MDD patients. (2) In patients with MDD, functional anomalies within the AI rank among the most frequently reported findings in the current literature (Diener et al., 2012; Sliz and Hayley, 2012; Hamilton et al., 2013). (3) More generally, recently formulated models propose that insular dysfunction and its consecutive abnormal modulation of interactions between networks (i.e., aberrant engagement and disengagement of the DMN and CEN due to aberrant AI mediated network switching) contribute to several neuropsychiatric disorders via aberrant mapping and detection of salient external stimuli and internal events (Menon and Uddin, 2010), potentially manifesting in

**FIGURE 5 | Intra-iFC in the right anterior insula within the salience network is associated with severity of symptoms and inter-iFC between DMN and CEN in patients with major depressive disorder.** Intrinsic functional connectivity (inter-iFC) between ICNs of interest was calculated by the use of Pearson's correlation between networks' time courses. **(A)** Intra-iFC in the right anterior insula within the SN (turquoise spatial map) was significantly correlated with severity of negative symptoms in patients with major depressive disorder as measured by both HAM-D (partial correlation, *r* = −0*.*554, *p* = 0*.*008) and BDI (partial correlation, *r* = −0*.*556, *p* = 0*.*007). **(B)** Intra-iFC in the right anterior insula within the SN was significantly correlated with the inter-iFC between DMN and CEN in patients (turquoise arrows, partial correlations, ipDMN—dCEN: *r* = 0*.*472, *p* = 0*.*026; spDMN—dCEN: *r* = 0*.*541, *p* = 0*.*009). Furthermore, intra-iFC in the right anterior insula within the SN was significantly correlated with the inter-iFC between distinct subsystems of the CEN (rvCEN—dCEN: *r* = 0*.*605, *p* = 0*.*003). All partial correlations were corrected for age, sex, and GM volume of the brain areas covered by the 7 networks of interest. Spatial maps indicate the anterior/inferior-posterior/superior-posterior default mode network (a/ip/spDMN), left-ventral/right-ventral/dorsal central executive network (lv/rv/dCEN), and salience network (SN) (see also **Tables 5**–**7**).

specific symptom dimensions by specific AI/SN changes (Menon, 2011; Uddin et al., 2011; Palaniyappan and Liddle, 2012). For example we found recently that aberrant intra-iFC within the rAI is related to aberrant interaction between DMN and CEN in psychotic patients with schizophrenia (Manoliu et al., 2013b). Taken together, our data suggest a direct association between **Table 5 | Partial correlations between intra-iFC in the right/left AI within the SN and severity of symptoms in patients with major depressive disorder.**


*Italics indicate p < 0.05; \*significant for p < 0.05, after Bonferroni-correction for multiple comparisons (n* = *2).*

*Partial correlation, corrected for age, sex and total GM volume within brain areas covered by the 7 ICNs of interest.*

*Abbreviations: AI, anterior Insula; HAM-D, Hamilton Depression Rating Scale; BDI, Beck Depression Inventory (see Figure 5).*

## **Table 6 | Partial correlations between intra-iFC in the right/left AI within the SN and inter-iFC in patients with major depressive disorder.**


*Italics indicate p < 0.05.*

*Partial correlation, corrected for age, sex and total GM volume within brain areas covered by the 7 ICNs of interest.*

*Abbreviations: a/ip/spDMN, anterior/inferior posterior/superior posterior DMN; lv/rv/dCEN, left ventral/right ventral/dorsal CEN; SN, salience network; AI, anterior insula (see Figure 5).*

insular dysfunction, severity of symptoms and aberrant internetwork interactions via impaired insular control in patients with MDD.

Two further comments concerning laterality and specificity of our results might be useful: (i) Laterality: It is to note **Table 7 | Partial correlations between inter-iFC and severity of symptoms in patients with major depressive disorder.**


*Italics indicate p < 0.05.*

*Partial correlation, corrected for age, sex and GM volume within brain areas covered by the 7 ICNs of interest.*

*Abbreviations: HAM-D, Hamilton Depression Rating Scale; BDI, Beck Depression Inventory; a/ip/spDMN, anterior/inferior posterior/superior posterior DMN; lv/rv/dCEN, left ventral/right ventral/dorsal CEN; SN, salience network.*

that although both right and left AI showed decreased intraiFC within the SN, only patients' right AI showed significant correlation with the severity of symptoms while the left AI displayed no results. Furthermore, the decreased intra-iFC in the right AI within the SN correlated with the decreased connectivity between the DMN and CEN, whereas it is to note, however, that this correlation lost significance after performing correction for multiple comparisons (*n* = 21). This finding is consistent with previous observations: in healthy controls, the right AI modulates selectively inter-network interactions (Sridharan et al., 2008), in patients with MDD only the right AI is characterized by aberrant activity at the onset of DMN/CEN activity (Hamilton et al., 2011). This insular asymmetry might relate with the asymmetric representation of afferent sympathetic nervous system activity in the insula and the fact that interoceptive feelings are predominantly associated with the right AI (Craig, 2002, 2009). Therefore, our data might indicate a potential link between aberrant rAI control processes, sympathetic activity and interoception in patients with MDD. (ii) Specificity: Since increasing evidence for the relevance of right AI dysfunction is emerging in various neuropsychiatric disorders, Menon (2011) suggested, that anomalies within the right AI might contribute to aberrant inter-network interactions, leading to various symptom dimensions such as affective and psychotic symptoms via distinct disease-specific pathways. It is evident that presented results support this notion. However, it is still unclear how the triple network model of anomalies among DMN, SN, and CEN might be linked to anomalies beyond these networks (Williamson and Allman, 2012), such as aberrant reinforcement prediction error (Gradin et al., 2011), striatal reward processing (Robinson et al., 2012) and connectivity (Meng et al., 2013), or aberrant medial temporal lobe activity and connectivity (Tahmasian et al., 2013) in patients with MDD. Further studies investigating the possible link between aberrant DMN/SN/CEN organization and subcortical functional and/or neurochemical (Tahmasian et al., 2013) alterations in patients with MDD are necessary to better understand both the pathophysiology of MDD in particular and the nature of AI's involvement in psychiatric disorders in general.

## **DMN/CEN INTERACTIONS IN PATIENTS WITH MDD** *Intra-iFC within the DMN in patients with MDD*

In patients with MDD, we found selectively increased intra-iFC within both aDMN and ipDMN as well as mainly increased intra-iFC within the spDMN. Furthermore, we found a trend to increased connectivity between aDMN and spDMN (*p* = 0*.*003) that, however, lost significance after correction for multiple comparisons (*n* = 21). Our findings are well in line with current literature reporting predominantly increased FC within the DMN during rest in patients with MDD (Greicius et al., 2007; Broyd et al., 2009; Posner et al., 2013). In particular, increased intra-iFC in the subgenual anterior cingulate and ventromedial prefrontal cortex of the DMN seems to be a highly robust finding in patients with depression (Greicius et al., 2007; Horn et al., 2010; Sheline et al., 2010; Veer et al., 2010). Considering that patients with MDD display less activation of the DMN in response to both positive and negative external stimuli [see Hamilton et al. (2013) for review], it has been proposed that the pattern of increased connectivity during rest and decreased activation during task might indicate that self-related cognition might be more susceptible to internal generated thoughts than to external stimuli in patients with MDD (Hamilton et al., 2013). Taken together, our results confirm previously reported findings, extending them by demonstrating that distinct subsystems of the DMN are consistently characterized by abnormal intra-iFC in patients with MDD.

## *Intra-iFC within the CEN in patients with MDD*

In the current study, we found heterogeneous alterations in intra-iFC within the three sub-components of the CEN, including both increased intra-iFC in the right angular gyrus and decreased intra-iFC in the middle temporal gyrus and precuneus within the rvCEN. These findings are well in line with previous studies, which reported aberrant activity within right angular gyrus, middle temporal gyrus and precuneus in patients with MDD (Fitzgerald et al., 2008b). In general, the CEN, which is involved in goal-directed cognitively demanding tasks and control of emotional responses, has been shown to be altered in several psychiatric disorders, including MDD (Fitzgerald et al., 2008a; Pizzagalli et al., 2009; Menon, 2011; Diener et al., 2012). Although the CEN comprises both frontal and parietal regions, most studies investigated primarily the DLPFC, mainly due to the suggested link between functional anomalies within the DLPFC and impaired cognitive emotion regulation in MDD (Fitzgerald et al., 2008b). Several studies found heterogeneous results regarding both direction of effect (Diener et al., 2012) and exact localization (Hamilton et al., 2013) of aberrant iFC, possibly indicating that different findings might be located in different sub-networks of the CEN, each maintaining distinct tonic (resting-state) or phasic (affective response) cognitive processes (Hamilton et al., 2013). Our results confirm previous findings and support this notion that heterogeneously distributed anomalies of iFC are present within distinct subsystems of the CEN in MDD.

## *Inter-iFC between the DMN and CEN in patients with MDD*

In the current study, we found decreased inter-iFC between the ipDMN and dCEN as well as between the spDMN and dCEN, indicating a decreased connectivity between DMN and CEN in patients with MDD. These results are well in line with previous findings (Sheline et al., 2010; Alexopoulos et al., 2013). Particularly Hamilton and colleagues found that increased dominance of the DMN was related to the severity of ruminations. It is to note that whereas the significant between-group differences regarding the inter-iFC between the ipDMN and dCEN are based on differences in height of negative correlations between the ipDMN and dCEN in both groups (see also **Figures 3**, **4** and **Table 4**), the group differences regarding the inter-iFC between spDMN and dCEN are based on the fact that timecourses between spDMN and dCEN are negatively correlated in patients with MDD and positively correlated in healthy controls. However, positive correlation between spDMN and dCEN in healthy controls corresponds with previous results (Allen et al., 2011) and has also been found in an independent group of healthy controls (see Manoliu et al., 2013b) using the same methodological approach as presented in the current study. As discussed previously (Manoliu et al., 2013a) positive correlation between subsystems of the DMN and CEN seems unexpected and in contrast to the notion of anti-correlation between DMN and CEN (Fox and Raichle, 2007). However, studies using high model order ICA (e.g., Allen et al., 2011) demonstrated that both DMN and CEN consist of functional sub-networks, while Smith et al. (2012) demonstrated by applying high temporal resolution resting-state fMRI that distinct sub-networks within the DMN are characterized by specific connectivity patterns between themselves and other networks. The current data suggests a partial re-organization of these inter-iFC patterns, particularly between subsystems of the DMN and CEN in patients with MDD.

Taken together, our findings replicate previously reported results about aberrant network connectivity in MDD, demonstrating the representative nature of our study sample. Moreover, we extend the current knowledge by linking aberrant intra-iFC in the AI within the SN with both aberrant inter-iFC between the DMN and CEN and severity of symptoms in patients with MDD.

## **PSYCHOPATHOLOGICAL IMPLICATIONS OF ALTERED LARGE-SCALE BRAIN NETWORKS IN MDD**

According to Beck's cognitive theory of depression (Beck, 1967; Beck and Alford, 2009), negative cognitive biases lead to a generally negative view of oneself and the world, thus underlying depression (Mathews and MacLeod, 2005; Willner et al., 2013). In particular, activation of negative schemata that are mainly associated with activity in areas of the SN and subcortical regions such as the amygdala or the striatum are suggested to bias attention, processing, and memory (for review Disner et al., 2011). For example, increased synchronous activity between the striatum and the anterior cingulate cortex is thought to increase both the activity within the mPFC and to decrease the activity within the DLPFC, being associated with biased thinking and memory, leading to depressive symptoms such as rumination. Since these regions are key regions of SN, DMN, and CEN, a link between neurocognitive models of negative bias and aberrant large-scale intrinsic network interactions arises. We suggest an essential relationship between the negative bias model of Beck and the frame of interacting SN, DMN, and CEN (Menon, 2011; Hamilton et al., 2013). In more detail, in the frame of interacting networks [i.e., the triple network model of Menon (2011)] the DMN is involved in selfreferential processes (Buckner et al., 2008), while the CEN is involved in goal-directed processes (Fox and Raichle, 2007). The right AI within the SN has been demonstrated to mediate the switching between DMN-based self-referential and CEN-based goal-directed processes (Sridharan et al., 2008). Therefore, anterior insular dysfunction within the SN might contribute to an abnormal switching between DMN and CEN, thus leading to an abnormal behavioral response to both internal and external stimuli and events (Menon and Uddin, 2010). While increased activity in the DMN has been demonstrated to be related with depressive ruminations (Hamilton et al., 2013), abnormal engagement and disengagement of DMN and CEN might underlie difficulties to disengage the processing of negative information, thus negatively biasing attention and cognitive processing. This bias, in turn, might contribute to the worsening of symptoms in patients with MDD. This argument suggests a link between modern large-scale network theories and traditional cognitive theories in MDD, possibly providing a valuable contribution to the better understanding of the neurobiology of MDD. However, several limitations regarding this model have to be considered. We mention only two of them: (i) how are findings beyond the DMN, SN and CEN linked to this model (Williamson and Allman, 2012)? For instance, we found recently that the functional connectivity between both amygdala and hippocampus into the AI and dorsomedial PFC (overlapping with CEN and SN) is consistently disrupted in major depression, potentially constituting a pathway to modulate aberrantly large-scale networks (Tahmasian et al., 2013). (ii) How are DMN, SN, and CEN linked to potential neurochemical anomalies, such as aberrant availability of striatal dopamine in MDD (Nestler and Carlezon, 2006)? For example disrupted reward learning in major depression is associated with the altered reward prediction error activity in the putamen, which again depends on dopaminergic input (Robinson et al., 2012); putamen activity and reward learning are critically linked with the SN (Kapur, 2003), however, it is unknown how these processes relate with the regulation of large-scale networks. Further studies are necessary to test these questions.

## **LIMITATIONS**

We acknowledge several limitations of the current study. (1) Independent Component Analysis. Although often performed, ICA is still associated with methodological constraints, such as arbitrary selection of the model order and subjective bias in identification of the components of interest (Cole et al., 2010). First, our selection of model order was empirical. While model order of around 75 components appears to be optimal for network stability (Abou-Elseoud et al., 2010), computational or objective criteria are still missing. Second, visual selection of networksof-interest from ICA-derived components has some pitfalls due to subjective bias. To circumvent this problem, we run automated spatial regression analysis of all ICs on network templates from a previous study using the exactly same analysis approach based on a large sample of 603 healthy subjects (Allen et al., 2011). Third, although our results of inter-network connectivity match previous literature, the nature of the interaction within and between different intrinsic connectivity networks or their subsystems is not yet fully understood. For example, Smith et al. (2012), using high-temporal resolution rs-fMRI, discovered remarkable temporal dynamic within intrinsic networks, which is incompletely addressed by our measure of inter-iFC. (2) Causality and inter-iFC between SN and DMN: While we found significantly aberrant inter-iFC between SN and DMN, we did not provide direct evidence for the direction—or stronger causality—of this aberrant interaction. Even when previous studies do suggest a controlling effect from the AI of the SN onto the DMN, our data do not provide direct evidence for this direction of effect. To overcome this missing evidence, the use of Granger causality (GC) analysis (which is a form of time-lagged correlation analysis for BOLD time series) might be a candidate; however, there is still ongoing debate whether BOLD-based GC is a valid method to detect causality among neural processes underlying BOLD signals [for detailed discussion see Manoliu et al. (2013b)]. However, several methods have recently been proposed to overcome the potential restrictions of previous variants of GC analysis (Ryali et al., 2011; Tang et al., 2012). For example Ryali and colleagues proposed a multivariate dynamical systems model (MDS) approach, in which they use the frame of probabilistic graphs to estimate dynamic interactions among regions (Ryali et al., 2011). Definitely, future studies, which apply such advanced methods, are necessary to specify directions of aberrant inter-iFC of the salience network in major depression. (3) Structural anomalies. Since structural anomalies have been shown to have an influence on FC (e.g., Lu et al., 2011), we used the total GM volume extracted from a mask covering the DMN, SN, and CEN as a covariate of no-interest in all statistical analyses of FC. Furthermore, we correlated the GM values derived from the mask covering all 7 ICNs of interest with both interiFC between all networks and intra-iFC in the left and right AI within the SN, finding no significant results. However, it is important to note that correcting for linear covariates or investigating potential linear correlations does not exclude the presence of non-linear effects possibly associated with structural changes. Moreover, the effect of structural anomalies on FC is still subject of current research and not yet fully understood. Further studies are necessary to investigate the relationship between structural anomalies and functional connectivity in both healthy participants and patients with MDD. (4) Medication. Antidepressant medication has been demonstrated to have an impact on intrinsic functional connectivity (Delaveau et al., 2011). However, 24 out of 25 patients were medicated at the timepoint of scanning. Although our results are widely consistent with the current literature, the presented data should be interpreted cautiously until replicated in an unmedicated patient sample. (5) Co-morbidities. We would like to point out that although MDD was the primary diagnosis for our patient sample, 14 out of 25 patients with MDD were diagnosed with psychiatric co-morbidities, including generalized anxiety disorder, somatization disorder and avoidant or dependent personality disorder. However, since we aimed to investigate the relationship between intra-iFC, inter-iFC and severity of symptoms in MDD, which is a heterogeneous mental disorder including a high variance in duration of the disorder, number of episodes, family history of MDD and psychiatric co-morbidities, we followed the selection criteria reported in Hennings et al. (2009) to obtain a clinically representative sample, thus including the patients with aforesaid co-morbidities. (6) Although resting-state fMRI is applied frequently throughout the literature to explore possible differences in the brain's functional architecture of patients with mental disorders compared to healthy controls, it is still unclear if the reported alterations might have been at least partially induced by psychological-behavioral differences during the scan. For example, patients might display a higher level of arousal and/or anxiety that, in turn, might have an influence on the ongoing cognitive processes during the scan. In the current study, we investigated whether the patients had experiences of odd feelings during the scan. However, we missed to explicitly measure these possible confounds on a psychometrical or physiological level (e.g., via electrodermatoactivity). Therefore, the possibility that different levels of arousal and/or anxiety might have had an influence on the presented results cannot be excluded.

#### **CONCLUSION**

The current study provides evidence that aberrant connectivity in the right anterior insula (rAI) within the salience network is associated with the severity of symptoms and aberrant interactions between DMN and CEN in MDD. Data suggest a link between anterior insular dysfunction, altered inter-network connectivity, and severity of symptoms in patients with MDD.

#### **ACKNOWLEDGMENTS**

This work was supported by the German Federal Ministry of Education and Research (BMBF 01EV0710 to Afra M. Wohlschläger, BMBF 01ER0803 to Christian Sorg) and the Kommission für Klinische Forschung, Technischen Universität München (KKF 8765162 to Christian Sorg). We are grateful to the participants of the study and the staff of the Department of Psychiatry and Neuroradiology for their help in recruitment

and data collection. The authors have declared that there are no conflicts of interest in relation to the subject of this study.

## **REFERENCES**


BOLD signals with LASSO. *PLoS Comput. Biol.* 8:e1002513. doi: 10.1371/journal.pcbi.1002513


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 29 May 2013; accepted: 22 December 2013; published online: 21 January 2014.*

*Citation: Manoliu A, Meng C, Brandl F, Doll A, Tahmasian M, Scherr M, Schwerthöffer D, Zimmer C, Förstl H, Bäuml J, Riedl V, Wohlschläger AM and Sorg C (2014) Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder. Front. Hum. Neurosci. 7:930. doi: 10.3389/fnhum.2013.00930*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2014 Manoliu, Meng, Brandl, Doll, Tahmasian, Scherr, Schwerthöffer, Zimmer, Förstl, Bäuml, Riedl, Wohlschläger and Sorg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Disparity between dorsal and ventral networks in patients with obsessive-compulsive disorder: evidence revealed by graph theoretical analysis based on cortical thickness from MRI

## *Seung-Goo Kim1,2, Wi Hoon Jung3, Sung Nyun Kim4, Joon Hwan Jang4 and Jun Soo Kwon1,3,4\**

*<sup>1</sup> Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea*

*<sup>2</sup> Research Group for Cortical Networks and Cognitive Functions, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany*

*<sup>3</sup> Clinical Cognitive Neuroscience Center, SNU-MRC, Seoul National University Hospital, Seoul, South Korea*

*<sup>4</sup> Department of Psychiatry, College of Medicine, Seoul National University, Seoul, South Korea*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Rik Vandenberghe, Katholieke Universiteit Leuven, Belgium Zhang Chen, University of Alberta, Canada*

#### *\*Correspondence:*

*Jun Soo Kwon, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 110-744, South Korea e-mail: kwonjs@snu.ac.kr*

As one of the most widely accepted neuroanatomical models on obsessive-compulsive disorder (OCD), it has been hypothesized that imbalance between an excitatory direct (ventral) pathway and an inhibitory indirect (dorsal) pathway in cortico-striato-thalamic circuit underlies the emergence of OCD. Here we examine the structural network in drug-free patients with OCD in terms of graph theoretical measures for the first time. We used a measure called efficiency which quantifies how a node transfers information efficiently. To construct brain networks, cortical thickness was automatically estimated using T1-weighted magnetic resonance imaging. We found that the network of the OCD patients was as efficient as that of healthy controls so that the both networks were in the small-world regime. More importantly, however, disparity between the dorsal and the ventral networks in the OCD patients was found in terms of graph theoretical measures, suggesting a positive evidence to the imbalance theory on the underlying pathophysiology of OCD.

**Keywords: obsessive-compulsive disorder, magnetic resonance imaging, cortical thickness, structural connectivity, graph theoretical analysis, network efficiency, small-worldness, dorsal-ventral imbalance**

## **1. INTRODUCTION**

Obsessive-compulsive disorder (OCD) is an anxiety disorder characterized by intrusive, distressing thoughts and ritualistic, repetitive behaviors (American Psychiatric Association, 1994). The most widely accepted neuroanatomical model of OCD has suggested the involvement of a direct and an indirect corticostriato-thalamic (CST) pathway (Cummings, 1993; Saxena et al., 1998). In this model, the direct pathway involves in an excitatory input to the internal part of globus pallidus that leads to a disinhibition of thalamus and increased excitation of prefrontal cortex, whereas the indirect pathway involves in an inhibitory input to the external part of globus pallidus that evokes an increased inhibition of thalamus and decreased excitation of prefrontal cortex (Mataix-Cols and van den Heuvel, 2006). Although the oversimplification of the model is questioned (Menzies et al., 2008; Milad and Rauch, 2011), the dichroism has been a well-known basis in approaching the disorder for a long time (Saxena et al., 1998).

Functional studies driven by the CST model have converged on altered activation in patients with OCD in relation to healthy controls in basal ganglia, caudate nucleus, thalamus, orbital frontal cortex, cingulate gyrus, dorsal lateral cortex and parietal regions, using single-photon emission computed tomography (SPECT), positron emission tomography (PET) or functional magnetic resonance imaging (fMRI) (Whiteside et al., 2004; Friedlander and Desrocher, 2006). Despite of the inconsistencies in the literature to some degree, many papers reported higher activations from OCD patients in orbital frontal cortex and basal ganglia, in particular striatum, which have been related to the "hyperactivation of ventral fronto-strital system". In addition, lower activations from OCD patients in dorsal lateral prefrontal cortex and anterior cingulate gyrus, which have been known as the "hypoactivation of dorsal fronto-strital system" (Saxena et al., 1998; Remijnse et al., 2005; Oh et al., 2012; Mataix-Cols and van den Heuvel, 2006).

In relation to the functional findings supporting the CST hypothesis, structural neuroimaging evidences of the abnormalities in OCD patients have been cumulated. Structural alterations were mainly localized in prefrontal regions and basal ganglia. In meta-analyses, gray matter densities in bilateral anterior putamina were found to be higher in OCD patients than healthy controls, and those in dorsal prefrontal regions were found to be lower in OCD patients (Radua and Mataix-Cols, 2009; Rotge et al., 2010). In addition, diffusion tensor imaging (DTI) studies have shown that smaller fractional anisotropy (FA), which has been commonly used to characterize local diffusion and thus to infer white matter integrity (Basser, 1995), were found in clinical population with OCD in the anterior part of cingulum, corpus callosum and other white matter regions in frontal and parietal lobes (Szeszko et al., 2005; Garibotto et al., 2009; Ha et al., 2009; Bora et al., 2011; Koch et al., 2012; Nakamae et al., 2011; Oh et al., 2012).

It should be noted that the previous structural studies mentioned above have mainly focused on differences in local morphology using massive univariate frameworks such as voxel-based morphometry (VBM; Ashburner and Friston, 2000) or tractbased spatial statistics (TBSS; Smith et al., 2006). Due to its complex nature of brain network, local alterations might not be sufficient to understand the disorder. As needs for investigating connectivities within the CST circuits and between other brain regions have been motivated in the previous literature on OCD (Remijnse et al., 2005; Mataix-Cols and van den Heuvel, 2006; Menzies et al., 2008), a seed-based correlation method (Harrison et al., 2009; Jang et al., 2010) and a whole-brain graph analysis have been used in functional studies (Zhang et al., 2011).

While it is demonstrated that network analyses are capable of investigating the properties of human brains that could not be described using the conventional analyses (Bullmore and Sporns, 2009), relatively new neuroimaging modalities such as DTI and resting-fMRI have been mainly used for the brain connectivity studies. In order to exploit conventional anatomical scans such as T1-weighted MRI, a network analysis based on the correlation of local morphology has been proposed as an alternative framework to examine structural networks in human brains (Worsley et al., 2004; Lerch and Evans, 2005; He et al., 2007; Bernhardt et al., 2011).

In the studies, gray matter is characterized by cortical thickness in a sub-millimeter resolution using cortical surface reconstruction techniques (Dale et al., 1999; Fischl and Dale, 2000; MacDonald et al., 2000). This approach, which only requires T1-weighted MRI, assumes that the positive correlation of cortical thickness may reflect the anatomical connectivity, presumably because of common experiences, shared trophic or maturational influences (Lerch et al., 2008; He et al., 2009; Raznahan et al., 2011). These networks constructed based on cortical thickness have shown their resemblance to DTI-based networks (Gong et al., 2012) and similar modular structures with known functional modules (Chen et al., 2008, 2011). More recently, the cortical thickness-based network in developing brains notably overlapped a functional network known as default-mode-network (DMN; Raznahan et al., 2011).

Here we apply the cortical thickness network analysis on patients with OCD. To our best knowledge, there has been no preceding study to examine graph theoretical measures of brain networks in patients with OCD based on the correlation of cortical thickness so far. As the cortical thickness network analysis has shown its ability to detect reliable and meaningful attributes of human brains in healthy population (He et al., 2007; Chen et al., 2008, 2011; Gong et al., 2009, 2012) and clinical populations with disorders such as multiple sclerosis (He et al., 2009), Alzheimer's disease (He et al., 2008) or temporal lobe epilepsy (Bernhardt et al., 2011), we expect to find alterations in the brain of patients with OCD in terms of network properties, in particular, with a supporting evidence for the dorsal-ventral imbalance in the CST circuits (Saxena et al., 1998; Mataix-Cols and van den Heuvel, 2006), as well as abnormalities in other circuits including dorsal anterior cingulate cortex (Milad and Rauch, 2011) and parietal cortex (Menzies et al., 2008).

Our main contributions include: (1) performing a cortical thickness network analysis on drug-free patients with OCD, (2) investigating graph theoretical measures in the perspective of the major hypothesis of OCD at a network-level (Latora and Marchiori, 2001) and a node-level (Achard and Bullmore, 2007), and finally (3) examining the pathophysiology of OCD in terms of disparity between dorsal and ventral networks, as recently shown as a spatial bias in FA alteration within corpus callosum in OCD patients (Oh et al., 2012).

## **2. MATERIALS AND METHODS**

## **2.1. PARTICIPANTS**

We recruited 32 patients who fulfilled the criteria for OCD in DSM-IV (American Psychiatric Association, 1994) *via* the OCD clinic at Seoul National University Hospital (Seoul, Korea). The patients were diagnosed using the Structured Clinical Interview for DSM-IV (SCID; First et al., 1996). All of the patients with OCD were drug-free: 23 patients were drug-naïve, and the other 9 patients were unmedicated for at least 4 weeks at the time of inclusion. Four patients were assessed to have personality disorders in addition to OCD: three were with obsessivecompulsive personality disorders and one was with schizotypal personality disorder. In addition to the patients, we also recruited 35 age- and gender-matched controls (HC) using the SCID Non-patient Version to confirm that none of the controls was with Axis I psychiatric disorders. The exclusion criteria for both patients and control included lifetime history of psychosis, bipolar disorder, major depressive disorder, substance abuse or dependence, significant head injury, seizure disorder or mental retardation. All participants were right-handed. The severity of depression and anxiety was measured by selfreporting Beck's Depression Inventory (BDI; Beck et al., 1961) and Beck's Anxiety Inventory (BAI; Beck et al., 1988), respectively. The severity of OC symptoms was assessed with clinicianadministered Yale-Brown Obsessive-Compulsive Scale (Y-BOCS; Goodman et al., 1989). The institutional review board (IRB) of Seoul National University Hospital (H-1209-025-424) approved the present study. All participants were fully instructed about the procedures of scanning and assessment and then submitted written informed consents.

## **2.2. IMAGE ACQUISITION AND GRAPH CONSTRUCTION**

We obtained magnetic resonance imaging (MRI) using 1.5T MAGNETOM Avanto syngo scanner (Siemens, Erlangen, Germany). T1-weighted 3D images were acquired with the following parameters: TR = 1160 ms, TE = 4.76 ms, FOV = 230 mm, flip angle = 15◦, voxel size: 0*.*45 × 0*.*45 × 0*.*90 mm, volume dimension: 350 × 263 × 350 mm.

The steps of image analysis are illustrated in **Figure 1**. To compare brain networks between the patients and the controls at the final stage of analysis, we estimated cortical thicknesses from MRIs and constructed brain networks based on them. The detailed steps of the present analysis are explained in the followings. The analysis was carried by custom MATLAB (Mathworks Inc., Natick, MA, USA) codes, if not otherwise specified.

## *2.2.1. Cortical thickness estimation*

The reconstruction of cortical surfaces and the estimation of cortical thickness were performed using FreeSurfer1 . As in its standard pipeline (Dale et al., 1999), the intensity of T1-weighted images were normalized and the bias of B0 field was corrected. Then the images were resampled in a unit millimeter isovoxel. An inner cortical surface (the interface between white matter and gray matter) and an outer cortical surface (the interface between gray matter and cerebrospinal fluid) were modeled as triangular tessellation. The cortical thickness was computed by averaging distances from the inner surface to outer surface and the distance from the outer surface to the inner surface (Fischl and Dale, 2000).

## *2.2.2. Spatial normalization and resampling on a template surface*

The estimated cortical surfaces were spatially normalized onto a given template surface, called *"fsaverage6"* with 40962 vertices for each hemisphere, using curvature matching technique to align major sulci patterns (Fischl et al., 1999). Then the cortical thickness was resampled onto the template surface, resulting in the correspondence of measures across all participants. This normalization enables a direct comparison of a vertex or a set of vertices across participants.

#### *2.2.3. Heat kernel smoothing* **via** *Laplace-Beltrami eigenfunction*

Individual cortical thickness maps on the template surface were smoothed using a heat kernel smoothing technique based on obtain adjacency matrices at a certain rewiring cost (**E**, section 2.2.5).

Laplace-Beltrami (LB) eigenfunctions (Seo et al., 2010; Kim et al., 2011b; Seo and Chung, 2011). The surface-based smoothing reduces the impact of possible abrupt noise or errors from MRI scanning, surface reconstruction and thickness estimation, thus increases statistical power (Chung et al., 2005; Lerch and Evans, 2005). In addition, due to its analytic formulation, the heat kernel smoothing *via* LB eigenfunctions has a benefit of circumventing numerical errors in conventional smoothing techniques based on iterations. Theoretical details are explained in somewhere else (Seo et al., 2010). In this paper, we used 4000 orthonormal bases of LB eigenfunctions. The measurements were smoothly recovered with the bandwidth parameter σ of 10 mm, using freely available MATLAB codes by Moo K. Chung<sup>2</sup> .

## *2.2.4. Partial correlation between ROIs*

Automatic parcellations of gray matter into 74 regions-of-interest (ROIs) per hemisphere were adapted from (Fischl et al., 2004; Destrieux et al., 2010), which was included in FreeSurfer as *"Destrieux 2009 atlas"*. Although the 148 ROIs are less uniform in terms of area (mean area = 13*.*73 ± 9*.*68 cm2) than in a high-resolution parcellation with about 1000 ROIs (mean area ∼1.5 cm2 with standard deviation less than 0.15 cm2) used in Hagmann et al. (2007, 2008), the anatomical significance of the current parcellations assists us in interpreting results while reducing the computational loads in permutation tests as described in

<sup>1</sup>http://surfer.nmr.mgh.harvard.edu

<sup>2</sup>http://brainimaging*.*waisman*.*wisc*.*edu/~chung/lb/

the section 2.4. Thickness measures were averaged in each ROI and used in further analysis.

We computed partial correlation between the ROIs while factoring out the effect of age and gender, as well as the mean of measures, as previous cortical thickness network studies (He et al., 2007, 2008, 2009; Bernhardt et al., 2011). First we fit such a general linear model (GLM) as

$$\mathbf{c}(\mathbf{x}) = \beta\_0 + \beta\_1 \mathbf{a} + \beta\_2 \mathbf{g} + \varepsilon,\tag{1}$$

where **c***(x)* is the vector of the cortical thickness of the *x*-th ROI for individual participants, β*<sup>k</sup>* <sup>=</sup> <sup>0</sup>*,* <sup>1</sup>*,* <sup>2</sup> are unknown parameters to estimate, **a** is the vector of ages, **g** is the vector of genders and  is the vector of Gaussian random noise. Once we estimated the parameters with the least square method, the residuals **<sup>c</sup>***(x)* <sup>−</sup>**c***(x)* of the GLM were used to compute a correlation matrix **R** = *rxy* ∈ R148×<sup>148</sup> as

$$r\_{\mathfrak{x}\mathfrak{y}} = \text{corr}(\mathfrak{c}(\mathfrak{x}) - \widehat{\mathfrak{c}}(\mathfrak{x}), \mathfrak{c}(\mathfrak{y}) - \widehat{\mathfrak{c}}(\mathfrak{y})), \tag{2}$$

corr*(***i***,***j***)* is the Pearson product of two vectors **i** and **j** as

$$\text{corr}(\mathbf{i}, \mathbf{j}) = \frac{\sum \mathbf{i}\mathbf{j} - \left[\left(\sum \mathbf{i} \sum \mathbf{j}\right) / n\right]}{\left[\mathbf{i}^2 - \left(\sum \mathbf{i}\right)^2 / n\right] \left[\mathbf{j}^2 - \left(\sum \mathbf{j}\right)^2 / n\right]},\tag{3}$$

where *n* is the number of the elements of **i** or **j**. Since we are interested in the anatomical connectivity due to neuronal associations under the same assumptions in the previous network studies based on cortical thickness (He et al., 2007, 2009; Bernhardt et al., 2011), we do not examine anti-correlations in this paper. Although negative association should also be studied in the future to reveal the biological mechanism of the cortical thickness interdependence (Lerch et al., 2008; Raznahan et al., 2011), it would be beyond the scope of the current study.

## *2.2.5. Network construction for different rewiring costs*

For it is known that the *rewiring costs*, or the density, of a network critically affects the graph theoretical properties and topological characteristics of network (Eguíluz et al., 2003; Latora and Marchiori, 2003; Achard et al., 2006; Gong et al., 2009), we controlled the costs to compare the brain networks of the OCD patients with that of the controls. When an undirected and unweighted graph *G* is written as a set of two sets as

$$\mathcal{G} = \{\mathcal{V}, \mathcal{E}\}, \tag{4}$$

where *V* is the set of vertices and *E* is the set of edges, then the cost of a graph *G* is given as

$$\text{cost}(\mathcal{G}) = \frac{K}{N(N-1)/2},\tag{5}$$

where *K* = |*E*| as the number of edges and *N* = |*V*| as the number of vertices in the graph *G*. Note that *N(N* − 1*)/*2 is the largest number of possible *K*. Thus the cost equals to zero when there is no connections and the cost equals to one when every node is directly connected to all the other nodes. We binarized the correlation matrices so that they have the equivalent cost, ranging from 0.01 to 0.50 with a step of 0.01. It resulted in 100 (50 costs × 2 groups) adjacency matrices **A***g, <sup>c</sup>* with the dimensionality of 148 × 148, where *g* is the group index (*g* = 1 for the controls; *g* = 2 for the OCD patients) and *c* is the cost (*c* = 0*.*01, 0.02,*...* , 0.50). The denser graphs with the cost of more than 0.50 are indistinguishable between the groups and even from the theoretical models (random and lattice), thus we did not include the range over 0.50 in our study. One might note that the selected range of cost is slightly wider than in some previous studies: 0*.*05 ≤ *c* ≤ 0*.*40 (Bernhardt et al., 2011), 0*.*06 ≤ *c* ≤ 0*.*40 (He et al., 2008), but narrower than in another study: 0 *< c <* 1 (He et al., 2009). However, determining a threshold for binary graph analysis is no trivial issue, and even selecting multiple thresholds also introduces empirical choices (Langer et al., 2013). It should be noted that using too high threshold (i.e., low cost) has a risk of excluding true connections (false negative) and too low threshold (i.e., high cost) has a risk of including false connections (false positive).

#### **2.3. GRAPH MEASURES: EFFICIENCY AT NETWORK AND NODE LEVELS**

In order to characterize the properties of cortical thickness networks, we used *efficiency* in this paper, which measures how efficiently a network exchanges information (Latora and Marchiori, 2001). The efficiency measure is given in two ways: (Latora and Marchiori, 2001): *global efficiency* and *local efficiency*, which are closely related to the *small-worldness* measures such as *characteristic path length* and *clustering coefficients* (Watts and Strogatz, 1998). In contrast to the small-worldness measures are defined only in a network with only one connected component, the efficiency measures are more adoptable for the real-world networks as they are also applicable to disconnected networks.

In addition to the originally proposed network-wise measures for efficiency (Latora and Marchiori, 2001), a node-wise measure has been used in the previous brain connectivity literature (Achard and Bullmore, 2007; He et al., 2009; Wang et al., 2009b; Lo et al., 2010), but only limited to the global efficiency. Combining two levels (network- and node-) and two efficiency measures (global and local), we used four different types of efficiency measures in the paper, as explained in the following subsections.

For the assessment of real-world networks from human brains, we generated cost-matched theoretical networks. The theoretical networks provide benchmarks for a network with a maximal global efficiency [i.e., a random network for unweighted graphs; Latora and Marchiori (2003)] or a network with a high local efficiency (i.e., a regular lattice) under a given constraint of cost. A "economic behavior" of network, or small-worldness, is often used to describe a network with a low characteristic path length, or a high global efficiency, as a random network and a high clustering coefficient, or a high local efficiency, as a regular lattice (Latora and Marchiori, 2003). For each level of cost, 1000 random networks with uniform probability of connections and 1000 lattice networks with the fixed patterns of adjacent connections were synthesized, then the graph measures were averaged over instances. The efficiency measures in the followings were computed using a custom modification of the MATLAB code in Brain Connectivity Toolbox<sup>3</sup> .

## *2.3.1. Global efficiency*

*Global efficiency* of a graph *G* is given (Latora and Marchiori, 2001) as

$$E\_{\text{glob}}(\mathcal{G}) = \frac{1}{(N-1)N} \sum\_{i \in \mathcal{G}} \sum\_{j \in \mathcal{G}} \frac{1}{d\_{ij}},\tag{6}$$

where *N* is the number of nodes in the graph *G*, the nodes *i* and *j* within *G* are different, (*i* = *j*) and *dij* is the shortest path length, or geodesic distance (Newman, 2003), between the two nodes. One may note that *E*glob*(G)* quantifies the expectation on how closely a node is connected to all the other nodes in the whole network. It has a clear relation to a graph theoretical measure previously known as *characteristic path length* (Watts and Strogatz, 1998). While the characteristic path length is the arithmetic mean of the shortest path lengths, the *E*glob is the reciprocal of the harmonic mean of the shortest path lengths (Latora and Marchiori, 2003). The *E*glob is bounded from 0 to 1. When there are no connections between any nodes, all geodesic distances are equal to infinity then the *E*glob equals to zero. On the other hand, when the all nodes are directly connected, all distances are equal to one and the *E*glob also equals to one.

#### *2.3.2. Local efficiency*

*Local efficiency* of a network *G* is given as the average of the global efficiencies of sub-graphs (Latora and Marchiori, 2001) as

$$E\_{\text{loc}}(\mathcal{G}) = \frac{1}{N} \sum\_{i \in \mathcal{G}} E\_{\text{glob}}(\mathcal{G}\_i) \tag{7}$$

where *G<sup>i</sup>* is a sub-graph centering the node *i*, that is, the set of the node *i* and its neighbors (the nodes with the distance of a single edge from the node *i*) and the set of edges between the nodes. As the measure depicts connectivity within local neighbors, Latora and Marchiori (2003) have shown that the *E*loc*(G)* is related to *clustering coefficient* (Watts and Strogatz, 1998). As the *E*loc*(G)* is the average of *E*glob*(Gi)*, the measure is also bounded from 0 to 1. The higher the measure, the more efficiently the nodes within a local network are interconnected.

#### *2.3.3. Nodal efficiency*

Besides of network-level, we can measure how efficiently an individual node transfer information at node-level as

$$E\_{\text{nodal}}(i; \mathcal{G}) = \frac{1}{N - 1} \sum\_{j \in \mathcal{G}} \frac{1}{d\_{\vec{\eta}}},\tag{8}$$

where *j* = *i*. *E*nodal*(i*; *G)* is known as *nodal efficiency* (Achard and Bullmore, 2007). When the global efficiency *E*glob*(G)* can be understood as "the global efficiency of a network", we can regard the nodal efficiency *E*nodal*(i*; *G)* as "the global efficiency of a node". Remind that the term "global" or "local" only indicates whether the efficiency measure is computed for the interconnections to the all nodes, i.e., global network, or whether it is for the intra-connections within the neighboring nodes, i.e., local network (Latora and Marchiori, 2003).

## *2.3.4. Neighboring efficiency*

The efficiency within the local neighbors of a node is computed as

$$E\_{\rm nbr}(i; \mathcal{G}) = E\_{\rm global}(\mathcal{G}\_i) = \frac{1}{(N\_i - 1)N\_i} \sum\_{m \in \mathcal{G}\_i} \sum\_{n \in \mathcal{G}\_i} \frac{1}{d\_{mn}} \tag{9}$$

where *Ni* is the number of nodes in a sub-graph *G<sup>i</sup>* and the nodes *m* and *n* in *G<sup>i</sup>* are different (*m* = *n*). We call this measure *E*nbr*(i*;*G)* as *neighboring efficiency*, which is a node-level measure of local efficiency, as well as the nodal efficiency *E*nodal*(i*;*G)* is a node-level measure of global efficiency. By definition, *E*nbr*(i*;*G)* is given when *Ni* ≥ 2, otherwise *E*nbr*(i*; *G)* = 0 for a node with no connections or only one connection.

#### **2.4. STATISTICAL INFERENCES**

We tested the equalities of the expected efficiency measures between the controls and the OCD patients. The null hypotheses of the equality of the expected network-level efficiencies (*E*glob, *E*loc) between the networks of the controls (*G*1) and that of the OCD patients (*G*2) are given as

$$\begin{cases} H\_0^{\text{glob}} : \mathbb{E}\left(E\_{\text{glob}}(\mathcal{G}\_2)\right) - \mathbb{E}\left(E\_{\text{glob}}(\mathcal{G}\_1)\right) = 0\\ H\_0^{\text{loc}} : \mathbb{E}\left(E\_{\text{loc}}(\mathcal{G}\_2)\right) - \mathbb{E}\left(E\_{\text{loc}}(\mathcal{G}\_1)\right) = 0. \end{cases} \tag{10}$$

For the node-level measures (*E*nodal, *E*nbr), the null hypotheses of equality at a node *i* are given as

$$\begin{cases} H\_0^{\text{nodal}}(i) : \mathbb{E} \{ E\_{\text{nodal}}(i; \mathcal{G}\_2) \} - \mathbb{E} \{ E\_{\text{nodal}}(i; \mathcal{G}\_1) \} = 0 \\\ H\_0^{\text{nbr}}(i) : \mathbb{E} \{ E\_{\text{nbr}}(i; \mathcal{G}\_2) \} - \mathbb{E} \{ E\_{\text{nbr}}(i; \mathcal{G}\_1) \} = 0. \end{cases} \tag{11}$$

We used randomization to compute the exact *p*-values for the significances of differences (Nichols and Holmes, 2001) in the global, local, nodal and neighboring efficiency as given in section 2.3. The group identifiers (*g* = 1*,* 2) were randomly permuted for 2000 times and the identical analysis steps were applied to construct graphs and derive efficiency measures. The difference between the two randomly separated groups were used to obtain the null distribution under the hypothesis *H*<sup>0</sup> that there is no difference between the controls and the OCD patients. The *p*-values were calculated at each cost as two-tailed *p*-values.

The significance level is given as 0*.*05 in this study. We did not apply any multiple comparison corrections in comparing the efficiency measures for each ROI as in the previous brain connectivity studies (Achard and Bullmore, 2007; Wang et al., 2009a), neither for each cost since the networks with the adjacent costs are obviously not independent. The similar relationship between the networks across the costs has been explained

<sup>3</sup>https://sites*.*google*.*com/site/bctnet/

by *graph filtration* (Lee et al., 2011), the process in which the succeeding network embeds the preceding network with the decreasing threshold of correlation (or the increasing "epsilon" distance in constructing *Rips complexes* (Ghrist, 2007) in an *N*-dimensional similarity space). We pursued, however, a persistent group difference over the various costs as well in this study.

## **3. RESULTS**

#### **3.1. DEMOGRAPHIC AND CLINICAL VARIABLES OF PARTICIPANTS**

Demographic and clinical variables are tabulated with corresponding statistics and *p*-values in **Table 1**. There were no significant differences in age (*p* = 0*.*49), gender ratio (*p* = 0*.*88), education year (*p* = 0*.*59) and IQ (*p* = 0*.*49). BDI and BAI scores in the OCD patients were significantly higher than the controls (BDI, *p <* 10<sup>−</sup>7; BAI, *p <* 10<sup>−</sup>6). The mean of total Y-BOCS in the OCD patients was 21.03 with the standard deviation of 6.06.

Out of 32 OCD patients, 13 patients (41%) were with contamination, 8 patients (25%) with checking, 5 patients (16%) with aggressions and 5 patients (16%) with other obsessions of sex, religion, somatic, or a combination of them as their prominent symptoms, as classified with Y-BOCS Symptom Checklist (Goodman et al., 1989). The main symptom of one patient was not determined. No significant differences between the two largest subgroups (contamination *vs.* checking) were found in total Y-BOCS (*p* = 0*.*31), neither in obsession (*p* = 0*.*10) nor compulsion (*p* = 0*.*98) subscores. The equalities across the other subgroups were not tested due to the small sizes of the subgroups. In addition, we did not find the effect of the history of medications on the severity of OCD either; between 23 drug-naïve patients and 8 unmedicated patients, there were no significant differences in total Y-BOCS (*p* = 0*.*25) and the subscores of obsession (*p* = 0*.*90) and compulsion (*p* = 0*.*58).


*The mean and the standard deviation of the variables are shown except for gender. The IQ was estimated by Korean-Wechsler adult intelligence scale-revised (K-WAIS-R). Abbreviations: BDI, Beck's depression inventory; BAI, Beck's anxiety inventory; Y-BOCS, Yale-Brown obsessive-compulsive disorder Scale.*

## **3.2. NO GROUP DIFFERENCES IN CORTICAL THICKNESS AND CORRELATION COEFFICIENTS**

In prior to graph measure analysis, we compared cortical thickness covayring age and gender with multiple comparison correction by SurfStat MATLAB toolbox <sup>4</sup> (Worsley et al., 2009). We found no significant group differences (Figure not shown; corrected *p >* 0*.*56 in left hemisphere and *p >* 0*.*16 in right hemisphere). In addition, inter-regional correlations *rxy* as given in (Equation 2) were compared between groups using Fisher's *z*transformation. Due to the substantially large number (148 × 147/2 for all possible pairs) of simultaneous testings, falsediscovery-rate (FDR; Benjamini and Hochberg, 1995) is used for this case. Once again, no correlation between the pairs of nodes were found to be significantly different between the patients with OCD and the controls (*q >* 0*.*40).

## **3.3. SMALL-WORLDNESS OF THE BRAIN NETWORKS**

The global efficiencies and local efficiencies of the brain networks, as well as the random and lattice networks with the matched costs, are given over the varying costs (0*.*01*,* 0*.*02*,*··· *,* 0*.*50) in **Figure 2**. We found that the network-level efficiency measures of the brain networks were invariently in-between the cost-matched random and lattice networks as

$$E\_{\rm global}(\mathcal{G}\_{\rm rnd}) > E\_{\rm global}(\mathcal{G}\_{\rm brain}) > E\_{\rm global}(\mathcal{G}\_{\rm lat}) \text{ and}$$

$$E\_{\rm loc}(\mathcal{G}\_{\rm lat}) > E\_{\rm loc}(\mathcal{G}\_{\rm brain}) > E\_{\rm loc}(\mathcal{G}\_{\rm rnd}), \tag{12}$$

except for two extreme cases (cost of 0.01 and 0.50).

These characteristics of inequalities have been typically referred as economic small-world behaviors of networks (Latora and Marchiori, 2003; Achard and Bullmore, 2007). It has been found that many brain networks of clinical populations are still in the small-world regime despite the significantly altered properties of patients in comparison with the healthy populations (He et al., 2007, 2008, 2009; Wang et al., 2009b; Lo et al., 2010). Thus we presume that the cortical thickness network of the OCD patients has the small-world architecture, as well as that of the controls.

#### **3.4. NO GROUP DIFFERENCES IN NETWORK-LEVEL EFFICIENCY**

We found significantly smaller global efficiencies in the OCD patients than the controls at the cost of 0.06 (*p* = 0*.*03), 0.48 (*p* = 0*.*04) and 0.49 (*p* = 0*.*02), but found no differences at the other costs. No significant group differences in local efficiency were found at any costs we studied. In addition, the area under curves (AUC) divided by the range of costs, or the mean of efficiencies across the discrete costs, were compared. We found no differences in the mean global efficiency (*p* = 0*.*14) nor the mean local efficiency (*p* = 0*.*74). Taken together, we did not find a clear distinction between the OCD patients and the controls in terms of the aggregated network-level efficiency measures.

#### **3.5. GROUP DIFFERENCES IN NODE-LEVEL EFFICIENCY**

In contrast to the results of network-level efficiency, we found significant group differences at node-level. The heat maps of

<sup>4</sup>http://www*.*math*.*mcgill*.*ca/keith/surfstat/

group-wise nodal efficiency and neighboring efficiency over costs are given in **Figures 3**, **4**, respectively. Additionally, the differences in efficiency measures between the groups and the negative logarithm with the base of ten of *p*-values are also shown together. The logarithmic *p*-value is signed as positive when the efficiency is greater in the OCD patients than in the controls (− log10 *p >* 0) and as negative when the value is smaller in the patients than in the controls (log10 *p <* 0).

What can be prominently noted from **Figure 3** is that the number of disconnected nodes (the nodes with infinity distance to all other nodes thus zero nodal efficiency; blue pixels in **Figures 3A,B**) at a low cost are larger in the OCD patients than in the controls (e.g., when *c* = 0*.*01, 74 in OCD, 61 in HC), and that it takes higher costs to be connected to any nodes in the OCD patients (*c* = 0.20) than in NC (*c* = 0*.*15). Unlike the small-wordness measure (Watts and Strogatz, 1998), which is given for a connected network, the efficiency measure (Latora and Marchiori, 2003) enables the investigation of the disconnected graphs with smaller costs in the present study.

As we have done previously for the network-level efficiency measures, the AUC divided by the range of costs were compared for each node, so that we can compare mean measures across costs. Out of the 148 ROIs, 9 nodes showed significant group differences in nodal efficiencies, while 15 nodes were found to be significantly different in neighboring efficiencies (*p <* 0*.*05), as summarized in **Table 2**. For frontal regions, left orbital frontal gyrus and right lateral orbital sulcus showed lower neighboring efficiencies in the OCD patients than the controls, while left middle frontal sulcus exhibited smaller nodal efficiency as well. On the other hand, parietal regions such as left postcentral gyrus, right postcentral sulcus, left superior parietal gyrus, and bilateral sulci intermedius primus of Jensen showed higher efficiency measures in the OCD patients. Finally, medial occipitotemporal gyri around parahippocampal gyri bilaterally showed smaller nodal and neighboring efficiencies in the patients with

OCD, with smaller nodal efficiency in right lateral and medial occipito-temporal gyri.

See Destrieux et al. (2010) for abbreviation of the ROI labels. The group

## *3.5.1. Spatial pattern of node-level efficiency differences*

Interestingly, the spatial bias of the node-level differences in efficiency measures was found at a large scale (i.e., dorsal *vs.* ventral). For the sake of simplicity, the nodes are classified either as a dorsal or ventral node, based on the Z-coordinate of the center of mass of a ROI, in relation to the median of Z-coordinates of the all ROIs. Mind that this separation based on the Z-coordinate is only for the purpose of a simple comparison of the spatial distribution. Further investigation on community structures based on thickness correlation also might be useful to analyze the distribution of local alterations, but we did not include such an analysis in the present study to keep our focus here to the efficiency of networks. The geometrical distribution of the efficiency measures

efficiency in OCD than in HC (− log10 *p >* 0), and vice versa (log10 *p <* 0).

and the corresponding binary networks in the template space are visualized in **Figures 5**, **6**, for nodal efficiency and neighboring efficiency, respectively. As previously hinted at by **Table 2**, it can be noticed that the efficiency measures of many dorsal nodes are greater in the OCD patients than the controls, and the ones of many ventral nodes are smaller from **Figures 5**, **6**. The signed *p*-values of 148 ROIs are summarized while the location (dorsal or ventral) of nodes with significant group differences marked in **Figure 7**. For the nodal efficiency *E*nodal (**Figure 7A**), all of the 4 nodes with significantly larger values in the OCD patients (nodes above the upper red line) were dorsal without any ventral nodes (100%; green), and all of the 5 nodes with significantly smaller values (nodes under the lower red line) were ventral (100%; magenta). For the neighboring efficiency *E*nbr (**Figure 7B**), 6 out of 8 nodes with significantly greater values in the OCD patients were dorsal (75%), and all of the 7 nodes with significantly smaller values were ventral (100%).

Although the spatial bias in the present results seems clearly discernible (100%; 100%; 75%; 100%), one may be interested in the stability of the present finding. It can be possible to compute


**Table 2 | The summary of the nodes with significant differences in mean efficiency (***p <* **0***.***05) as the subtraction of controls (HC) from the OCD patients (OCD-HC) with corresponding** *p***-values for nodal efficiency (A) and neighboring efficiency (B).**

*Dorsal nodes and ventral nodes are separated for tabulation.*

reliability using a resampling method known as jack-knifing. Unfortunately, however, we used randomization to compute *p*values for group differences. To see the spatial pattern of the nodes with significantly different efficiency in a resampled subset, each subset requires a new run of randomization and graph measure computation. It renders impractical computational load with the current MATLAB codes. Thus we have not carried out the analysis for this study. We discuss on the spatial bias of node-level efficiency measures between the dorsal and ventral networks from the perspective of the imbalance theory of dorsal-ventral pathways in the OCD patients in the following section.

#### *3.5.2. Altered relationship between node-level efficiency and node centrality*

In addition to the spatial pattern, the relationship between the efficiency and centrality are further examined. We used degree, which is the number of connected edges to a node, as a simple measure for node centrality. The mean efficiencies are plotted over degrees in **Figure 8**. The efficiency is fitted using a GLM as

$$E(i) = \beta\_0 + \beta\_1 D(i) + \beta\_2 G(i) + \beta\_3 D(i)G(i) + \varepsilon \qquad (13)$$

where *E(i)* is an efficiency measure of the *i*-th node, *D(i)* is a degree and *G(i)* is a group index. The efficiencies were well explained by the full models (nodal efficiency, *R*<sup>2</sup> = 0*.*98; neighboring efficiency, *R*<sup>2</sup> = 0*.*50). We used logarithm of degrees when the model fit is improved as in case of neighboring efficiency (*R*<sup>2</sup> for the full model with a linear degree measure is 0.39).

The interactions between group index and degree were found to be significant for nodal efficiency (*p <* 0*.*005) and neighboring efficiency (*p <* 0*.*0005). The nodes with significantly difference efficiencies seem to be responsible for the interaction, especially for that the nodes with lower efficiency are deviated from the fitting lines. The *p*-values for the interactions without the nodes with significantly smaller efficiencies were higher than significance level in the study (nodal efficiency, *p* = 0*.*23; neighboring efficiency, *p* = 0*.*24). Thus the nodes with significantly smaller efficiencies in patients with OCD seem to be aberrant from the other nodes in the OCD patients. As an illustration for this idea, the degrees and efficiencies of the nodes with the smaller efficiencies from OCD patients are plotted over cost in **Figure 9**. For comparison, the measures of the other nodes with similar degrees from the patients are also given. In the process of graph

**FIGURE 5 | Mean nodal efficiencies across costs are overlaid on the cortical surfaces of the controls (HC, column A) and the OCD patients (OCD, column C) from a dorsal view (upper row) and a ventral view (lower row).** See the color bar on the leftmost side for the color coding from 0 to 0.8. The sub-graphs (*G<sup>i</sup>* ) of the binary

networks at the cost of 0.10 are shown as well for HC (column **B**) and OCD (column **D**). From the nodes that showed significantly different nodal efficiencies (marked by thick circles), their first neighbors are connected in distinct colors which correspond to the ROI legend on the rightmost side.

growth with the increasing cost, the efficiencies of the nodes without significant group differences (gray lines) increase earlier than the nodes with significant group differences (cyan lines). Thus the nodes with group differences have smaller mean efficiency (AUC divided by the range of cost) than the other nodes with similar mean degrees, deviating from the fitting lines in **Figure 8**.

## **4. DISCUSSION**

## **4.1. THE BRAIN NETWORK OF OCD PATIENTS IN THE SMALL-WORLD REGIME**

We found that the brain network of the OCD patients is within the small-world regime as well as that of the controls. The small-worldness of neuronal network has been demonstrated in various scales: the neuronal system of *C. elegans* (Watts and Strogatz, 1998), the brains of cats and macaque monkeys (Hilgetag and Kaiser, 2004; Kaiser, 2007), and that of humans (Sporns et al., 2004; Sporns and Honey, 2006; Achard and Bullmore, 2007; Hagmann et al., 2007; He et al., 2007; Hagmann et al., 2008). Although the network properties were found to be altered to a significant degree, the brain networks of clinical population also exhibited small-worldness distinctively from the cost-matched theoretical networks (He et al., 2008, 2009; Wang et al., 2009a; Bernhardt et al., 2011). In consistence with a previous functional network study on the OCD patients (Zhang et al., 2011), we confirmed that the structural network of the patients shows small-worldness as well in terms of inequality of network-level efficiency measures (Equation 12) as shown in **Figure 2**.

The small-worldness of a network implies the existence of local clusters in relation to its equivalent random counterpart (Kaiser, 2011). As it can be seen in **Figures 1E**, **10**, the cortical thickness networks from both of the OCD patients and the controls remarkably showed the variant degrees of connections across nodes. Regarding that the random and lattice networks are generated to have uniform distributions of degrees, the variety of degrees of the real-world brain networks makes them clearly distinguishable from the theoretical networks. In particular, the pattern of mean degrees showed noteworthy resemblance between the brain networks of the OCD patients and the control as given in **Figure 10**. The correlation of mean degrees between the groups was strongly positive (*r* = 0*.*4969*, p <* 10<sup>−</sup>9). It may imply that the essential structures and functions of brain network are still preserved in the OCD patients, as shown as intact capabilities of basic behaviors and primitive functioning of the patients, though diverse impairment in high level cognitive functioning (Graybiel and Rauch, 2000; Kuelz et al., 2004).

## **4.2. DORSAL AND VENTRAL DISPARITY IN OCD PATIENTS**

The most significant contribution of our present study is detecting the disparity between the dorsal and the ventral networks in

**FIGURE 6 | Mean neighboring efficiencies across costs for the controls (HC, column A) and the patients with OCD (OCD, column C) and the sub-graphs (***Gi***) of the corresponding binary networks at the cost of 0.1**

**(HC, column B; OCD, column D) are shown in the same graphical scheme as Figure 5, except that the color coding range of neighboring efficiencies is adjusted from 0.3 to 0.8 for a better visualization.**

the OCD patients in terms of graph theoretical measures, supporting the hypothesis on "the imbalance of tone" between direct and indirect CST pathways (Saxena et al., 1998, 2001).

Although we could not find any group differences in the mean network-level efficiency measures, we found significant alterations of the node-level efficiency in the OCD patients that were localized with an evident spatial bias as shown in **Figures 5**, **6**, which may reflect the imbalance between the dorsal and ventral pathways in the patients. In particular, the topological alterations were particularly localized in sensory-motor regions including paracentral lobule, postcentral regions, parietal cortices and middle frontal cortex as greater nodal or neighboring efficiencies in the OCD patients than the controls, and the aberrations were also detected in the ventral frontal and temporal regions including orbital cortices the parahippocampal cortices as smaller nodal or neighboring efficiencies in the OCD patients. We have shown that the ratios of nodes with significantly greater or smaller efficiency are not equal between the dorsal and ventral nodes, which may reflect the spatial disparity of the subnetworks in the patients with OCD, as shown in **Figure 7**.

Our findings are in accordance with the previous VBM studies those found local alterations of gray matter in parietal cortex (Kim et al., 2001; Valente et al., 2005), middle temporal and occipital cortex (Togao et al., 2010) and orbital frontal cortex (Pujol et al., 2004; Szeszko et al., 2008). Given the neuropathological model (Saxena et al., 2001; Menzies et al., 2008), those regions have been considered as the loci of the abnormalities of OC symptoms such as attention control deficit, excessive anxiety and failure of impulse control (Friedlander and Desrocher, 2006; Menzies et al., 2008).

More interestingly, the present findings seem to be closely related to a multivariate study on the structural network of OCD patients (Menzies et al., 2007), which used a statistical technique called partial least square (PLS; McIntosh et al., 1996). Unlike the massive univariate approaches such as VBM, PLS extracts spatial patterns that optimally correlate with a given measure of interest from the whole image. Thus the ability of PLS to detect a component with covariance is quite similar to that of the cortical thickness network analysis we used in this paper, in the sense of multivariate approaches. Their study showed that higher gray matter density in a "parieto-cingulo-striatal system" and lower gray matter density in a "fronto-temporal system" were correlated with increasing behavioral impairment in OCD patients (Menzies et al., 2007), with a striking congruence with the current findings.

## **4.3. ABERRANT RELATIONSHIP BETWEEN EFFICIENCY AND CENTRALITY IN OCD PATIENTS**

We also found that significant interactions between degree centrality and group on node-level efficiency. The interactions seemed to be driven by the nodes that showed significant group differences, which are deviated even from the other nodes within the patients. In particular, the nodes with smaller efficiency in OCD patients demonstrated different graph growth trajectory with the increasing costs, compared to the other nodes in OCD patients with similar mean degrees. The nodal efficiency of a node can increase without the additional connections to the node but

**FIGURE 7 | The mean node-level efficiency measures were compared.** Signed logarithmic *p*-values for nodal efficiency **(A)** and for neighboring efficiency **(B)** are given. The x-axis indicates the index of the ROI, or the node, from the left hemisphere (LH) to the right hemisphere (RH), which are separated by dashed vertical lines. Along the y-axis, the positive values mean the higher

efficiency measures in OCD patients than in controls (− log10 *p >* 0), and the negative values mean lower efficiency measures in OCD patients (log10 *p <* 0). The significance level of α = 0*.*05 are marked with red horizontal lines and the suprathreshold nodes are highlighted by green circles (dorsal nodes) or magenta squares (ventral nodes). See **Table 2** for ROI labels.

**FIGURE 8 | Mean nodal efficiency (A) and neighboring efficiency (B) are shown over mean degrees in a linear (A) and a logarithmic (B) scale.** Each point indicate an ROI from the networks of the healthy controls (HC, blue dots) and patients with OCD (OCD, red dots). Specific ROIs with significant group differences in efficiency measures are marked with

either green triangles (*E(i*; *G*OCD*) > E(i*; *G*HC*)*) or cyan triangles (*E(i*; *G*OCD*) < E(i*; *G*HC*)*). Regression lines are given for each group (HC, blue line; OCD, red line). Above panels, *F*-statistic and *p*-value for a GLM testing the interaction between logarithm of degree and group and *R*<sup>2</sup> for the full model are given.

**FIGURE 9 | Degree (upper row) and efficiency (lower row) are plotted over cost for the nodes with smaller nodal efficiency (A, cyan) and smaller neighboring efficiency (B, cyan) from OCD patients.** For comparison, the measures of the other nodes from the patients with

similar mean degrees are also shown (gray). The nodes with smaller efficiency in OCD than healthy controls exhibits different growth of efficiency even though the mean degrees are similar to the other nodes in OCD.

by the additional connections to the other node that is already connected to the node. In other words, a node that is connected to a hub node can have high nodal efficiency even with only 1◦. In case of OCD patients in the present study, the nodes without group differences (**Figure 9A**, gray lines) showed abrupt increase of nodal efficiency at a low cost (*<*0*.*05) without much increase of degrees. On the other hand, the nodes with group differences (**Figure 9A**, cyan lines) took high costs to have sudden increase of nodal efficiency, which is likely to be the point when the node is connected to the other node with high degrees.

Neighboring efficiency does not monotonously increase by increasing cost, since the measure quantifies the connections between the first neighbors of the node. Thus the neighboring efficiency can suddenly decrease during the graph growth when a new node without any connections to the pre-existing first neighbors is connected. In our case of OCD patients, the nodes without group differences (**Figure 9B**, gray lines) showed sudden increases at low degrees and sudden decrease, while the nodes with group differences (**Figure 9B**, cyan lines) evolved with slowly increasing neighboring efficiency, which means connecting unrelated nodes into its the first neighborhood.

Only 3 nodes out of 24 nodes with group difference in efficiency found to be with significantly different degree between groups (*p <* 0*.*05). It is presumably because that the nodes with significant group differences showed different trajectory of evolution. Thus the difference in node level efficiency could not be explained solely by degree centrality but also the connectivity of other nodes as well. As an alternative measure for centrality in a close relation with efficiency, *information centrality* has been introduced (Latora and Marchiori, 2004), but further investigation in terms of graph theories remains to pursuit in this paper.

## **4.4. CORTICAL THICKNESS NETWORK AND THE PATTERN OF FUNCTIONAL ACTIVATIONS IN OCD PATIENTS**

The disruption of structural architecture may correlate with the alteration of the functional activation of involved areas. Specifically, the cortical thickness network has demonstrated its spatial correspondence to the DMN in human brains (Raznahan et al., 2011), and the altered relationship between the subcorticocortical structures in a mouse model of Huntington's disease, in which its subcortical functions were impaired by a gene-knockout (Lerch et al., 2008). Here, the altered efficiency measures at a node level in the cortical thickness network of OCD patients implicate the different pattern of one-to-*n* similarity of local morphology of a node (nodal efficiency), or the different pattern of *n*-to-*n* similarity within the neighbors of the node (neighboring efficiency) in relation to the network of the controls. Although we do not have concurrent functional dataset of the participants, an fMRI meta-analysis using activation likelihood estimation (ALE; Turkeltaub et al., 2002) demonstrated that a greater activation was found in the left inferior parietal cortex in OCD patients than controls and a smaller activation in the left parahippocampal gyrus was found during various tasks (Menzies et al., 2008) in relation to our current findings. However, it would be fair to note that the meta-analysis also reported the foci of abnormal activations that are not clearly relevant to our results, as well as other studies that showed the different patterns of activation in OCD patients, during tasks (Nakao et al., 2005; Han et al., 2011) and using PET at rest (Kwon et al., 2003). Thus the relationship between the patterns of functional activation and the efficiencies of cortical thickness network may not be simply straightforward but manifold due to the complex nature of human brains.

A recent whole-brain analysis on the functional connectivity of OCD patients showed significantly higher or lower inter-regional correlations of activations at rest (Zhang et al., 2011). The spatial patterns of aberrant functional connectivity in their findings were not quantified, but it can be noted that higher correlations were found between parietal nodes, cingulate nodes and dorsal frontal nodes and lower correlations were found between prefrontal nodes and posterior temporal nodes (Zhang et al., 2011). Although a larger number of samples and simultaneous multi-modal data will be beneficial to clarify the interaction between the structural and functional networks, we conjecture that the topological alteration in the structural network in OCD patients would exhibit concurrent deviant patterns of functional activations.

It can be added to discussion, that the resting-state hyperactivity in the ventral networks in OCD patients has been consistently found in terms of greater local activation (Kwon et al., 2003; Friedlander and Desrocher, 2006) and higher cortico-strial functional connectivity of the basal ganglia (Harrison et al., 2009). Intriguingly, it was demonstrated that the ventral network, primarily including orbital frontal cortex, showed smaller deactivation (i.e., the failure of inhibition), responding to the participant's own error, thus resulting in higher activation in OCD patients compared to controls (Stern et al., 2011). The coarse connections and lower efficiency in the ventral network of the OCD patients in our present findings implies that the morphometric similarity of the ventral nodes with other nodes is disturbed. It may reflect the underlying pathology of the dysfunction of inhibitory controls in the OCD patients.

## **4.5. LIMITATIONS AND FUTURE WORKS**

The first methodological limitation of our study is that the current practice of cortical thickness network analysis is restricted to cortical structures. Although the subcortical structures were considered to be highly involved in the pathophysiology of mental disorders including OCD (Cummings, 1993; Saxena et al., 2001), the present technical issues such as MR imaging resolution and tissue contrast still render the surface analysis problematic to the other brain structures than neocortices, despite recent computational advances (Khan et al., 2008; Qiu et al., 2010). Alternatively, the volumetric measure of a subcortical structure may be used along with the cortical thickness (Lerch et al., 2008). In addition, it can also be possible to characterize the covariance structure of local morphology in volumetric space (Kim et al., 2011a; Tijms et al., 2012). It may be useful to adapt and combine those methods to investigate the relationship within and between the cortical and subcortical networks.

The second limitation is that we could not separate the OCD patients by their main symptoms, mainly due to the small size of subgroups. As there have been rich discussions and supporting evidences for the heterogeneity of OCD symptoms (Mataix-Cols and van den Heuvel, 2006; van den Heuvel et al., 2009; Koch et al., 2012), possible subtypes and multi-dimensions of the disorder were discussed in the context of refining the diagnosis criteria in the next generation of DSM (Leckman et al., 2010; Mataix-Cols et al., 2010; Taylor, 2011). Even though we did not carry out the analyses on the subgroups of the OCD patients in this paper, a methodological improvement of the diagnosis and a larger number of samples may resort the inconsistency in the previous findings due to the diversity of OCD.

In relation to heterogeneity, we did not find any group differences in cortical thickness in the current sample. Although we previously reported cortical thinning in other patients with unmedicated OCD (Shin et al., 2007), it was demonstrated that a severity of OCD subtype may be correlated with the cortical thickness (Nakamae et al., 2012). The underlying mechanism of OCD might not be directly reflected in the local morphometry, but rather be manifested in the interaction of complex networks, which motivated the series of graph analysis on human brain including the current study as well.

In conclusion, we have examined the network properties in the patients with OCD based on the cortical thickness for the first time. The anatomical network in the OCD patients was in the small-world regime as well as that of the healthy controls. We found topological alterations in the patients in terms of efficiency at node level and its relation to node centrality. The alteration showed disparity between the dorsal and ventral networks, which may contribute to confirm the dorsal-ventral imbalance hypothesis (Saxena et al., 2001).

## **ACKNOWLEDGMENTS**

The authors would like to thank Dr. Moo K. Chung for methodological consulting. The portion of this work was presented at the 18th Annual Meeting of the Organization for Human Brain

## **REFERENCES**


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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 27 January 2013; accepted: 06 June 2013; published online: 03 July 2013.*

*Citation: Kim S-G, Jung WH, Kim SN, Jang JH and Kwon JS (2013) Disparity between dorsal and ventral networks in patients with obsessive-compulsive disorder: evidence revealed by graph theoretical analysis based on cortical thickness from MRI. Front. Hum. Neurosci. 7:302. doi: 10.3389/fnhum.2013.00302*

*Copyright © 2013 Kim, Jung, Kim, Jang and Kwon. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Topological correlations of structural and functional networks in patients with traumatic brain injury

## *Karen Caeyenberghs 1,2\*, Alexander Leemans 3, Inge Leunissen4, Karla Michiels <sup>5</sup> and Stephan P. Swinnen4*

*<sup>1</sup> Department of Physical Therapy and Motor Rehabilitation, Faculty of Medicine and Health sciences, University of Ghent, Ghent, Belgium*

*<sup>2</sup> Department of Movement and Sport Sciences, Faculty of Medicine and Health sciences, University of Ghent, Ghent, Belgium*

*<sup>3</sup> PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands*

*<sup>4</sup> Group Biomedical Sciences, Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium*

*<sup>5</sup> Department of Physical Medicine and Rehabilitation, University Hospital, Leuven Campus Pellenberg, Leuven, Belgium*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Bharat B. Biswal, UMDNJ, USA Claudia A. M. Wheeler-Kingshott, University College London, UK*

#### *\*Correspondence:*

*Karen Caeyenberghs, Rehabilitation Sciences and Physiotherapy, University of Ghent, Campus Heymans 1B3, De Pintelaan 185, 9000 Ghent, Belgium e-mail: karen.caeyenberghs@ ugent.be*

Despite an increasing amount of specific correlation studies between structural and functional connectivity, there is still a need for combined studies, especially in pathological conditions. Impairments of brain white matter (WM) and diffuse axonal injuries are commonly suspected to be responsible for the disconnection hypothesis in traumatic brain injury (TBI) patients. Moreover, our previous research on TBI patients shows a strong relationship between abnormalities in topological organization of brain networks and behavioral deficits. In this study, we combined task-related functional connectivity (using event-related fMRI) with structural connectivity (derived from fiber tractography using diffusion MRI data) estimates in the same participants (17 adults with TBI and 16 controls), allowing for direct comparison between graph metrics of the different imaging modalities. Connectivity matrices were computed covering the switching motor network, which includes the basal ganglia, anterior cingulate cortex/supplementary motor area, and anterior insula/inferior frontal gyrus. The edges constituting this network consisted of the partial correlations between the fMRI time series from each node of the switching motor network. The interregional anatomical connections between the switching-related areas were determined using the fiber tractography results. We found that graph metrics and hubs obtained showed no agreement in both groups. The topological properties of brain functional networks could not be solely accounted for by the properties of the underlying structural networks. However, combining complementary information from both imaging modalities could improve accuracy in prediction of switching performance. Direct comparison between functional task-related and anatomical structural connectivity, presented here for the first time in TBI patients, links two powerful approaches to map the patterns of brain connectivity that may underlie behavioral deficits in brain-injured patients.

#### **Keywords: functional connectivity, structural connectivity, brain networks, graph theoretical analysis, brain injury**

## **INTRODUCTION**

Many patients with traumatic brain injury (TBI) are faced with persistent cognitive deficits, including impairments in information processing speed, memory, and executive function, which limit recovery (Levin and Kraus, 1994; Miller, 2000; Godefroy, 2003). The clinical pathology underlying this poor cognitive outcome is traumatic axonal injury (TAI), which is characterized by widespread axonal damage due to shearing forces by acceleration, deceleration, or rotation of the brain. TAI disrupts the efficient functioning of brain networks, which consists of white matter (WM) tracts that connect brain regions As cognitive control depends on the coherent activity of widely distributed networks, it is important to examine the characteristics of the brain networks in TBI.

The area of graph theory is an established mathematical field and has proven a very effective and informative way to explore brain networks and human behavior (Bassett and Bullmore, 2009; Bullmore and Sporns, 2009) in health (e.g., Iturria-Medina et al., 2008; Li et al., 2009) and disease (for an overview, see Griffa et al., 2013). With graph theory, the brain can be represented in an abstract manner as a set of "nodes," defined by anatomical regions across the cortex, and "edges," which reflect connection properties between these nodes (e.g., Hagmann et al., 2008). While the node/edge characteristics are typically represented by "connectivity matrices," a graph theoretical analysis (GTA) provides a novel way to explore topological and geometrical properties of brain networks, such as clustering coefficient, small worldness, efficiency, path length, connectivity degree, among others [for an in-depth discussion of these measures, see (Rubinov and Sporns, 2010)].

As TAI disrupts the connections of distributed brain networks, GTA has already offered insights into the dysfunction of these networks following TBI using different imaging modalities. For example, using fMRI-based GTAs (Caeyenberghs et al., 2012a), patients with TBI showed increased connectivity degree and strength, and higher values of local efficiency, compared with controls. On the other hand, diffusion MRI-based GTAs have shown reduced connectivity degree, longer average path lengths, and reduced network efficiency in brain-injured adults (Caeyenberghs et al., 2012b; Pandit et al., 2013) and children (Caeyenberghs et al., 2012c). These findings suggest that TBI affects the global organization of the brain network and support the notion of TBI as a "disconnection syndrome" from a network perspective (Guye et al., 2010).

However, most studies have used only one of the imaging modalities at a time. Greater effort should be focused on the integration of different modalities, since combining complementary information from the different imaging modalities may be more fruitful than using either one alone (for a review, see Damoiseaux and Greicius, 2009). It can be especially helpful in studying disease (e.g., Andrews-Hanna et al., 2007; Rocca et al., 2007; Lowe et al., 2008; Skudlarski et al., 2010; Palacios et al., 2012). For example, Lowe et al. (2008) found that in a cohort of 11 multiple sclerosis patients and 10 control subjects, mean FA of the transcallosal motor pathway, as derived from DTI, correlated positively with functional connectivity of the bilateral primary sensorimotor cortices, as measured with resting state fMRI.

In these studies, WM microstructural measures usually come in the form of single scores. However, it is important and informative to compare *equidimensional* structural and functional connectivity maps/matrices, that is, cases in which both structural and functional connectivity indices are available for the same pairs of regions-of-interest. Moreover, in order to extract relevant information from the brain's structure and function, it is necessary to validate them against different parameters of another framework, such as GTA. For example, Hagmann et al. (2008) and Honey et al. (2009) were, to the best of our knowledge, the first to use GTA to directly compare resting-state functional connectivity with structural connectivity. The authors found that the strength of resting-state functional connectivity correlated positively with structural connectivity strength in healthy participants. However, there is still a need for combined studies, especially in pathological conditions.

In this paper, we compared the graph metrics of task-related functional connectivity, using event-related fMRI, and structural connectivity, derived from fiber tractography using diffusion MRI data, computed in the same participants (17 adults with TBI and 16 controls). Our primary goal was to test the hypothesis that TBI patients would show a negative correlation between the two aspects of brain connectivity, i.e., TBI patients who exhibit more profound structural deficits (lower structural connectivity) would show higher functional connectivity (to compensate). Specifically, TAI might cause reorganization of functional connectivity and thus cause a negative association between functional and structural connectivity. This was predicted on the basis of relevant earlier work showing that patients with TBI who showed higher functional connectivity degree displayed lower switching task performance and more severe brain injury (Caeyenberghs et al., 2012a). Conversely, the results of our diffusion MRI based GTA's showed a decrease in global integration in structural networks in TBI patients (Caeyenberghs et al., 2012b,c). These earlier results suggested that the higher functional network cohesion in the TBI group may be directly related to a poorer neurobiological substrate, i.e. structural disconnection between brain areas or lower structural connectivity. Moreover, we sought to validate whether complementary structural and functional connectivity information can be combined to improve accuracy in prediction of behavioral deficits.

## **MATERIALS AND METHODS**

## **PARTICIPANTS AND MRI DATA ACQUISITION**

The present study included data from 17 adults with TBI and 16 controls. The TBI patients had sustained closed-head trauma due to traffic accident or sport injury that averaged 4 years 3 months prior to the study (*SD* = 2 years 5 months). The majority of patients sustained moderate to severe TBI as measured by the postresuscitation Glasgow Coma Scale (GCS, Teasdale and Jennett, 1974) (only available from 4 patients, *M* = 7*.*8, range = 4–12), the duration of loss of consciousness (30 min or more), the length of post-traumatic amnesia (*>*1 day), the anatomical features of the injury based on inspection by an expert neuroradiologist (see below), and the injury mechanism (traffic accidents and falls), or combinations thereof. Informed consent was obtained from each subject, and ethical approval was granted by the local ethics committee for biomedical research.

Diffusion tensor images (**Figure 2B**) were acquired with a Siemens 3 T Magnetom Trio MRI scanner (Siemens, Erlangen, Germany) using the following parameters: single shot spin-echo; slice thickness 2.9 mm; repetition time (TR) 7200 ms, echo time (TE) 81 ms, number of diffusion directions 64, diffusion weighting 1000 s/mm2, number of sagittal slices 56, in-plane resolution 2*.*2 × 2*.*2 mm<sup>2</sup> with a field of view of 210 × 210 mm2.

Functional data (fMRI) (**Figure 2A**) were acquired with a descending gradient echo planar imaging (EPI) pulse sequence for T2∗-weighted images (*TR* = 3000 ms, *TE* = 30 ms, flip angle = 90◦, 50 oblique axial slices each 2.8 mm thick, inter-slice gap 0.028 mm, in-plane resolution 2*.*5 × 2*.*5 mm2, and matrix size of 80 × 80).

Finally, a high resolution T1-weighted structural image was acquired using magnetization prepared rapid gradient echo (MPRAGE; *TR* = 2300 ms, *TE* = 2*.*98 ms, 1 × 1× 1.1 mm<sup>3</sup> voxels, field of view (FOV): 240 × 256 mm2, 160 sagittal slices). These structural MRI scans were inspected and classified by an experienced neuro-radiologist (S.S.). Demographic and neurologic variables are provided in **Table 1**.

## **BEHAVIORAL TESTING**

Assessment of executive function was performed using the Local Global Task (LGT). Participants performed the LGT (derived from Miyake et al., 2000) with their right hand. The target stimulus (as shown in **Figure 1**) consisted of a "global" square or rectangle, composed of much smaller "local" squares or rectangles. Each trial began with the presentation of a prime cue, indicating to which dimension to attend. The global dimension was cued by a big square, to the left of the stimulus, and a big rectangle to the right. For the local dimension the same square and rectangle appeared, only smaller. After a random cue-target interval of 400–600 ms, the target stimulus was presented. Both the cue and the target stimulus remained on the screen until a participant responded, or until 2500 ms had elapsed. Participants


**Table 1 | Summary of demographic and injury characteristics for the TBI group.**

*Anatomy codes: RH, right hemisphere; LH, left hemisphere; FL, frontal lobe; TL, temporal lobe; PL, parietal lobe; OL, occipital lobe; R, right; L, left. Other codes: TBI, traumatic brain injury; MRI, magnetic resonance imaging; RH, right-handed; LH, left-handed; M, male; F, female, GCS, Glasgow Coma Scale score.*

were required to identify the relevant target stimulus dimension and press one key with their index finger for squares and another with their middle finger for rectangles (**Figure 1**). The interval between a response and the presentation of the next trial varied randomly between 900 and 1100 ms. The experiment was comprised of two unidimensional blocks, and one switch block. In the unidimensional blocks, participants attended to either the global cues or the local cues. The order was counterbalanced across participants. In the third switch block, the target stimulus dimension alternated every other trial (i.e., two "local" trials, followed by two "global" trials, etc.). When the prime cues changed, the participants had to switch from responding to the local dimension to the global dimensions of the target stimulus, and vice versa. A short amount of practice was given to ensure the instructions were understood (4 trials for each unidimensional block, and 8-16 trials for the switch block). The experiment consisted of 24 trials in each pure block, and 49 trials in the switch block. Variables of interest were RT and accuracy on repetition trials and on switch trials, and switch cost (=RT switch trial—RT repetition trial). The whole task took about 15 min.

#### **PREPROCESSING**

In preparation for the definition of the nodes, the fMRI time series were passed through several preprocessing steps using the SPM 5 software package (Wellcome Department of Imaging Neuroscience, University College, London) implemented in MATLAB 7.7 (Mathworks, Sherborn, MA). The first three functional images of each subject's data set were discarded to allow for T1 equilibration. The remaining images were spatially realigned to the first image in the time series, then corrected for differences in slice acquisition time by temporal interpolation to the middle slice (reference slice = 25). Functional images were spatially coregistered to the anatomical image, and normalized using a combination of cost function masking and a unified segmentation procedure (Ashburner and Friston, 2005; Crinion et al., 2007). Finally, the normalized functional images were smoothed with an isotropic 10 mm FWHM Gaussian kernel.

The DTI data were analyzed and processed in ExploreDTI (Leemans et al., 2009; Jones and Leemans, 2011), as previously described in detail (Caeyenberghs et al., 2010a,b, 2011). In summary, for each data set the diffusion-weighted MRI images were corrected for subject motion and eddy-current induced geometrical distortions correction (Leemans and Jones, 2009). During this processing step, we adjusted the B-matrices with the appropriate reorientations and included the required signal intensity modulation with the Jacobian determinant of the spatial transformation (Leemans and Jones, 2009; Jones and Cercignani, 2010). The diffusion tensor was estimated using a non-linear regression procedure (Veraart et al., 2012) from which the diffusion metrics (e.g., fractional anisotropy—FA) were computed for further analysis (Basser and Pierpaoli, 1996).

## **SUBJECT MOTION**

From the realigned fMRI data, it was verified that no subject had head movement larger than 2 mm in any direction during any of the functional runs (translational movements: TBI: mean = 0.55 mm, range = 0.19–1.09 mm; Controls: mean = 0.38 mm, range = 0.18–0.58 mm; rotational motions: TBI: mean = 0*.*53◦, range = 0.23–1.02◦; Controls: mean = 0*.*38◦, range = 0.23– 0.61◦).

Translational motions did not exceed 1 voxel for the DTI data (TBI: mean = 0.95 mm, range = 0.40–1.44 mm; Controls: mean = 0.73 mm, range = 0.37–1.19 mm). Rotations of the DTI data were on average 0.81◦ and ranged between 0.31 and 1.31◦ for the TBI group; in the control group rotations were on average 0.68◦, ranging between 0.22 and 1.25◦.

#### **WHITE MATTER TRACTOGRAPHY**

The interregional anatomical connections between the switchingrelated areas were determined using the fiber tractography results as obtained with *ExploreDTI* (**Figure 2G**) (Basser et al., 2000; Leemans et al., 2009). These fiber pathways were generated by starting seed points sampled uniformly throughout the data at 2 mm isotropic resolution. Trajectory propagation was terminated if FA *<* 0.2 or if the angle between consecutive steps exceeded 45◦. The step size was set at 1 mm.

## **DEFINITION OF THE NODES AND EDGES**

Our network of particular interest was the switching motor network. This group of 22 brain regions (see **Figure 2D**), encompassing the medial frontal cortex (SMA: pre-SMA and SMA-proper), anterior cingulate cortex, bilateral dorso-lateral prefrontal cortex (DLPFC), inferior frontal cortex (BA44), basal ganglia (globus pallidus, putamen, subthalamic nucleus region), bilateral cerebellum (lobule VI), right precuneus, left premotor cortex (dorsal and ventral), bilateral insula, and right superior and right inferior parietal lobules was active during Switch *>* Continue in an eventrelated fMRI design (**Figure 2C**, Coxon et al., 2010; Leunissen et al., 2013b).

As a functional measure (**Figures 2E,F**) we calculated, in each of the 31 subjects, the partial correlations between each pair of ROI's mean time series, filtering out the effects of the remaining 20 brain regions (for details, see Caeyenberghs et al., 2012a). The structural measure for each subject was the number of WM trajectories connecting the ROIs (**Figure 2H**) (Gong et al., 2009; Lo et al., 2010; Hagmann et al., 2010; van den Heuvel et al., 2010). Self-connections of nodes were not included in the analyses.

A weighted graph approach was used, with the partial correlation representing a proxy measure for the strength of functional connectivity, and the number of WM trajectories as a weight value for structural connectivity. In addition to the weighted connectivity matrices, we also calculated unweighted binary matrices, in which the weighting was omitted from the analysis. For each individual dataset, all non-zero weights were set to one and to zero otherwise (van den Heuvel et al., 2010). Thus, for each participant, there were four different kinds of networks (weighted structural, binary structural, weighted functional, binary functional), each of which was represented by a symmetric 22 × 22 matrix.

Within the main analysis, the weights of the connections for structural connectivity were determined by means of the number of WM trajectories. An alternative measure for connectivity strength could be the level of FA, as FA values are regularly used as a measure of WM microstructural organization and as a marker for WM abnormalities in patient studies (Beaulieu et al., 2005; Mori et al., 2007; Caeyenberghs et al., 2010a,b, 2011). Therefore, an additional analysis was performed in which FA values were used as a measure of connectivity strength. In this additional analysis, a similar weighted graph analysis was performed but this time the weights of the connections were determined by means of the FA values of the interconnecting WM connections rather than the number of tracts. Similar to the analysis using the number of WM trajectories, overall graph organizational properties (efficiency, strength, and betweenness centrality) were computed and compared between groups.

**FIGURE 2 | Structural and functional brain connectivity was examined using graph theory through the following steps.** First, we acquired task-related fMRI data **(A)** and DTI data **(B)** in the same participants. **(C,D)** We defined the network nodes as fMRI activation foci. A sphere with radius (of 10 mm) was placed around the MNI coordinates of each ROI's activation peak. **(E)** For each subject, the average time series for each ROI was extracted for the Switch *>* Continue condition in an event-related fMRI design (Coxon et al., 2010; Leunissen et al., 2013a). **(F)** Based on the average time

series data, matrices of partial correlations were then calculated, quantifying the unique functional relationships between each pair of ROIs (Caeyenberghs et al., 2012a). **(G)** Next, using a deterministic tractography approach, the number of white matter trajectories between each pair of regions of the switching motor network was determined. **(H)** This value became the edge weight in the structural connectivity matrix. **(I)** Finally, from the resulting brain networks, graph metrics, including connectivity degree, connection strength, regional efficiency, and betweenness centrality, were computed.

## **GRAPH THEORY ANALYSIS**

The properties of the switching network were investigated at the global and regional (nodal) levels using the Brain Connectivity Toolbox (Rubinov and Sporns, 2010; https://sites*.*google*.*com/ site/bctnet/). The equations to calculate each of these measures can be found in Rubinov and Sporns (2010). We only provide brief, formal definitions for each of the network properties used in this study: connectivity degree, connection strength, regional efficiency, and betweenness centrality (**Figure 2I**). Node degree is the number of links connected to the node. Node strength is the sum of weights of links connected to the node. The local efficiency is the average inverse shortest path length in the network (global efficiency) computed on node neighborhoods. Betweenness centrality is the fraction of all of the shortest paths in a network that contain a given node, with higher numbers indicating participation in a large number of shortest paths. The nodes with the largest betweenness centrality were considered to be pivotal nodes (i.e., hubs) in the network. Specifically, nodes were identified as hubs in the network if the values of nodal betweenness were 2 SDs greater than the average betweenness centrality of the network.

## **STATISTICAL ANALYSIS**

Demographic data, including age and handedness, were examined for between-group differences with *t*-tests. Analysis of the reaction times of the LGT were subjected to a repeatedmeasures ANOVA with factors Group (TBI, controls), Cue condition (Global, Local), and Switch condition (Switch, Repeat). Significant main and interaction effects were further explored by post hoc tests using Tukey correction. For the switch cost and accuracy rate of the LGT, two-sample *t*-tests were performed for comparing the TBI group with the age matched control group. Moreover, controls and patient subgroups with better and poorer switching skills (based on a median-split of the accuracy rate) were separated and used for further analyses (see below). Between-group differences in the functional and structural connectivity were evaluated using two-sample *t*-tests, with graph measures (i.e., degree, strength, local efficiency, and betweenness centrality) from each approach (DTI or fMRI) examined as dependent variables. Pearson correlations were used to determine the association between structural and functional connectivity. Finally, separate discriminant function analyses (based on a median-split of the accuracy rate) were performed to identify the predictive accuracy of (1) the model with degree of the structural connectivity, (2) the model with degree of the functional connectivity, and (3) the model with the combination of the two imaging modalities. These analyses allowed us to discern the specific potential of the modalities (structural, functional, or combination) to distinguish between both groups. Discriminatory power of the models was quantified by the resultant sensitivity, specificity, overall classification accuracy and the Wilks' lambda statistic (1 = no discriminatory power; 0 = perfect discriminatory power).

## **RESULTS**

#### **DEMOGRAPHIC CHARACTERISTICS**

Demographic features and clinical characteristics for the patients enrolled in this study are shown in **Table 2**. No significant **Table 2 | Graph metrics of the switching network of both imaging modalities, mean, and standard error for both groups.**


*Results of the two-sample-t-tests, bold values indicate significant results (p < 0.05). \*p < 0.05.*

difference in age were found between controls (*M* = 24.5 years, *SD* = 1*.*5 years) and patients (*M* = 24.9 years, *SD* = 5*.*8 years), [*t(*31*)* = −0*.*30, *p* = 0*.*77]. Controls and patients did not differ by handedness, as defined by the Edinburgh Handedness Inventory (Oldfield, 1971) (laterality quotient: TBI: mean = 81, range = 22–100; control: mean = 92; range = 60–100).

## **DIFFERENCES IN BEHAVIORAL PERFORMANCE ON THE LOCAL GLOBAL TASK (LGT)**

For reaction times of the local and global trials in the LGT, there was only a significant main effect of Cue condition [*F(*1*,* <sup>29</sup>*)* = 5*.*99, *p <* 0*.*05], indicating that global level information (597 ± 23 ms) was processed faster than information of local trials (634 ± 29 ms). For reaction times of the repeat and switch trials, there was a significant main effect of Switch condition [*F(*1*,* <sup>29</sup>*)* = 8*.*63, *p <* 0*.*01], with longer reaction times for the switching trials. Moreover, there was a significant interaction effect between the two factors Switch condition and group [*F(*1*,* <sup>29</sup>*)* = 4*.*11, *p <* 0*.*05] (**Figure 3A**). *Post hoc* (Tukey) testing revealed only a significant difference between the switch (676 ± 37 ms) and repeat (641 ± 32 ms) trials within the TBI group. The mean accuracy rate (of the switch trials) and switch cost (switch reaction time – repeat reaction time) differed significantly between the TBI patients and the controls, accuracy: [*t(*29*)* = 2*.*11, *p <* 0*.*05, switch cost: *t(*29*)* = −2*.*03, *p <* 0*.*05] with the lower accuracy scores and higher switch cost in the TBI subjects indicating poorer switching performance than in the controls (see **Figures 3B,C**).

## **GROUP DIFFERENCES IN CONNECTIVITY**

While the graph metrics of the structural connectivity were basically identical between the groups (all *p*'s *>* 0.05), the TBI group consistently showed the tighter functional connectivity as compared to controls, which manifested in a higher connection injury.

strength [*t(*31*)* = −2*.*24, *p <* 0*.*05] between the network nodes of the switching network (see **Table 2**).

## **ASSOCIATIONS BETWEEN STRUCTURAL AND FUNCTIONAL CONNECTIVITY**

Whilst each of the graph measures emphasizes a different facet of connectivity spectrum captured via graph theoretical analyses, these measures are highly inter-correlated. For example, connectivity degree was highly correlated with efficiency within the control group for both structural (*r* = 0*.*71, *p <* 0*.*01) and functional connectivity (*r* = 0*.*96, *p <* 0*.*001). Subsequently, we compared each graph metric of both structural connectivity and task-related functional connectivity and found no significant correlations (as shown in **Table 3**). Weak correlations were found within the control group (0.1–0.3) and very weak to zero values of the correlations were observed within the TBI group (*<*0.1). In nodal characteristics, we found that there was only one significant positive correlation between functional and structural connectivity of the connectivity degree of the left superior medial frontal gyrus (Brodmann area 6, *r* = 0*.*63, *p <* 0*.*01, indicated in magenta in **Figure 4**). No other significant associations were observed between functional and structural connectivity. From these results, it is clear that there is no overall agreement between functional and structural connectivity within the switching network.

#### **CONTROL CONDITION: FA-WEIGHTED GRAPH ANALYSIS**

An additional weighted graph analysis was performed in which the weight of the connections was represented by the FA value of the interconnecting WM tracts rather than the number of trajectories. Similar to the streamlines-weighted analysis, no significant group differences in structural connectivity were observed (all *p*'s *>* 0.05, see **Table S1**). Moreover, consistent with the results of the correlation analyses of the number of WM trajectories



*(Very) weak to none correlations were found within both groups.*

weighted analysis, very weak correlations were found between structural and functional connectivity for the FA-weighted analysis (see **Table S2**).

## **IDENTIFICATION OF HUBS**

To identify the hub regions, we calculated the betweenness centrality for each node of each subject's functional and structural network. Then, we calculated the mean betweenness centrality of each node by averaging across subjects for each group for each modality. For the functional connectivity, the identified hub nodes included the right dorsolateral prefrontal cortex, right insula lobe, and left dorsal premotor cortex for the TBI group (see yellow spheres in **Figure 4**). The hubs for the control group included only the right insula lobe. By contrast, for the structural connectivity, the bilateral subthalamic nuclei and right precuneus were identified as hub regions for both groups (indicated in cyan in **Figure 4**).

#### **CLASSIFICATION ACCURACY**

Discriminant function analyses were performed on the behavioral data (switch accuracy as outcome variable) and connectivity degree (of each modality separately and in combination) to obtain a more effective and significant discrimination between the two groups. All models reached a Wilks' lambda of zero, indicating that their discriminatory power was sufficient enough to correctly classify most subjects. Classification accuracy for each model, that is, how well each model (structural or functional connectivity or combination) correctly identifies group membership, was calculated. The model based exclusively on one modality was less effective in distinguishing between good and worse performers on the LGT (functional: sensitivity: 42.9%; specificity: 70.6%, overall: 58.1%; structural: sensitivity: 28.6%; specificity: 76.5%, overall: 54.8%). The discriminatory power of the model based on the combination of the degree of functional and structural connectivity was slightly higher, achieving a sensitivity of 42.9% and specificity of 76.5% in the present sample (overall classification accuracy: 61.3%).

#### **DISCUSSION**

The present study examined the relation between structural and functional connectivity in patients with TBI from a graph theoretical network perspective. We found that connectivity matrices

**regions. Upper** panel controls, **lower** panel TBI patients. Size of the ROIs (spheres) represents absolute value of the correlation coefficient. not significant. Moreover, yellow spheres indicate hubs of the functional connectivity, cyan spheres represent hubs of the structural connectivity.

obtained using both diffusion MRI and task-related fMRI showed no agreement. In addition, we observed that by combining complementary information from the different imaging modalities the accuracy in prediction of switching performance is improved.

#### **EXECUTIVE CONTROL DEFICITS IN TBI PATIENTS**

The behavioral results showed that the information of the easy conditions (repeat and global trials) was processed faster than the information from the difficult conditions (switch and local trials). Moreover, controls outperformed TBI patients on the switching task. The healthy controls completed the executive control tasks more rapidly than the TBI patients and they presented higher accuracy rates during the LGT. These results are consistent with our previous studies (Caeyenberghs et al., 2012a; Leunissen et al., 2013a) suggesting that these switching deficits in TBI patients may be related to disruptions in the cortico-subcortical connectivity, limiting the ability to enforce efficient cognitive control over action.

#### **GROUP DIFFERENCES IN CONNECTIVITY**

The topological architecture of the functional networks was significantly altered in patients with TBI. Specifically, an increase in connection strength was consistently observed in patients with TBI. Strength provides information on the variability of node local connectivity in the brain and tells whether brain nodes are all more or less connected to the same number of nodes. Strength is defined as the weighted variant of the degree and is the sum of all neighboring link weights (Rubinov and Sporns, 2010). Thus an increase in strength in TBI patients implies that their network connections are relatively denser than in controls. Increased functional brain connectivity has also been reported in our previous studies in TBI adults and children (Caeyenberghs et al., 2011, 2012a), in elderly (Heitger et al., 2013), and in patients with brain tumors (Bartolomei et al., 2006; Bosma et al., 2008). Even though it is intuitively appealing to suggest that higher connectivity strength in clinical groups may reflect functional compensation, it is clearly not the whole story. Strength is also thought to reflect neurodevelopmental exigencies of wiring cost minimization, and network topological feature optimization (Bullmore and Sporns, 2012). At the cellular level, it would be metabolically difficult in TBI to maintain an extremely high number of connections because this metabolism is simply not sustainable (Griffa et al., 2013). Thus, a higher connection strength can point to high levels of energetic cost, indicative of an overcharged network that is unbalanced in the transmission versus energy consumption trade-off, as recently observed in a longitudinal study in patients with TBI using resting-state magnetoencephalogram recordings (Castellanos et al., 2011).

The absence of group effects on graph metrics of structural connectivity tends to suggest that TBI does not strongly affect the structural connectivity or organization of the switching network. This result is not consistent with our previous study (Caeyenberghs et al., 2012b,c), in which the nodes were defined using the automated anatomical labeling atlas (AAL, Tzourio-Mazoyer et al., 2002) covering the whole brain. However, Smith et al. (2011) suggested that a data-driven approach by defining networks based only on areas showing clear task-related activation is preferable to template-based approaches in order to minimize confounds and obtain a better picture on brain connectivity.

## **CORRELATION BETWEEN STRUCTURAL CONNECTIVITY AND TASK-RELATED FUNCTIONAL CONNECTIVITY**

To the best of our knowledge, DTI and task-related fMRI have not been combined in a graph theoretical approach in TBI patients. Our results show no significant association between graph metrics of structural and functional connectivity. No significant correlations between each graph metric, including connectivity degree, connection strength, regional efficiency, and betweenness centrality, of both structural connectivity and task-related functional connectivity were found. Moreover, the hubs obtained using both techniques showed no agreement. The right dorsolateral prefrontal cortex, right insula lobe, and left dorsal premotor cortex presented as hubs in the functional connectivity. By contrast, the bilateral subthalamic nuclei and right precuneus acted as hub in both groups for the structural connectivity. In other words, the topological properties of brain functional networks cannot be solely accounted for by the properties of the underlying structural networks in this clinical group.

Although there are many studies investigating structural and functional connectivity in the same cohort of participants, most of these studies have employed a "univariate" approach, where each modality is analyzed separately. For example, Palacios et al. (2012) found a significant relationship between mean FA values of several WM tracts, including the inferior and superior longitudinal fasciculi, cingulum, uncinate, and corpus callosum, and functional activation scores for the default mode network and working memory network. Although a valuable contribution is made by the concurrent consideration given to distinct DTI data and (resting-state) fMRI data, a systematic framework of integrating them is required to achieve reasonable inferential power.

The number of studies *directly* comparing functional and structural connectivity is relatively small (for a review, see Damoiseaux and Greicius, 2009). Although these studies use slightly different functional and structural units (including the default network, a set of predefined anatomical regions, voxels, etc), they show largely convergent results, i.e., the strength of resting-state functional connectivity is positively correlated with structural connectivity strength (e.g., Hagmann et al., 2008, 2010; Skudlarski et al., 2008; van den Heuvel et al., 2008). Moreover, functional connectivity was also observed between regions when there is little or no structural connectivity, which most likely indicates functional correlations mediated by indirect structural connections (via one or more intermediate regions) (Greicius et al., 2009; Honey et al., 2009).

Both the mean connectivity matrices and the graph metrics at the nodal level show no similarity between DTI and fMRI estimates of graph metrics. This divergence between DTI and task-related connectivity may help us to understand the biological substrates of changes. Increased functional connectivity in the absence of reduction of structural connectivity would point to impaired network nodes that fail to utilize existing neuronal connections effectively. TBI might cause natural reorganization of functional connectivity giving rise to the decoupling between the two aspects of brain connectivity. While both methods assess particular aspects of brain connectivity, combining complementary information from the different imaging modalities can improve accuracy in prediction of behavioral deficits. Our results indicate that the multimodality classification approach yields significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the combination is 61.3%, which is an increase of at least 3.2% from the single modality-based methods.

## **LIMITATIONS AND CONCLUSIONS**

The number of WM trajectories was used as a weight value for structural connectivity. Other definitions of edge weight for structural connectivity, such as FA, mean diffusivity, level of myelination, and the number of fibers have previously been used (e.g., Gong et al., 2009; Li et al., 2009; Hagmann et al., 2010; van den Heuvel et al., 2010; Vaessen et al., 2012). Currently, no consensus prevails which weighting factor is the most representative measure of structural connectivity in the construction of the graphs. To test the robustness of our results, we also constructed networks weighted by FA values (see Supplemental Material). The results of those networks were comparable with those of the presented WM networks (the number of WM trajectories and binary).

Moreover, in this study, we employed a deterministic streamline tractography (Basser et al., 2000; Mori and van Zijl, 2002) to define the edges of the structural network. This is by far the most widely applied tractography method in clinical research, mainly for its simplicity, robustness and speed (Cheng et al., 2012; Griffa et al., 2013). Nevertheless, deterministic tractography is known to be particularly sensitive to noise, and the tensor model is unable to recover multiple diffusion orientations in single voxels, making it impossible to reconstruct tracts passing through brain regions with complex fiber architecture, also referred to as "crossing fibers" (Tournier et al., 2011; Jeurissen et al., 2013). Graph theoretical analyses in clinical populations would surely benefit from the use of more advanced reconstruction and tractography techniques, such as diffusion spectrum magnetic resonance imaging (DSI) (Wedeen et al., 2005, 2008) or high angular resolution diffusion imaging (HARDI) with Q-ball reconstruction of multiple fiber orientations (Tuch, 2004; Hess et al., 2006; Jeurissen et al., 2011).

As the methodologies for measuring structural and functional connectivity improve and their complementarity strengths are applied in parallel, we expect important advances in our prognostic capacities for degree of brain injury. Even though our results should be interpreted with caution, to our knowledge, this is the first report combining measures of altered functional and structural connectivity of the switching network to elucidate the mechanisms responsible for cognitive deficits after brain injury.

#### **FUNDING**

This research was supported by the Interuniversity Attraction Poles Programme initiated by the Belgian Science Policy Office (P7/11). Additional funding was obtained by the Research Fund KU Leuven (OT/11/071) and FWO Vlaanderen (G.0482.10, G.A114.11, G.0483.10; G.0721.12).

## **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/ 10.3389/fnhum.2013.00726/abstract

**Table S1 | Graph metrics of the switching network of the FA-weighted control analysis, mean, and standard error for both groups.**

**Table S2 | Results of the correlation analyses between graph metrics of structural connectivity and functional connectivity.** (Very) weak to absent correlations were found within each of both groups.

## **REFERENCES**


MS - A 3-T study. *Neurology* 69, 2136–2145. doi: 10.1212/01.wnl.0000295504. 92020.ca


**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 31 May 2013; accepted: 12 October 2013; published online: November 2013. 05*

*Citation: Caeyenberghs K, Leemans A, Leunissen I, Michiels K and Swinnen SP (2013) Topological correlations of structural and functional networks in patients with traumatic brain injury. Front. Hum. Neurosci. 7:726. doi: 10.3389/fnhum.2013.00726 This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Caeyenberghs, Leemans, Leunissen, Michiels and Swinnen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Abnormalities of functional brain networks in pathological gambling: a graph-theoretical approach

*Melanie Tschernegg1†, Julia S. Crone2†,Tina Eigenberger 3, Philipp Schwartenbeck1,2, Mira Fauth-Bühler 4,Tagrid Lemènager 4, Karl Mann4, Natasha Thon3, Friedrich M.Wurst 3† and Martin Kronbichler1,2\*†*

*<sup>1</sup> Centre for Neurocognitive Research and Department of Psychology, University of Salzburg, Salzburg, Austria*

*<sup>2</sup> Neuroscience Institute and Centre for Neurocognitive Research, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Salzburg, Austria*

*<sup>3</sup> Department of Psychiatry and Psychotherapy II, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, Salzburg, Austria*

*<sup>4</sup> Department of Addictive Behavior and Addiction Medicine, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany*

#### *Edited by:*

*Yong He, Beijing Normal University, China*

#### *Reviewed by:*

*Qingbao Yu, The Mind Research Network, USA Xia Liang, National Institute on Drug Abuse, USA*

#### *\*Correspondence:*

*Martin Kronbichler, Centre for Neurocognitive Research and Department of Psychology, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria e-mail: martin.kronbichler@sbg.ac.at*

†*Melanie Tschernegg and Julia S. Crone, as well as Friedrich M. Wurst and Martin Kronbichler have contributed equally to this work. Melanie Tschernegg and Julia S. Crone are joint first authors, and Friedrich M. Wurst and Martin Kronbichler are joint last authors.*

Functional neuroimaging studies of pathological gambling (PG) demonstrate alterations in frontal and subcortical regions of the mesolimbic reward system. However, most investigations were performed using tasks involving reward processing or executive functions. Little is known about brain network abnormalities during task-free resting state in PG. In the present study, graph-theoretical methods were used to investigate network properties of resting state functional magnetic resonance imaging data in PG. We compared 19 patients with PG to 19 healthy controls (HCs) using the Graph Analysis Toolbox (GAT). None of the examined global metrics differed between groups. At the nodal level, pathological gambler showed a reduced clustering coefficient in the left paracingulate cortex and the left juxtapositional lobe (supplementary motor area, SMA), reduced local efficiency in the left SMA, as well as an increased node betweenness for the left and right paracingulate cortex and the left SMA. At an uncorrected threshold level, the node betweenness in the left inferior frontal gyrus was decreased and increased in the caudate. Additionally, increased functional connectivity between fronto-striatal regions and within frontal regions has also been found for the gambling patients. These findings suggest that regions associated with the reward system demonstrate reduced segregation but enhanced integration while regions associated with executive functions demonstrate reduced integration. The present study makes evident that PG is also associated with abnormalities in the topological network structure of the brain during rest. Since alterations in PG cannot be explained by direct effects of abused substances on the brain, these findings will be of relevance for understanding functional connectivity in other addictive disorders.

**Keywords: fMRI, graph theory, network, connectivity, pathological gambling, reward, behavioral addiction, small world**

## **INTRODUCTION**

Patients suffering from pathological gambling (PG) show persistent gambling behavior despite negative consequences resulting in a wide-range of psychosocial impairments (Goudriaan et al., 2004). PG is classified as an impulse control disorder in DSM-IV (American Psychiatric Association, 2000), but is increasingly conceptualized as a behavioral addiction with striking similarities to substance addictions such as withdrawal symptoms and signs of tolerance (Petry, 2007). Therefore, PG (besides being renamed as disordered gambling) has been reclassified under the chapter "Addiction and related disorders" (together with substance addictions) in DSM 5 (American Psychiatric Association, 2013; Petry et al., 2013).

Most functional neuroimaging studies in PG up to date have examined brain activity abnormalities using paradigms such as reward processing, reactivity to gambling related cues, learning, decision making, and executive functions (for reviews, see Potenza, 2008, 2013; van Holst et al., 2010). In line with brain imaging studies on substance addiction, activation abnormalities in regions of the mesolimbic reward system (mainly in orbitofrontal, medial and lateral prefrontal regions, and the ventral striatum) were consistently found in patients with PG (Cavedini et al., 2002; Potenza et al., 2003; Reuter et al., 2005; Tanabe et al., 2007; Balodis et al., 2012; Choi et al., 2012; Miedl et al., 2012; van Holst et al., 2012a; Hudgens-Haney et al., 2013; Limbrick-Oldfield et al., 2013).

Brain activation differences in fronto-striatal regions in PG have also been found in executive function tasks and been commonly interpreted as reflecting impairments in cognitive control and inhibitory functions (Potenza et al., 2003) which contribute to maladaptive decision making in PG, comparable to such impairments in substance addiction (Tanabe et al., 2007).

Recent interest in functional neuroimaging studies on neuropsychiatric disorders has focused on analyzing resting state functional connectivity (Fox and Greicius, 2010; van den Heuvel and Hulshoff Pol, 2010; Menon, 2011; Xia and He, 2011;

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Buckholtz and Meyer-Lindenberg, 2012; Yu et al., 2012). Compared to task based studies, resting state data is easier to obtain and does not have to deal with group differences in task performance and compliance. Resting state connectivity studies have revealed abnormalities in a wide range of neuropsychiatric disorders such as depression, schizophrenia, attention-deficit hyperactivity disorder (ADHD), and Alzheimer's disease (for review, see Greicius, 2008).

Resting state functional magnetic resonance imaging (fMRI) data can also be used to analyze topological network properties of the brain using graph-theoretical approaches (He and Evans, 2010; Bullmore and Sporns, 2012). These approaches provide important information on the architecture of brain networks. Small-world networks are characterized by dense local interconnectivity and short path length linking individual network nodes in a short and efficient way (e.g., brain regions based on a parcellation atlas; Bullmore and Sporns, 2009). Short pathways between one node and any other node as well as a high density of connections between nearest neighbors are necessary for efficient segregation and functional integration (Salvador et al., 2005; Achard et al., 2006; Bassett and Bullmore, 2009). Network graphs are based on structural or functional data and quantify the structural and functional organization of the brain (Stam and Reijneveld, 2007).

Studies have shown that the small-world architecture and topological network properties of the brain exhibit abnormalities in neuropsychiatric disorders (e.g., He et al., 2009; Lynall et al., 2010; Zhang et al., 2011a; Cocchi et al., 2012; Cisler et al., 2013; for a review, see Xia and He, 2011). For example, patient with schizophrenia show lower cortical integration (lower amount of connections, longer path lengths, and lower clustering coefficients) in the frontal, parietal, and temporal pole (Liu et al., 2009). Zhang et al. (2011a) found global integration differences between HCs and patients with major depressive disorder and differences in nodal centrality for frontal areas, and regions of the default-mode network as well as for subcortical regions like the caudate. Furthermore, patients with obsessive-compulsive disorder (OCD) show altered functional connectivity and small worldness-properties (Zhang et al., 2011b). OCD patients demonstrate higher local clustering in the brain's cognitive control network (posterior temporal regions and the cingulate cortex). Differences in brain topology are also reported for young adults with ADHD (Cocchi et al., 2012). Functional segregation of the orbitofrontal cortex in the intrinsic brain network is enhanced in ADHD which can be linked to attentional and perceptual control deficits. Both approaches demonstrate how network analyses of the brain identify alternations directly related to symptoms of the specific mental disorder.

To date, comparatively less is known about resting state functional connectivity in addictive disorders (for review, see Sutherland et al., 2012). For example, a resting state fMRI study in chronic heroin addicts found increased functional connectivity of mesolimbic pathways and decreased functional connectivity between frontal areas (Ma et al., 2010). Two studies using graphtheoretical approaches reported differences in global small-world properties and an increased degree in a number of medial frontal, frontal, and subcortical regions in chronic abstinent heroin addicts (Liu et al., 2009; Yuan et al., 2010). These studies suggest that topological network properties may provide important insights in functional brain abnormalities in addiction. However, both of these studies on small-world properties in addiction had a relatively small sample size (11 patients in Liu et al., 2009 and 12 patients in Yuan et al., 2010). Furthermore, in studies investigating substance addiction, results may also partly reflect the effects of the abused substance on brain structure and function (Clark and Limbrick-Oldfield, 2013).

To our knowledge, not one single study on resting brain connectivity and especially on topological network properties has been conducted in PG. Two recent reports of white matter microstructural abnormalities in PG suggest that brain connectivity and network organization may be affected in PG (Joutsa et al.,2011;Yip et al., 2013). Two studies in internet addiction report functional connectivity abnormalities (Ding et al., 2013; Hong et al., 2013). Ding et al. (2013) report differences between controls and internet addicts in functional connectivity between a part of the default mode network, that is, the posterior cingulate cortex (PCC), and regions in the cerebellum, the inferior parietal lobule, and the middle temporal gyrus. Hong et al. (2013) report decreased connectivity in internet addiction between a number of cortical and subcortical regions but no significant group differences in topological network properties. The authors point out that the low number of participants (11 addicted adolescents and 11 matched HCs) could be a reason for the absence of statistically significant differences in network properties.

The aim of the present study is to provide first evidence for alterations in topological network properties using resting state fMRI and gain further insights on the neural correlates of this disorder and addictive disorders in general.

## **MATERIALS AND METHODS**

## **SUBJECTS**

This study has been approved by the local ethics committee. Nineteen patients with PG and 19 age-matched HCs with no history of neurological or psychiatric disorders participated in this study. Written informed consent was provided by all participants. All patients were seeking treatment and have been recruited at the Pathological Gambling out-patient clinic at the Department of Psychiatry and Psychotherapy II. Control subjects were recruited via advertisements and mailings.

## **BEHAVIORAL ASSESSMENT**

The German version of the short questionnaire on gambling behavior (Kurzfragebogen zum Glücksspielverhalten – KFG; Petry, 1996) and The South Oaks Gambling Screen (SOGS) by Lesieur and Blume (1987) were used to quantify gambling behavior. Furthermore, all participants completed the Alcohol Use Disorders Identification Test (AUDIT;Babor et al.,2006), the Fagerstrom Test for Nicotine Dependence (FTND; Fagerstrom, 1978), the Behavioral Inhibition Scale (BIS; Carver and White, 1994), and the Beck Depression Inventory (BDI; Beck et al., 1996).

## **fMRI DATA ACQUISITION PREPROCESSING**

Resting state fMRI was performed witha3Tesla Siemens Tim Trio MRI using a 32-channel head coil. All participants were asked

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to quietly rest in the scanner with their eyes closed and not to think of anything specific. Two-hundred and fifty T2\*-weighted images were acquired (including six dummy scans which were discarded) with a gradient echo-planar imaging sequences with the following parameters: TR: 2.25 s; TE: 30 ms; flip angle: 78◦; field of view (FOV): 192 mm × 192 mm; matrix size: 64 × 64; 36 slices; slice thickness: 3 mm; slice gap 0.3 mm; voxel size: 3 mm × 3 mm × 3 mm. Additionally, a high-resolution structural scan (sagittal T1-weighted MPRAGE sequence; TR: 2300 ms; TE: 2.91 ms; voxel size: 1 mm × 1 mm × 1.2 mm; slice thickness: 1.20 mm; FOV: 356 mm × 356 mm; 160 slices; flip angle: 9◦) and fieldmaps were obtained from each participant.

Functional magnetic resonance imaging data were preprocessed using Statistical Parametrical Mapping (SPM 8, Wellcome Department of Imaging Neuroscience, London, UK1). The following procedures were included: realignment and unwarping to compensate for movement-related artifacts; slice timing correction; co-registration of the EPI scans to the skull-stripped T1-weighted structural scan; normalization to standard stereotaxic anatomical Montreal Neurological Institute (MNI) space; smoothing with 6 mm full-width at half-maximum (FWHM) Gaussian kernel; voxel size was resampled to isotropic 3 mm × 3 mm × 3 mm.

To address the problem of confounds due to small head motion which may influence resting state connectivity, we ensured that all data sets did not exhibit movements larger than 3 mm for translations or 3◦ for rotations. Movement parameters were compared between patients and HCs using two-tailed *t*-tests. There are no significant differences in any of the six movement parameters (all *t*s < 1, all *p*s > 0.3).

For further analyses, noise correction and filtering with a bandpass filter between 0.01 and 0.1 Hz was performed with the conn toolbox (Whitfield-Gabrieli and Nieto-Castanon, 2012). For noise correction all six movement parameters and the first derivative of the time-series were removed from the data by regression. For further noise reduction, noise signals were estimated from white matter and CSF signal and removed from the data with the CompCor method (Behzadi et al., 2007) as implemented in the conn toolbox. These noise removal steps have been shown to substantially reduce noise from non-neural sources and increase the sensitivity and reliability of functional connectivity analysis (Whitfield-Gabrieli and Nieto-Castanon, 2012). No global signal regression was performed as it may result in lower reproducibility of network metrics (see Telesford et al., 2013).

#### **NETWORK CONSTRUCTION**

The Harvard–Oxford Atlas was used to extract the preprocessed fMRI data from 48 left and 48 right hemisphere cortical regions, as well as from seven left and seven right subcortical regions. Timeseries of the low-frequency BOLD signal were extracted for each of the 110 regions and averaged over all voxels in each node. For each subject, the time-series of all 110 regions were correlated with each other to create an undirected and weighted correlation matrix using Pearson correlation. These steps were performed with the conn toolbox. In contrast to partial correlation, the Pearson correlation coefficient is gaining higher values of reproducibility

1http://www.fil.ion.ucl.ac.uk

(see Telesford et al., 2013). In this network, each region represents a node with the correlation coefficients of the time-series between the different regions defining the edges resulting in a 110 × 110 connectivity matrix.

## **GRAPH ANALYSES**

Analyses of network properties were performed with the GAT<sup>2</sup> (Hosseini et al., 2012), which uses routines of the Brain Connectivity Toolbox for network metrics calculation (Rubinov and Sporns, 2010).

## *Threshold selection*

To make groups comparable, we ensured that all graphs had the same number of edges by applying an individual threshold to each correlation matrix. This was done by calculating the ratio of the number of actual connections divided by the maximum number of all possible connections described as the so-called cost of the network (connection density). Since there is still no consensus of the best threshold to be chosen, a wide range of threshold values were applied in this study (0.11 ≤ *T* ≤ 0.55 with an increment of 0.02). To verify that the selection of the threshold range is not too wide which may produce disconnected nodes and networks without small worldness features on either ends of the range, we ensured that all subjects (a) had an averaged degree value of 2\*log(*N*) with *N* = number of nodes and (b) showed network properties of small worldness with σ > 1.1 in all threshold values (Zhang et al., 2011a).

## *Network metrics*

For each threshold, the following global metrics were calculated: characteristic path length (*L*); the average of the clustering coefficient (*C*); global efficiency (*E*glob); small worldness (σ); additionally, the following local metrics were calculated for each threshold: degree (*k*); local efficiency (*E*loc); node betweenness (*N*bc); clustering coefficient (*C*).

The degree describes the number of edges linking one node to the rest of the network and gives information on how functionally connected a network is. The clustering coefficient is a measure of degree to which nodes in a graph are forming a cluster. The characteristic path length describes the number of edges between one node and any other node in a network giving an overview of the effectiveness of information transfer. The global efficiency is inversely related to the characteristic path length. The local efficiency is computed on node neighborhoods and is related to the clustering coefficient reflecting the efficiency of parallel information transfer, robustness, and fault tolerance of a network. Compared to the clustering coefficient and the characteristic path length, measures of efficiency have the advantage of including disconnected nodes with a value of 0 while the former remove them from the analysis, and therefore, may falsify the results when disconnected nodes are present (Achard and Bullmore, 2007). The node betweenness is a measure of centrality and specifies the fraction of all shortest pathways in a network that contain a given node. The so-called small worldness is the ratio of the averaged and normalized clustering coefficient (γ) to the normalized characteristic path length (λ) and assesses the small-world properties of a network characterized by high clustering coefficient and a

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<sup>2</sup>http://nnl.stanford.edu/tools.html

low characteristic path length. Small-worldness properties of a network are usually given when sigma (σ) is greater than 1.

All metrics were compared with the corresponding values obtained and averaged from 20 random networks with the same number of nodes, total edges, and degree distribution resulting in, for example, γ = *C*/*C*rand and λ = *L*/*L*rand (Maslov and Sneppen, 2002).

## *Statistical analyses*

Group comparisons of the metrics were conducted with permutation tests implemented in the GAT toolbox using the area under the curve (AUC) calculated over the threshold for each metric (Bruno et al., 2012; Hosseini et al., 2012; Singh et al., 2013). All results were corrected for multiple comparisons using a false positive correction, *p* < 1/*N* (Alexander-Bloch et al., 2010). All *p*-values corrected for multiple comparisons have been transformed and are reported as *p*cor.

Since this study is an exploratory study and the first in PG using graph theoretical approaches to assess network properties in resting state data, we also report significant results with uncorrected *p*-values.

To examine possible alterations of functional connectivity strength between regions, the correlation values of all regions were compared between both groups to find significant differences in connectivity. Analyses of functional connectivity were performed with the conn toolbox and corrected for multiple comparisons using an FDR-threshold, *p* < 0.05.

## **RESULTS**

### **SAMPLE CHARACTERISTICS**

Sample characteristics are shown in **Table 1**. No statistically significant group differences were found for sex ratio, years of education, or age. Furthermore, PG patients were comparable to HCs with respect to tobacco and alcohol consumption as assessed by the FTND and the AUDIT.

Large group differences were found in gambling behavior (KFG; SOGS). PG patients also demonstrated a larger number

**Table 1 | Sample characteristics and group differences for healthy controls (HCs) and pathological gamblers (PG) in all questionnaires.**


*\*\*\*p* < *0.001.*

of depressive symptoms as measured by the BDI and higher impulsivity as measured by the BIS.

## **GLOBAL METRICS**

Both groups showed small worldness properties with σ > 1 and there were no significant differences between groups (*p* = 0.845). Compared to random networks, both groups showed a higher averaged clustering coefficient (γ > 1) and similar values for the characteristic path length (λ ∼ 1). None of the global metrics differed between patients and controls (*E*glob: *p* = 0.646; λ: *p* = 0.797; γ: *p* = 0.817). Results for all global metrics are displayed in **Figure 1**.

## **NODAL METRICS**

At the corrected significance threshold, differences in nodal metrics were found in medial frontal regions. As can be seen in **Figure 2**, patients with PG demonstrated a decreased clustering coefficient for the left juxtapositional lobe (supplementary motor area, SMA; *p*cor = 0.038) and the left paracingulate gyrus (*p*cor = 0.044). Additionally, local efficiency for the left juxtapositional lobe (SMA) was decreased for PG patients (*p*cor = 0.022). Node betweenness was increased in the right paracingulate gyrus (*p*cor =0.05) as well as in the left paracingulate gyrus (*p*cor =0.011) in PG patients. Further differences in regional metrics at an uncorrected significance level are shown for exploratory purposes in **Table 2**.

## **FUNCTIONAL CONNECTIVITY ANALYSES**

Functional connectivity was increased in patients between frontal regions and between frontal and temporal regions (see **Table 3**). Furthermore, we found increased connectivity in patients between the left caudate and the right anterior cingulum as well as the left anterior cingulum. Additionally, the left amygdala with the left subcallosal cortex demonstrated weaker connectivity in patients than in controls.

## **DISCUSSION**

In this exploratory study, we investigated the functional network properties of patients with PG during the resting state using a graph-theoretical approach. While several studies could demonstrate functional abnormalities in PG during tasks associated with gambling, executive functions, and reward processing (Reuter et al., 2005; Tanabe et al., 2007; Balodis et al., 2012; Choi et al., 2012; Miedl et al., 2012; van Holst et al., 2012a; Hudgens-Haney et al.,2013; Limbrick-Oldfield et al.,2013; for a review, see Potenza, 2013), we are the first to show that patients with a behavioral addiction such as PG exhibit alterations in the topology of resting state networks in regions associated with reward processing and self-regulation.

Network properties at the global level showed no differences between patients and HCs. Global efficiency of information transfer and fault tolerance, for example, were similarly high in both groups. This is in line with a previous graph-theoretical study investigating the global topology of subjects suffering from internet addiction (Hong et al., 2013).

In contrast to global network properties, we found significant differences between healthy subjects and patients in network

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**local efficiency; (B) clustering coefficient; (C) node betweenness.** Error bars reflect standard deviations; SMA: supplementary motor area.

properties at the nodal level. Corrected for multiple comparisons, only medial frontal regions were affected in patients with PG. The SMA and the paracingulate cortex both showed a reduced clustering coefficient and impaired local efficiency of information transfer and fault tolerance. Furthermore, the contribution to the number of shortest paths was increased in both regions suggesting that these regions seem to adopt a more central position in the network than in healthy subjects. Note that the results for local efficiency in the paracingulate cortex and for betweenness centrality in the SMA are only tendencies, since they are not significant at a corrected level. These findings indicate that in medial frontal regions the balance between integration and segregation seem to be altered.

Medial frontal regions like the paracingulate cortex are associated with reward processing (Knutson et al., 2001; van den Bos et al., 2007; Fujiwara et al., 2009). Dysfunctions in reward processing are typical findings of previous investigations in PG (Reuter et al., 2005; Clark and Limbrick-Oldfield, 2013). The cingulate cortex is also important for gambling situations especially for specific processes of gambling (Campbell-Meiklejohn et al., 2008) like loss-chasing and quitting gambling.

Another frontal region which was found to be affected in PG is the SMA. The SMA demonstrated the same pattern of impairments as the paracingulate cortex with decreased clustering and efficiency of local information transfer but an increase in betweenness centrality.

The SMA is associated with motor execution and vigilance performance (Hinds et al., 2013) but is also involved in error detection and reward expectancy (McClure et al., 2004). Thus, the findings of this study demonstrating alterations

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*CC, clustering coefficient; DG, degree; LE, local efficiency; NB, node betweenness; p*cor*: corrected for multiple comparisons; \*Statistically significant at p* < *0.05, corrected for multiple comparisons.*

in integration and segregation of medial frontal regions may underlie specific behavioral difficulties patients with PG exhibit.

Since this was an exploratory study, we also want to discuss findings which do not exceed the threshold selected to correct for multiple comparisons.

We found a reduced fraction of path length in the left inferior frontal gyrus which also contributes to the general findings of impairments in frontal regions in gambling and addiction. A previous study showed that PG patients exhibit alterations in inferior frontal activity during gambling cue presentation (Crockford et al., 2005). The inferior frontal gyrus has been associated with executive control and response inhibition (Hampshire et al., 2010). Interestingly, while medial frontal regions showed an increase in betweenness centrality, in lateral frontal regions, this metric was decreased. This pattern may support previous findings demonstrating deficits in self-regulation and working memory in PG (Forbush et al., 2008), but enhanced involvement of the reward system.

Additionally, wefurtherfound alterations in subcortical regions at an uncorrected threshold level. The right caudate plays a more central role as a main hub for integration of information compared to HCs while the hippocampus is less involved. Again, this points out the enhanced involvement of the reward system in PG. The caudate is part of the striatum which is an important part of the mesolimbic reward system. The alterations found in network properties of the hippocampus, are in line with deficits in heroin addicts identified in a previous study (Liu et al., 2009).

This pattern of impaired topology in regions which were previously associated with the executive control network and the reward system (Potenza et al., 2003; Reuter et al., 2005; Tanabe et al., 2007; Limbrick-Oldfield et al., 2013) is complemented by our findings of increased functional connectivity of fronto-striatal

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**Table 3 | Significant differences between pathological gamblers (PG) and healthy controls (HC) in functional connectivity.**

*\*Statistically significant at FDR-corrected threshold, p* < *0.05.*

circuits and between frontal regions. Note that almost all differences in connectivity in which patients with PG exhibit higher functional connectivity than controls affect regions associated with the reward system. This is in line with previous studies finding alterations in functional connectivity between medial frontal and subcortical regions in addiction (Ma et al., 2010).

One previous study investigating network properties in behavioral addiction was performed in subjects with internet addiction (Hong et al., 2013). This study did not identify any alterations in the network topology in addicts. However, the authors emphasize that the non-significant results may be due to the small sample size. Additionally, two studies in heroin addiction also focused on graph-theoretical methods to investigate network properties (Liu et al., 2009; Yuan et al., 2010). They report dysfunction in several frontal regions including the cingulate cortex and the SMA, and subcortical regions including the striatum and the hippocampus. Our findings endorse the association of addictive behavior with alterations in functional connectivity and network topology during resting state in these specific frontal and striatal regions. This finding is of high relevance since previous investigations showing abnormalities in brain topology focused on addiction involving substance abuse. Thus, conclusions drawn from these studies are confounded by the neurotoxic effects of the abused substances (Clark and Limbrick-Oldfield, 2013). With this study, we confirm that abnormalities in network properties can also be found in behavioral addiction and therefore cannot solely be explained by effects of drugs on brain connectivity.

There are no available standards for a uniform application of graph theories at present (Bullmore and Sporns, 2009). One methodological limitation when investigating network topology with a graph-theoretical approach, for example, is the choice of thresholds. There are several possibilities to select the threshold and no golden standard has been defined yet. When comparing groups it should be ensured that each network has the same number of edges. However, the problem with a global threshold is that it may lead to disconnected graphs. Comparing network properties of one graph with the other is problematic if the one is connected at a given node and the other is disconnected. To address this problem, we ensured that the averaged degree is above the selected threshold and all subjects show small-world properties. Furthermore, we also investigated the global and local efficiency in addition to the clustering coefficient and the characteristic path length. These metrics have some methodological advantages when dealing with disconnectedness (Achard and Bullmore, 2007).

Another limitation is the wide range of thresholds selected. Depending on the range, results differ between studies and make comparison of findings and their interpretation difficult. However, we have implemented strategies which have been successfully applied in previous studies using graph-theoretical approaches (e.g., Zhang et al., 2011a; Bruno et al., 2012). Since this is a first exploratory study in PG using graph-theoretical analyses of resting state fMRI data, further research must be conducted to confirm these results.

Moreover, Zalesky et al. (2012) has shown that the type of randomization (topology randomization, correlation matrix randomization, or time-series randomization) influences the normalization process of the metrics. For a low density of around 7% the authors identified a discrepancy of approximately 60% when applying topology randomization compared to correlation matrix randomization to estimate the normalized clustering coefficient. In addition, using correlation matrix randomization to normalize characteristic path length may lead to longer path lengths due to the randomization of hub nodes since the degree distribution is not preserved. These limitations affect especially low density thresholds around 7% and are evident when looking at absolute small-world properties of the networks in each group. However, they are less essential for the comparison of network metrics between groups which is the focus of this study.

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This first study in PG using graph-theoretical approaches to investigate network properties demonstrates that alterations of regions associated with the reward system and executive functions are not only present in task-related activity but also during rest. Alterations are reflected in a decrease in segregation and an increase of information integration in specific regions of the reward system.

This may contribute to the ongoing discussion whether PG is characterized by a hyper- or a hypoactive reward system (Hommer et al., 2011; van Holst et al., 2012b). Furthermore, our results suggest deficits of integration in regions associated with executive functions. These alterations may

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## **ACKNOWLEDGMENTS**

Melanie Tschernegg was supported by the Doctoral Collage"Imaging the Mind" of the Austrian Science Foundation (FWF-W1233). Further support was provided by grants of the Austrian Science Foundation (FWF P-23916-B18) and the Scientific Funds of the Paracelsus Medical University (E-10/12/062-KRO) to Martin Kronbichler. We want to thank Eva Reiter for her help with data acquisition.

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obsessive-compulsive disorder. *J. Psychiatry Neurosci.* 36, 23–31. doi: 10.1503/jpn.100006

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 05 June 2013; accepted: 10 September 2013; published online: 27 September 2013.*

*Citation: Tschernegg M, Crone JS, Eigenberger T, Schwartenbeck P, Fauth-Bühler M, Lemènager T, Mann K, Thon N, Wurst FM and Kronbichler M (2013) Abnormalities of functional brain networks in pathological gambling: a graph-theoretical* *approach. Front. Hum. Neurosci. 7:625. doi: 10.3389/fnhum.2013. 00625*

*This article was submitted to the journal Frontiers in Human Neuroscience.*

*Copyright © 2013 Tschernegg, Crone, Eigenberger, Schwartenbeck, Fauth-Bühler, Lemènager, Mann, Thon, Wurst and Kronbichler. This is an open-access article distributed under the terms of the* *Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, providedthe original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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