Edited by: André Schmidt, King’s College London, UK
Reviewed by: Frederic Haesebaert, Centre de recherche de l’Institut Universitaire en Santé Mentale de Québec, Canada; Andrew James Greenshaw, University of Alberta, Canada
Specialty section: This article was submitted to Neuroimaging and Stimulation, a section of the journal Frontiers in Psychiatry
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Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.
Over the last decade, research on the human brain has experienced an important shift in paradigm; the functional investigation of neuronal activity has moved from studying local mechanisms toward large-scale network organization. Unavoidably, this change in the examination of neural connectivity has reached the field of psychiatry. Until recently, most connectivity studies in psychiatric patients were predominantly carried out using functional magnetic resonance imaging (fMRI). The findings from these studies generally indicate the presence of structural and/or functional abnormalities linked to the diseases [e.g., Ref. (
A parallel stream of research explores the physical connections between brain regions by assessing structural connectivity with magnetic resonance imaging (MRI) using diffusion tensor imaging (DTI) and fractional anisotropy. These techniques allow the examination of white matter integrity and fiber tract organization and are thereby able to reveal anatomical disruptions of long-range structural connections (
When small neighboring neuronal populations synchronize their oscillations, local assemblies are forged, and coupling among these small assemblies can bridge distant areas (creating long-range connections) (
Although EEG permits the probing of large-scale networks, with high temporal resolution, MEG has a number of advantages. For instance, the magnetic signal that is captured by MEG is less distorted by brain tissue and skull than the electrical field detected by EEG. In addition, MEG source reconstruction methods can provide valuable spatial information to better characterize neural network modulations. Finally, in the context of clinical research, and more specifically in psychiatry, the fast and easy setup of the MEG system is likely to be less unnerving for patients than the lengthy procedure of EEG. Taken together, exploration of the potential of MEG in psychiatry is an important endeavor that could lead to better understanding of psychopathology. Further details about the technical aspects of MEG have been overviewed elsewhere [e.g., Ref. (
Despite being the object of a thriving field of clinical research, the investigation of intrinsic neural network alterations in psychiatric illnesses is in its early days and is predominantly conducted using fMRI or EEG. The recent contributions and future promise of MEG in this field are not fully recognized and valued. In this article, we review recent findings in MEG resting-state connectivity within two mood disorders: major depressive disorder (MDD) and bipolar disorder (BD). Most importantly, this review provides a critical assessment of currently employed methods and outlines important limitations that need to be considered in future resting-state MEG studies of mood disorders.
When subjects are asked to lay or sit still in an MRI, PET, EEG, or MEG setup and to let their minds wander, the activity that arises is one that speaks of the fundamental organization—or disorganization—of the brain [e.g., Ref. (
The literature on neural network connectivity, particularly graph theory, suggests that the purpose of a node is guided by how it is connected to other nodes in a given network and that its function is a consequence of the action of its integral network (
Three types of connectivity are generally examined: anatomical, functional, and effective. First, anatomical connectivity pertains to the physical connection between brain regions. It is typically examined using MRI-based DTI analysis of white matter, axonal, and tracts (
In this review, we focus on functional connectivity abnormalities across MDD and BD.
It has been proposed that local and long-range neural synchrony patterns speak of the inherent organization of the brain (
Specifically,
Although both local and long-range synchrony can be measured at sensor and source levels during EEG and MEG studies, caution should be taken when measuring the statistical or coherence differences between two recording (sensor) sites. Indeed, what may first be thought to be coordinated time series reflecting connectivity between two brain areas (
The following section provides a brief and non-exhaustive multimodal overview of the rapidly increasing body of neuroimaging research that links psychiatric disorders with pathological alterations in neuronal connectivity, in line with previous work that overviewed network connectivity in SZ (
With over 100,000 scientific papers on PubMed, depression is the most common and the most studied psychiatric illness in humans. MDD is characterized by features such as low mood and/or a loss of interest in daily activities for an extended amount of time and, typically, involves ruminative, self-referential thoughts (
Although a number of impactful task-based studies have explored alterations in oscillatory synchronizations [e.g., Ref. (
Given the DMN’s role in self-referential behaviors (
Moreover, a number of original studies and reviews have reported atypical functional connectivity within areas key to emotional processing. Indeed, limbic regions [amygdala, insula (
All in all, within and between network connectivity of the DMN and limbic system appear to be altered in MDD patients, particularly with respect to projections involving the subgenual ACC. The atypical resting-state organization of their brain appears to correlate with their cognitive and emotional symptoms (
Examinations of local synchronizations in depressed populations have consistently found low-frequency bands (<20 Hz) to display enhanced power and coherence across most brain regions (
Similar to the findings from fMRI, EEG studies have found disruptions and asymmetrical connectivity patterns within the frontal lobe of MDD patients in theta (5–7 Hz) and alpha (8–13 Hz) frequency bands compared with healthy control subjects (
The observations discussed above are consistent with insights achieved using deep brain stimulation (DBS) in MDD patients. Clinical trials with DBS have targeted the overactive subgenual ACC and the thalamocortical pathway, for the treatment of severe depression (
Bipolar disorder is a functionally debilitating disorder. The main categories of BD are BD-I and BD-II, which are characterized by a combination of manic episodes and depression and by hypomania episode(s) and depressive symptoms, respectively (
Task-based fMRI/EEG studies that have explored connectivity patterns in BD population have found synchronization alterations that differentiate them from healthy individuals (
Multiple fMRI studies have attempted to untangle the connectivity anomalies observed in these patients, with somewhat contradicting results. Indeed, depending on the analytical method used to extract connectivity, whether it be independent component analysis (ICA) or seed-based/ROIs, wavering conclusions have been made on BD patients’ corticolimbic connectivity patterns [e.g., Ref. (
Although DMN activity has been closely linked to mind wandering and interoceptive thoughts (
With respect to the ventrolateral PFC, contradicting findings have been reported, with some estimating enhanced connectivity with the amygdala (
All in all, models proposed by many researchers suggest that disruption between these keys areas, particularly connectivity involving the amygdala, underlie the occurrence of manic symptoms and inefficient emotional management (
Electroencephalography analyses of local synchronization have predominately reported group differences in the frontal region and the cingulate cortex. Examinations in modulations of local oscillatory behavior in the frontal cortex show enhanced power in alpha (8–13 Hz) (
With respect to long-range connectivity, an EEG investigation noted decreased connectivity in the alpha (8–12 Hz) frequency band within frontocentral and centroparietal neural network connections of patients compared with healthy controls (
Taken together, the fMRI and EEG findings in MDD and BD patients indicate that these populations may have difficulties processing and transferring information economically that can be detectable as connectivity anomalies between and within resting-state networks. Research into pathological alterations of resting-state activity provides critical insights into large-scale network dynamics, which complement key findings that continue to emerge from task-based studies (
Although still in its early days, MEG has led to important clinical insights in numerous brain disorders and has become a promising tool for clinical and translational research in psychiatry (
A hierarchical approach was taken to isolate the keywords that were most appropriate for this review. First, we began with “MEG + condition,” where condition was the general term for either MDD (i.e., depression) or BD (i.e., bipolar). We then refined this search by adding the term “resting” to capture studies that included a resting-state paradigm. Finally, we used the term “connectivity” to capture publications that also evaluated long-range synchronization, as resting state alone, could potentially refer to power analyses. Various combinations of these terms, as well as cross-referencing, were employed to ensure that all studies investigating MEG resting-state local power and long-range synchrony in MDD and BD patients were considered.
Here, a PubMed search using the key words “MEG + connectivity + depression” resulted in 16 hits, 12 of which were, however, unrelated to our topic of interest. Another search of the keywords “MEG + depression + resting” yielded 12 studies, with only 2 studies being relevant additions to this review. Among the articles that were found, the final count of scientific articles included in this article is 5.
The recent article by Jiang et al. (
Moreover, Li et al. (
Table
Reference | Frequency range | Methods | Patients | Controls | Main findings |
---|---|---|---|---|---|
( |
Delta: 2–4 Hz |
Frontal alpha asymmetry and voxel-based partial correlation to examine connectivity in prefrontal-thalamic circuit (based on PET) |
30 MDD received rTMS, 6 males |
50 controls |
MDD appeared to have impaired prefronto-thalamic functional connections compared to controls. rTMS resolved this pattern in those who responded to treatment after 2 weeks of treatment at 10 Hz in their dorsolateral PFC |
( |
14–30 Hz | Correlation |
33 MDD |
19 controls |
Patients had reduced correlations between the subgenual ACC and hippocampus in a network with primary nodes in the precentral and middle frontal gyri. Patients showed increased correlations between insulotemporal nodes and amygdala compared to controls |
( |
14–30 Hz | Correlation |
13 MDD |
18 controls |
Patients displayed enhanced connectivity between insulotemporal areas and amygdala that were reduced to normal levels after ketamine treatment |
( |
Delta: 2–4 Hz |
Magnitude-squared coherence. Seed: dorsolateral PFC |
5 MDD received TMS, 1 non-responder | n/a | Symptom improvement by 10 Hz rTMS increased connectivity between dorsolateral PFC and amygdala, and dorsolateral PFC and pregenual ACC in delta band. rTMS decreased connectivity between dorsolateral PFC and subgenual ACC |
In a follow-up MEG study (
Pathak et al. (
Finally, Li and colleagues’ (
The main finding of these MEG papers speak of altered long-range connectivity between the DMN and the CEN. In particular, the resting-state MEG literature supports previous fMRI studies that have demonstrated the implication of the subgenual cingulate cortex, the dorsolateral PFC, and the thalamus in illness severity and symptomatology in MDD [e.g., Ref. (
This section explores task-based MEG studies that corroborate connectivity results from resting-state MEG studies in MDD.
A number of studies have investigated the long-range connectivity patterns that emerge during affective and cognitive tasks in psychiatric patients. Among their findings, the diminished long-range synchronization between the dorsolateral PFC and the amygdala, observed recently in resting-state MEG by Pathak et al. (
Other connectivity alteration in MDD patients have also been noted in task-based MEG studies. Specifically, a measure of wavelet coherence has shown enhanced connectivity between the ACC and the amygdala in the gamma (30–48 Hz) and in the delta (below 4 Hz) frequency bands (
Finally, Salvadore et al. (
An important limitation that connectivity studies might display is that of being conducted at sensor level rather than source level. Although most of the reported MDD studies were conducted in source space, the article by Li et al. (
Next, exploring specific frequency bands can also be a limitation. Indeed, in the article by Nugent et al. (
Furthermore, the metric of coherence and correlations can raise questions about the spatial accuracy of the long-range synchronizations observed due to potential field spread effect (see
Finally, small sample size can be problematic in the interpretation of findings. In their recent study, Nugent et al. (
A PubMed search of the key words “MEG + connectivity + bipolar” resulted in no findings. However, a search of the key words “MEG + bipolar + resting” yielded three studies, one of which was an EEG study (already discussed in fMRI Resting-State Connectivity Findings in MDD).
Al-Timemy et al. (
Table
Reference | Frequency range | Methods | Patients | Controls | Main findings |
---|---|---|---|---|---|
( |
Delta: 2–4 Hz |
Similarity index; using 11 sensors from the frontal lobe |
10 euthymic BD-I |
10 controls: 5 males |
Increased synchronization of δ frequency oscillations and decreased synchronization of β frequency oscillations in the frontal lobe in BD compared to controls |
To the best of our knowledge, no MEG task-based study has explored long-range synchronizations in BD. However, EEG resting-state studies, such as one by Kim et al. (
Although of important value, the resting-state MEG study by Chen et al. (
The present overview shows that MDD individuals have enhanced connectivity patterns within nodes of the DMN (as evidenced by resting-state fMRI and EEG studies), as well as altered connectivity between areas of the DMN and the limbic system (particularly between subgenual ACC and hippocampus, as evidenced by fMRI and MEG). Moreover, there is evidence for hypoactivity between regions of the CEN and the limbic system (particularly dorsolateral PFC and amygdala, as observed through fMRI and MEG), alterations between the nodes of the DMN and the CEN (particularly hyperconnectivity between the subgenual ACC and the dorsolateral PFC, as noted using fMRI, EEG, and MEG) and, finally, hyperconnectivity between the insula and the limbic system (amygdala, as noted by fMRI and MEG studies). Overall, projections from and to the subgenual ACC, as well as the dorsolateral PFC, appear to be critical in the treatment and expression of depressive symptoms. Figure
Most of the work in BD arises from resting-state fMRI research, which has observed altered connectivity between areas of the DMN and the limbic system (notably between the mPFC and the amygdala), hyperconnectivity between nodes of the DMN and CEN (particularly between the medial PFC and dorsal/ventrolateral PFC), and hyperconnectivity between the ventrolateral PFC and the amygdala. Across all three neuroimaging modalities of fMRI, EEG, and MEG, altered (mainly decreased) connectivity in the PFC has been noted. This hypoactivity in the frontal lobe has been thought to be due to the presence of psychotic symptoms in a proportion of BD patients (
The assessment of resting-state connectivity patterns in psychiatric populations with MEG is a fairly new field still faced with substantial technical challenges. This section addresses the most important methodological issues that need to be understood and taken into account. Most importantly, in addition to delineating current limitations, we provide suggestions and methodological recommendations to help the field move forward.
In contrast to functional connectivity estimations in fMRI, where the primary measure is straightforward correlation between the BOLD time series in various voxels or ROIs, MEG connectivity estimation is a more complex endeavor (
Generally speaking, most of the commonly used interaction measures (e.g., coherence or phase-locking value) face limitations caused by linear mixing. This is a problem often referred to as field spread (MEG) or volume conduction (EEG) when dealing with sensor level data, or signal leakage when exploring source-level data (
Although important information can be gained from combining sensor-level MEG data with advanced connectivity metrics, source-space network assessments are key to simultaneously identify the neuroanatomical substrates and functional role of the involved networks. Moreover, source-level estimation is critical to help bridge the gap between MEG and fMRI findings in the field of psychiatry. Although most of the studies reviewed here use source-space connectivity measurements (
A challenge that is not yet entirely resolved is that of the robustness and consistency of MEG-based resting-state estimation over time and across participants. Recent research has addressed the reliability of MEG resting-state connectivity metrics (
Comparisons between MEG resting-state connectivity patterns obtained in controls and patients are faced with additional difficulties caused by the pathological conditions. Increased head and body movement artifacts, eye blinks, and saccades in patient populations are not uncommon, and they all lead to poorer data quality compared with data acquired in healthy subjects. For equal MEG scanning durations, artifact rejection techniques will ultimately lead to less data being preserved for the patients, which may in turn yield lower SNR in patient data compared with controls. These differences in SNR must be avoided, or at least controlled for, since they will lead to differences in functional interaction patterns that may have nothing, or little, to do with the pathology at hand and rather reflecting differences in data quality. Minimizing data rejection through the use of artifact correction techniques such as ICAs could be of interest, although the differential application of ICA to the two groups (i.e., more extensive in the case of patient data) could also lead to differences that may bias connectivity findings and data interpretation. To address artifact-related SNR discrepancies between patients and controls, we recommend planning to acquire more data in patients from the start of the project or alternatively to use a subsample of data from the controls to achieve comparable SNR across the two groups.
A second, often overlooked, issue is that pathological changes in local signal amplitude can affect the estimation of long-range connectivity in patients and thereby lead to group differences that are in fact a reflection of inadequate or unreliable coupling estimation. This can occur because lower signal amplitudes (that equate noise levels) will
In both healthy and pathological populations, age has been shown to be an important variable that can affect brain structure, cognitive functions, and connectivity patterns (
Age of illness onset is also an important factor to take into consideration as early/preadolescence onset of psychopathologies typically correlate with worse prognosis and more severe clinical symptoms (
Medication is also a variable for that has substantial effects on the neural network of psychiatric patients, with different types of pharmacotherapies impacting connectivity in distinct ways (e.g., selective seratonergic vs noradregenic reuptake inhibitor) (
Finally, it is important to note that non-medication drugs, such as nicotine and caffeine, also appear to alter resting-state networks in healthy and clinical populations. Evidence of this effect has been reported using fMRI (
This review is the first of its kind to examine the literature’s findings on resting neural network connectivity patterns of BD and major depression disorder, based on MEG studies. A global analysis of current scientific papers demonstrate that the two illnesses display functional abnormalities that affect the way information is integrated, locally, and transferred from one brain region to another through long-range connections. Moreover, this review illustrated that resting-state neuroimaging paradigms are a useful way to access the disorganized brains of individuals with psychopathologies. Finally, although still in its early days, MEG carries the potential to significantly advance our understanding of large-scale network alterations associated with psychiatric disorders (
Overall, the PFC, in particular the mPFC which is at the core of the DMN and of social cognition, is affected across both psychopathologies. Given that this brain region is one of the last to develop during neurodevelopment (
An explanation for the imbalance in the amount of scientific papers published in these two mood disorders could be that depression is the mental illness affecting the largest percentage of individual worldwide in its various forms (e.g., MDD, postpartum depression, seasonal onset depression), while BD is symptomatically more complex and heterogeneous. Thus, when interpreting neuroimaging results, researchers should consider the effect of additional psychological factors, such as manic/cyclic mood and history of psychosis (
Resting-state MEG is expected to continue gaining momentum in psychiatry. One promising application is its ability to enhance the understanding of how neuromodulation (e.g., rTMS) can change the neural circuitry of mood disorder patients. Indeed, there is cumulating evidence that rTMS might reduce symptoms in MDD patients (
Our recommendations for future studies are to further explore the potential of examining functional and effective neural network connectivity in psychiatric disorders using a combination of tools, multimodal imaging techniques, yet employ common terminology (
GA and KJ designed and wrote the review; GA, A-SH, EC, VM, DA, TT, and KJ conducted the literature research, the critical assessment thereof, and comprehensive proofreading of the manuscript.
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.
GA was supported in part by a CERNEC scholarship. EC was supported in part by a PhD scholarship from Ecole Doctorale Inter-Disciplinaire Sciences-Santé, Lyon, France, and by PhD funding from the Natural Sciences and Engineering Research Council of Canada (NSERC). VM was supported by NSERC Undergraduate Student Research Awards. KJ acknowledges funding from the Canada Research Chairs program and NSERC Discovery Grant (RGPIN-2015-04854).
BD, bipolar disorder; EEG, electroencephalography; fMRI, functional magnetic resonance imaging; MDD, major depressive disorder; MEG, magnetoencephalography; PFC, prefrontal cortex; CEN, central executive network; ACC, anterior cingulate cortex; DBS, deep brain stimulation.