This article is part of the Research Topic New Concepts in Brain Networks

Review ARTICLE

Front. Syst. Neurosci., 18 January 2012 | doi: 10.3389/fnsys.2011.00104

Effective connectivity modeling for fMRI: six issues and possible solutions using linear dynamic systems

Jason F. Smith1*, Ajay Pillai1, Kewei Chen2,3,4 and Barry Horwitz1
  • 1 Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA
  • 2 Department of Mathematics and Statistics, Arizona State University, Tempe, AZ, USA
  • 3 Positron Emission Tomography Center, Banner Good Samaritan Medical Center, Tempe, AZ, USA
  • 4 Banner Alzheimer’s Disease Institute, Banner Good Samaritan Medical Center, Tempe, AZ, USA
  • 5 Arizona Alzheimer’s Consortium, Phoenix, AZ, USA

Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need to be addressed. The issues are discussed within the framework of linear dynamic systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a “node” in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an “instantaneous” connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis.

Keywords: effective connectivity, dynamic systems, fMRI

Citation: Smith JF, Pillai A, Chen K and Horwitz B (2012) Effective connectivity modeling for fMRI: six issues and possible solutions using linear dynamic systems. Front. Syst. Neurosci. 5:104. doi: 10.3389/fnsys.2011.00104

Received: 08 July 2011; Accepted: 30 December 2011;
Published online: 18 January 2012.

Edited by:

Robert Turner, Max Planck Institute for Human Cognitive and Brain Sciences, Germany

Reviewed by:

Stelios M. Smirnakis, Baylor College of Medicine, USA
Vince D. Calhoun, University of New Mexico, USA
Edward T. Bullmore, University of Cambridge, UK

Copyright: © 2012 Smith, Pillai, Chen and Horwitz. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

*Correspondence: Jason F. Smith, Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, Room 5D39, 10 Center Drive, Bethesda, MD 20892-1407, USA. e-mail: smithjas@nidcd.nih.gov

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