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    Neuroimage. 2011 Sep 15;58(2):296-302. Epub 2009 Sep 25.

    The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution.

    Source

    Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Postbus 616, 6200MD Maastricht, The Netherlands. a.roebroeck@maastrichtuniversity.nl

    Abstract

    Functional magnetic resonance imaging (fMRI) is increasingly used to study functional connectivity in large-scale brain networks that support cognitive and perceptual processes. We face serious conceptual, statistical and data analysis challenges when addressing the combinatorial explosion of possible interactions within high-dimensional fMRI data. Moreover, we need to know, and account for, the physiological mechanisms underlying our signals. We argue here that (i) model selection procedures for connectivity should include consideration of more than just a few brain structures, (ii) temporal precedence - and causality concepts based on it - are essential in dynamic models of connectivity and (iii) undoing the effect of hemodynamics on fMRI data (by deconvolution) can be an important tool. However, it is crucially dependent upon assumptions that need to be verified.

    Copyright © 2009 Elsevier Inc. All rights reserved.

    PMID:
    19786106
    [PubMed - indexed for MEDLINE]

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