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Brain Functional Modeling, What Do We Measure with fMRI Data?

Abstract : The description of specific circuits in networks should allow a more realistic definition of dynamic functioning of the central nervous system which underlies various brain functions. After introducing the programmed and acquired networks and recalling the concepts of functional and effective connectivity, we presented biophysical and physiological aspects of the BOLD signal. Then, we briefly presented a few data-driven and hypothesis-driven methods; in particular we described structural equation modeling (SEM), a hypothesis-driven approach used to explore circuits within networks and model spatially and anatomically interconnected regions. We compared the SEM method with an alternative hypothesis-driven method, dynamic causal modeling (DCM). Finally, we presented independent components analysis (ICA), an exploratory data-driven approach which could be used to complete the directed brain interactivity studies. ICA combined with SEM/DCM may allow extension of the statistical and explanatory power of fMRI data.
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Submitted on : Thursday, February 16, 2017 - 2:56:20 PM
Last modification on : Monday, May 25, 2020 - 3:08:02 PM

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Giovanni de Marco, Bernard Devauchelle, Patrick Berquin. Brain Functional Modeling, What Do We Measure with fMRI Data?. Neuroscience Research, Elsevier, 2009, 64 (1), pp.12-19. ⟨10.1016/j.neures.2009.01.015⟩. ⟨hal-01469479⟩

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