4 March 2011 Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data
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Abstract
Functional neuroimaging research is moving from the study of "activations" to the study of "interactions" among brain regions. Granger causality analysis provides a powerful technique to model spatio-temporal interactions among brain regions. We apply this technique to full-brain fMRI data without aggregating any voxel data into regions of interest (ROIs). We circumvent the problem of dimensionality using sparse regression from machine learning. On a simple finger-tapping experiment we found that (1) a small number of voxels in the brain have very high prediction power, explaining the future time course of other voxels in the brain; (2) these voxels occur in small sized clusters (of size 1-4 voxels) distributed throughout the brain; (3) albeit small, these clusters overlap with most of the clusters identified with the non-temporal General Linear Model (GLM); and (4) the method identifies clusters which, while not determined by the task and not detectable by GLM, still influence brain activity.
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Rahul Garg, Guillermo A. Cecchi, A. Ravishankar Rao, "Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data", Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 796502 (4 March 2011); doi: 10.1117/12.878311; https://doi.org/10.1117/12.878311
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