4 March 2011 Characteristics of voxel prediction power in full-brain Granger causality analysis of fMRI data
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Proceedings Volume 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging; 796502 (2011); doi: 10.1117/12.878311
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
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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KEYWORDS
Brain

Data modeling

Functional magnetic resonance imaging

Stochastic processes

Brain mapping

Machine learning

Analytical research

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