Fiber-centered granger causality analysis

Authors:
Xiang Li;Kaiming Li;Lei Guo;Chulwoo Lim;Tianming Liu
Affiliations:
Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA;Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA and School of Automation, Northwestern Polytechnic University, Xi'an, China;School of Automation, Northwestern Polytechnic University, Xi'an, China;Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA;Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2011

Citing 2
Cited 0

Cortical surface based identification of brain networks using high spatial resolution resting state FMRI data

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Fiber-centered analysis of brain connectivities using DTI and resting state FMRI data

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

Granger causality analysis (GCA) has been well-established in the brain imaging field. However, the structural underpinnings and functional dynamics of Granger causality remain unclear. In this paper, we present fibercentered GCA studies on resting state fMRI and natural stimulus fMRI datasets in order to elucidate the structural substrates and functional dynamics of GCA. Specifically, we extract the fMRI BOLD signals from the two ends of a white matter fiber derived from diffusion tensor imaging (DTI) data, and examine their Granger causalities. Our experimental results showed that Granger causalities on white matter fibers are significantly stronger than the causalities between brain regions that are not fiber-connected, demonstrating the structural underpinning of functional causality seen in resting state fMRI data. Cross-session and cross-subject comparisons showed that our observations are reproducible both within and across subjects. Also, the fiber-centered GCA approach was applied on natural stimulus fMRI data and our results suggest that Granger causalities on DTI-derived fibers reveal significant temporal changes, offering novel insights into the functional dynamics of the brain.