Fiber-centered granger causality analysis

Xiang Li, Kaiming Li, Lei Guo, Chulwoo Lim, Tianming Liu

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

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.

Original languageEnglish
Pages (from-to)251-259
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
DOIs
StatePublished - 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 18 Sep 201122 Sep 2011

Keywords

  • Brain State
  • DTI
  • fMRI
  • Granger Causality Analysis
  • Time Series

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