GPU accelerated extraction of sparse Granger causality patterns

Resting-state functional MRI, which provides a means to estimate the entire brain functional connectivity, has recently received a considerable amount of interest. This modality is increasingly being used to study functional connectivity dynamics, in particular with the aim of extracting individual...

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Bibliographic Details
Published inProceedings (International Symposium on Biomedical Imaging) pp. 604 - 607
Main Authors Sahoo, Dushyant, Honnorat, Nicolas, Davatzikos, Christos
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
Brain modeling
Computational modeling
Extrapolation
Functional magnetic resonance imaging
GPU
Granger Causality
Graphics processing units
Parallel Computing
Proximal Alternating Linearized Minimization
Sociology
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ISSN1945-8452
DOI10.1109/ISBI.2018.8363648

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Summary:Resting-state functional MRI, which provides a means to estimate the entire brain functional connectivity, has recently received a considerable amount of interest. This modality is increasingly being used to study functional connectivity dynamics, in particular with the aim of extracting individual biomarkers. However, the large amount of noise in the individual fMRI scans poses major challenges. In this work, we propose to analyze fMRI dynamics by extracting Granger causality patterns shared across subjects. This approach allows to capture individual brain organization while extracting population causality patterns which are more robust with respect to noise. We introduce an efficient method for the extraction of shared causality patterns, and we demonstrate its performance by processing the rs-fMRI scans of the hundred unrelated Human Connectome Project subjects.
ISSN:1945-8452
DOI:10.1109/ISBI.2018.8363648