GPU accelerated extraction of sparse Granger causality patterns

Dushyant Sahoo, Nicolas Honnorat, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538636367
StatePublished - May 23 2018
Externally publishedYes
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States


  • GPU
  • Granger Causality
  • Parallel Computing
  • Proximal Alternating Linearized Minimization

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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