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Proceedings Paper

A causal brain network estimation method leveraging Bayesian analysis and the PC algorithm
Author(s): Gemeng Zhang; Aiying Zhang; Vince D. Calhoun; Yu-Ping Wang
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Paper Abstract

Estimating causal brain networks from fMRI data is important in understanding functional human brain connectivity, and current causality estimation methods face various challenges such as high dimensionality and expensive computation. The joint estimation of causal networks between groups shows promising potential to investigate group-related brain connectivity variations. In this paper, we proposed a joint causal brain network estimation method by adding a prior to the popular PC algorithm1 (by Peter Spirtes and Clark Glymour). The prior is obtained through a fast joint Bayesian analysis (FIBA) and plays a role as a screening step, significantly reducing computational burden of PC algorithm. Moreover, the FIBA also enables us to efficiently address the high dimensionality problem of fMRI data. The experimental results from both simulation data sets and real fMRI data demonstrate the accuracy and efficiency of the proposed method. The specific brain connections identified in schizophrenia patients extend previous research and shed light on other studies of mental disorders.

Paper Details

Date Published: 28 February 2020
PDF: 6 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170X (28 February 2020); doi: 10.1117/12.2549295
Show Author Affiliations
Gemeng Zhang, Tulane Univ. (United States)
Aiying Zhang, Tulane Univ. (United States)
Vince D. Calhoun, The Ctr. for Translational Research in Neuroimaging and Data Science (TReNDS) (United States)
Yu-Ping Wang, Tulane Univ. (United States)

Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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