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Proceedings Paper

A hypergraph learning method for brain functional connectivity network construction from fMRI data
Author(s): Li Xiao; Julia M. Stephen; Tony W. Wilson; Vince D. Calhoun; Yu-Ping Wang
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Paper Abstract

Recently, functional magnetic resonance imaging (fMRI)-derived brain functional connectivity networks (FCNs) have provided insights into explaining individual variation in cognitive and behavioral traits. In these studies, how to accurately construct FCNs is always important and challenging. In this paper, we propose a hypergraph learning based method, which constructs a hypergraph similarity matrix to represent the FCN with hyperedges being generated by sparse regression and their weights being learned by hypergraph learning. The proposed method is capable of better capturing the relations among multiple brain regions than the traditional graph based methods and the existing unweighted hypergraph based method. We then validate the effectiveness of our proposed method on the Philadelphia Neurodevelopmental Cohort data for classifying subjects’ learning ability levels, and discover potential imaging biomarkers which may account for a proportion of the variance in learning ability.

Paper Details

Date Published: 28 February 2020
PDF: 6 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131710 (28 February 2020); doi: 10.1117/12.2543089
Show Author Affiliations
Li Xiao, Tulane Univ. (United States)
Julia M. Stephen, The Mind Research Network (United States)
Tony W. Wilson, Univ. of Nebraska Medical Ctr. (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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