A hypergraph learning method for brain functional connectivity network construction from fMRI data

Li Xiao; Julia M. Stephen; Tony W. Wilson; Vince D. Calhoun; Yu-Ping Wang

doi:10.1117/12.2543089

28 February 2020 A hypergraph learning method for brain functional connectivity network construction from fMRI data

Li Xiao, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, Yu-Ping Wang

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1131710 (2020) https://doi.org/10.1117/12.2543089
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Li Xiao, Julia M. Stephen, Tony W. Wilson, Vince D. Calhoun, and Yu-Ping Wang "A hypergraph learning method for brain functional connectivity network construction from fMRI data", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1131710 (28 February 2020); https://doi.org/10.1117/12.2543089

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