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.
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 (Presented at SPIE Medical Imaging: February 20, 2020; Published: 28 February 2020); https://doi.org/10.1117/12.2543089.
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