Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data.

ABSTRACT: Recently, a hypergraph constructed from functional magnetic resonance imaging (fMRI) was utilized to explore brain functional connectivity networks (FCNs) for the classification of neurodegenerative diseases. Each edge of a hypergraph (called hyperedge) can connect any number of brain regions-of-interest (ROIs) instead of only two ROIs, and thus characterizes high-order relations among multiple ROIs that cannot be uncovered by a simple graph in the traditional graph based FCN construction methods. Unlike the existing hypergraph based methods where all hyperedges are assumed to have equal weights and only certain topological features are extracted from the hypergraphs, we propose a hypergraph learning based method for FCN construction in this paper. Specifically, we first generate hyperedge

SUBMITTER: Xiao L 

PROVIDER: S-EPMC7376954 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature