Identifying disease-related subnetwork connectome biomarkers by sparse hypergraph learning.

ABSTRACT: The functional brain network has gained increased attention in the neuroscience community because of its ability to reveal the underlying architecture of human brain. In general, majority work of functional network connectivity is built based on the correlations between discrete-time-series signals that link only two different brain regions. However, these simple region-to-region connectivity models do not capture complex connectivity patterns between three or more brain regions that form a connectivity subnetwork, or subnetwork for short. To overcome this current limitation, a hypergraph learning-based method is proposed to identify subnetwork differences between two different cohorts. To achieve our goal, a hypergraph is constructed, where each vertex represents a subject and also a hype

SUBMITTER: Zu C 

PROVIDER: S-EPMC6513717 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature