High-Order Gray-White Matter Connectivity in Hypergraph-based Brain Network

Motivation: The high-order organization of white matter (WM) in functional brain networks is not fully understood. Goal(s): Our goal is to explore high-order functional connectivity between white matter and gray matter comprehensively. Approach: We constructed a novel hypergraph model to capture WM-GM connectivity, using hypernetwork clustering coefficients for network analysis in cognitively normal and subjects with Alzheimer's disease (AD). Results: The AD group showed significant reductions in hypergraph clustering coefficients (HCC), and HCCs demonstrated significantly higher decline in AD compared to traditional metrics. Impact: This hypergraph framework provides a sensitive tool to characterize AD-related functional deterioration in WM-GM connectivity.