Abstract #5186

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

Lyuan Xu^1,2, Yurui Gao^1,3, Muwei Li^1,4, Zhongliang Zu^1,4, Yikang Li^1,3, Kurt G. Schilling^1,4, Adam W. Anderson^1,3,4, John C. Gore^1,3,4, and Zhaohua Ding^1,2,3

¹Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States, ²Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States, ³Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ⁴Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States

Synopsis

Keywords: Functional Connectivity, White Matter

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.

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