Dynamic Node Weight Aware Directed Hypergraph Network for Major Depressive Disorder Identification

Wenbo Ning, Fei Yuan, Shijie Guo, Xiaobo Liu, Yan Niu, Rui Cao, Xin Wen

Abstract: Major depressive disorder (MDD) is a common neuropsychiatric disorder, yet its underlying physiological mechanisms remain unclear, limiting diagnostic advances. Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI), when combined with deep learning methods, has shown promise as diagnostic biomarker. Currently, most FC-based diagnostic methods rely on graph structures modeled by FC, and are limited to pairwise interactions between brain regions. Hypergraph representations enable the characterization of higher-order interactions across multiple regions. However, existing hypergraph models ignore the directionality of these interactions, thus limiting their ability to capture complex neural dynamics. To address these limitations, this study proposes a dynamic weight aware directed hypergraph learning method - dwDHGL, for MDD identification and subtype analysis. dwDHGL captures asymmetric causal interactions by modeling temporal lag effects and constructs a directed hypergraph network(DHN). It further utilizes a self-attention mechanism to dynamically learn inter node interactions during message passing and adaptively differentiate node importance. A node weight aware directed hypergraph convolution is designed to aggregate features based on hyperedge directions, incorporating dynamic weights to enhance representation learning. The proposed method is evaluated on the large-scale REST-meta-MDD dataset, achieving an MDD identification accuracy of 73.75 %, and outperforming existing advanced methods in subtype identification. Furthermore, dwDHGL identifies discriminative directed hyperedges, with the inferior frontal gyrus triangular part emerging as key biomarkers, providing new insights into the neural mechanisms of MDD.

External IDs:dblp:conf/bibm/NingYGLNCW25