[Research on weighted hypergraph attention neural network for the diagnosis of psychiatric disorders using brain functional connectivity networks].

The hypergraph neural network (HGNN) has demonstrated efficacy in modeling high-order interactions among brain regions, thus providing a promising framework for analyzing brain functional connectivity networks in the context of psychiatric research. The present study proposes a phase-amplitude coupling-weighted hypergraph attention neural network (PAC-HyperGAT) model for the diagnosis of psychiatric diseases. The proposed methodology first constructs a functional hypergraph using elastic net-based sparse regression and then assigns physiologically meaningful weights to hyperedges by quantifying the phase-amplitude coupling strength among nodes within each hyperedge. In light of these findings, the present study proposes a novel hypergraph attention convolution kernel. The efficacy of this approach is evidenced by its enhancement of the node-level message passing mechanism, a feat that facilitates the integration of hyperedge weight information. This phenomenon, in turn, results in an enhancement of the discriminative ability of brain functional connectivity network representations. The proposed model is systematically evaluated on publicly available electroencephalogram datasets for attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). The experimental results demonstrate that PAC-HyperGAT attains an accuracy of (72.14 ± 9.19) % in ADHD classification, surpassing the performance of existing brain functional connectivity network methods across a range of evaluation metrics. The model exhibits notable efficacy in MDD classification, signifying substantial cross-disorder generalization capabilities. Furthermore, PAC-HyperGAT has demonstrated efficacy in identifying brain regions associated with these disorders. In summary, the proposed model demonstrates excellent generalizability, robustness, and neurobiological interpretability, providing a reliable analytical framework for objective diagnosis and mechanistic investigation of psychiatric diseases.