PAC-KENReg: An interpretable hypergraph framework capturing nonlinear and dynamic functional connectivity for neuropsychiatric disease diagnosis.

BACKGROUND AND

OBJECTIVE

Hypergraph-based analysis has become a pivotal method for modeling functional connectivity networks to elucidate neuropathological mechanisms in psychiatric disorders. However, existing approaches are constrained by two key limitations: their reliance on linear assumptions, which limits the capture of complex nonlinear interactions among brain regions, and their failure to incorporate the neurophysiological meaning of hyperedge weights. To address these challenges, this study introduces an enhanced hypergraph framework that integrates nonlinear modeling with biologically meaningful neural information for both static and dynamic functional network analysis.

METHODS

This study proposes a novel framework, "Phase-Amplitude Coupling-weighted Kernelized Elastic Net Regularization" (PAC-KENReg). The method employs a kernelized Elastic Net to capture high-order nonlinear interactions and construct the hypergraph topology. It then innovatively weights hyperedges using phase-amplitude coupling (PAC) strength to incorporate neurophysiological information. Furthermore, we extend the framework to dynamic analysis by introducing temporal stability matrices that capture the time-varying characteristics of brain network organization.

RESULTS

Validated on public EEG datasets of Major Depressive Disorder (MDD) and Attention Deficit Hyperactivity Disorder (ADHD), PAC-KENReg achieved a classification accuracy of 78.39% and a balanced accuracy of 78.33% for MDD, significantly outperforming baseline models. The method also demonstrated superior discriminative power for ADHD, effectively identified key pathology-associated brain regions, and revealed abnormal dynamic connectivity profiles in patients.

CONCLUSION

The proposed PAC-KENReg framework exhibits excellent generalization capability and robustness, providing a reliable and interpretable tool for characterizing brain connectivity patterns. It offers an effective computational approach for the objective diagnosis and mechanistic investigation of psychiatric disorders.