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Related Experiment Video

Updated: Apr 11, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

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PAC-KENReg: An interpretable hypergraph framework capturing nonlinear and dynamic functional connectivity for

Haotao Yan1, Peizhou Wang2, Xiuwei Lin1

  • 1Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan 528000, China.

Computer Methods and Programs in Biomedicine
|April 9, 2026
PubMed
Summary

This study introduces a new hypergraph framework, PAC-KENReg, for analyzing brain connectivity in psychiatric disorders. It improves diagnostic accuracy for Major Depressive Disorder and Attention Deficit Hyperactivity Disorder by capturing nonlinear interactions and neurophysiological data.

Keywords:
Dynamic analysisFunctional connectivityNonlinear hypergraphPsychiatric disordersWeighted hyperedge

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Network Science

Background:

  • Hypergraph analysis is key for modeling brain connectivity in psychiatric disorders.
  • Existing methods struggle with nonlinear interactions and lack neurophysiological meaning in hyperedge weights.

Purpose of the Study:

  • To develop an enhanced hypergraph framework integrating nonlinear modeling and neurophysiological information.
  • To analyze both static and dynamic functional brain networks for psychiatric disorder research.

Main Methods:

  • Introduced "Phase-Amplitude Coupling-weighted Kernelized Elastic Net Regularization" (PAC-KENReg).
  • Utilized kernelized Elastic Net for high-order nonlinear interactions and hypergraph topology.
  • Weighted hyperedges with phase-amplitude coupling (PAC) strength for neurophysiological insight.
  • Extended to dynamic analysis using temporal stability matrices.

Main Results:

  • Achieved 78.39% classification accuracy for Major Depressive Disorder (MDD) on EEG data.
  • Demonstrated superior discriminative power for Attention Deficit Hyperactivity Disorder (ADHD).
  • Identified key pathology-associated brain regions and abnormal dynamic connectivity profiles.

Conclusions:

  • PAC-KENReg offers a robust and interpretable tool for brain connectivity analysis.
  • Provides an effective computational approach for objective diagnosis and mechanistic investigation of psychiatric disorders.