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Effective hyper-connectivity network construction and learning: Application to major depressive disorder

Jingyu Liu1, Wenxin Yang2, Yulan Ma3

  • 1Key Laboratory of Brain Health Intelligent Evaluation and Intervention, Ministry of Education, and the School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.

Computers in Biology and Medicine
|February 23, 2024
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Summary

This study introduces effective hyper-connectivity (EHC) networks and a directed hypergraph convolutional network (DHGCN) for brain disease identification. The novel approach accurately identifies major depressive disorder (MDD) by analyzing directional brain connectivity.

Keywords:
Deep learningFunctional connectivityHypergraph effective connectivityMajor depressive disorder (MDD)Resting-state functional magnetic resonance imaging (rs-fMRI)

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Computational Psychiatry

Background:

  • Resting-state functional connectivity (rs-fMRI) captures pairwise brain region correlations but misses higher-order interactions.
  • Hyper-connectivity methods are gaining attention for analyzing complex brain networks, yet often neglect connection directionality.
  • Information flow direction is crucial for understanding brain activity and cognitive processes, making its omission a significant limitation.

Purpose of the Study:

  • To propose a novel effective hyper-connectivity (EHC) network integrating direction detection and hyper-connectivity modeling for characterizing high-order directional information flow.
  • To develop a directed hypergraph convolutional network (DHGCN) for deep representation learning from EHC networks and functional indicators.
  • To enhance the identification of major depressive disorder (MDD) by leveraging these advanced neuroimaging analysis techniques.

Main Methods:

  • Developed an effective hyper-connectivity (EHC) network to model directed, higher-order relationships among brain regions.
  • Constructed a directed hypergraph convolutional network (DHGCN) capable of processing directed hypergraph data and incorporating multiple functional indicators.
  • Integrated DHGCN-derived deep representations with demographic factors for major depressive disorder (MDD) classification.

Main Results:

  • The proposed DHGCN framework significantly outperformed traditional functional connectivity (FC) and undirected hyper-connectivity models in brain disease identification.
  • The method demonstrated superior performance compared to existing state-of-the-art approaches for major depressive disorder (MDD) detection.
  • Abnormalities in effective hyper-connectivity (EHC) were identified, offering enhanced insights into brain function in individuals with MDD.

Conclusions:

  • The novel EHC network and DHGCN provide a more comprehensive understanding of brain activity by incorporating directional, higher-order interactions.
  • This framework offers a robust and effective method for brain disease identification, particularly for major depressive disorder (MDD).
  • The findings highlight the importance of directional hyper-connectivity in analyzing brain function and diagnosing neurological and psychiatric conditions.