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From pairwise to higher-order brain community detection: A hypergraph signal processing approach on brain functional

Breno C Bispo1, José R de Oliveira Neto1, Juliano B Lima1

  • 1Department of Electronics and Systems, Federal University of Pernambuco, Recife, Brazil.

Computers in Biology and Medicine
|December 26, 2025
PubMed
Summary

This study introduces a new hypergraph framework to reveal complex brain connectivity patterns missed by traditional methods. It uncovers higher-order brain structures and cross-network interactions, offering insights into brain organization.

Keywords:
Brain community detectionBrain functional connectivityHypergraph signal processingNeurosciencefMRI

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Traditional network theory struggles to capture complex, non-pairwise brain interactions.
  • Existing hypergraph methods often rely on approximations, limiting their ability to represent true higher-order relationships.
  • Understanding higher-order brain functional connectivity is crucial for neuroscience.

Purpose of the Study:

  • To develop a novel community detection framework for analyzing higher-order functional connectivity in the brain.
  • To identify neurobiologically interpretable brain structures using advanced mathematical tools.
  • To overcome limitations of traditional graph-based approaches in capturing complex brain interactions.

Main Methods:

  • Integration of multivariate information-theoretic measures with hypergraph signal processing.
  • Application to real-world resting-state fMRI data.
  • Comparative analysis of hypergraph and graph clustering models.

Main Results:

  • Identification of brain communities previously elusive to conventional graph-based methods.
  • Discovery of cross-network integrative patterns across distinct functional subsystems.
  • Demonstration of hypergraph modes revealing complex, higher-order brain organization.

Conclusions:

  • The novel hypergraph framework effectively analyzes higher-order functional connectivity.
  • Findings provide new insights into the brain's large-scale organization, including the redundancy-synergy balance.
  • The approach holds promise for clinical applications in studying brain disorders with disrupted connectivity.