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Updated: Aug 15, 2025

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study
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Normalizing the brain connectome for communication through synchronization.

Spase Petkoski1, Viktor K Jirsa1

  • 1Aix-Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.

Network Neuroscience (Cambridge, Mass.)
|January 6, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a dual particle-wave framework for brain networks, unifying traditional graph theory with synchronization dynamics. This novel approach reveals fundamental network properties and explains frequency-specific brain network cores.

Keywords:
Normalized connectomeOscillationsResting-state networksSpectral activation patternsSynchronizationTime delays

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Brain function relies on complex neural networks, commonly analyzed using graph theory based on a 'particle' view of information processing.
  • Brain oscillations and rhythms suggest a 'wave' perspective, emphasizing synchronization, which traditional graph theory does not fully capture.

Purpose of the Study:

  • To extend graph theory to a dual particle-wave framework for analyzing brain networks.
  • To integrate time delays and derive a normalized connectome representation.
  • To explain the emergence of frequency-specific network cores in the human brain.

Main Methods:

  • Developed a generalized framework combining particle and wave perspectives of network analysis.
  • Integrated time delays representing finite signal transmission speeds.
  • Applied the normalized connectome to Human Connectome Project data.

Main Results:

  • The dual framework successfully explains the emergence of frequency-specific network cores, such as visual and default mode networks.
  • Findings were robust across 100 human subjects, indicating a fundamental network property.
  • The normalized connectome unifies the particle view (infinite transmission speed) with the wave perspective.

Conclusions:

  • The proposed generalized framework reconciles particle and wave views of brain networks.
  • This unified approach enhances the applicability of graph theory to diverse network phenomena, including brain rhythms.
  • The normalized connectome offers a fundamental network property applicable to physiological and pathological brain states.