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Structure-function clustering in weighted brain networks.

Jonathan J Crofts1, Michael Forrester2, Stephen Coombes3

  • 1Department of Physics and Mathematics, Nottingham Trent University, Nottingham, NG11 8NS, UK.

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This summary is machine-generated.

This study introduces a new method to analyze brain networks, revealing how dynamic functional networks emerge from static structural connections. This approach uncovers brain operation beyond criticality, enhancing cognitive flexibility.

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Functional brain networks are crucial for higher cognitive functions.
  • Understanding the dynamics of these networks and their relationship with structural connectivity is key.
  • Current methods often oversimplify complex network data through arbitrary thresholding.

Purpose of the Study:

  • To develop a generalized measure for analyzing weighted multilayer brain networks.
  • To extend structure-function clustering to weighted networks.
  • To investigate the emergence of dynamical regimes and their relation to cognitive flexibility.

Main Methods:

  • Generalized multiplex clustering for weighted multilayer networks.
  • Extended weighted structure-function clustering.
  • Analysis of human connectome data combined with simulated neural activity and bifurcation analysis.

Main Results:

  • The new measure extracts neurologically relevant features missed by single-layer analyses.
  • Identified dynamical regimes supporting multistable neural activity patterns.
  • Demonstrated the utility of the weighted multilayer approach.

Conclusions:

  • Brain operation near criticality may promote cognitive flexibility.
  • Weighted multilayer network analysis offers a more nuanced understanding of brain function.
  • This framework advances the study of structure-function relationships in the brain.