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Frequency-based brain networks: From a multiplex framework to a full multilayer description.

Javier M Buldú1, Mason A Porter2

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PubMed
Summary
This summary is machine-generated.

We explored brain dynamics using multilayer and multiplex networks across different frequency bands. Network structure, particularly missing connections, significantly impacts brain synchronizability indicators like the second-smallest eigenvalue (λ₂).

Keywords:
Algebraic connectivityFunctional brain networksMagnetoencephalographyMultilayer networksMultiplex networks

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Area of Science:

  • Neuroscience
  • Network Science
  • Computational Biology

Background:

  • Brain function relies on complex dynamical interactions between regions.
  • Functional brain networks can be analyzed across different frequency bands.
  • Network properties, such as the second-smallest eigenvalue (λ₂), are linked to brain health and synchronizability.

Purpose of the Study:

  • To investigate the impact of network modeling approaches (multilayer vs. multiplex) on understanding brain dynamics.
  • To analyze how network structure, specifically frequency band interactions, influences brain synchronizability.
  • To evaluate the role of network heterogeneity and missing connections on the second-smallest eigenvalue (λ₂) in brain networks.

Main Methods:

  • Constructed functional brain networks as both multilayer and multiplex networks, with layers representing distinct frequency bands.
  • Calculated the second-smallest eigenvalue (λ₂) of the combinatorial supra-Laplacian matrix for both network models.
  • Utilized synthetic network models and real resting-state magnetoencephalography (MEG) data for analysis.

Main Results:

  • The choice between multilayer and multiplex network representations significantly affects the analysis of brain dynamics.
  • Heterogeneity in interlayer edge weights and the fraction of missing edges critically influence the λ₂ value.
  • Results demonstrate distinct outcomes for multiplex and multilayer approaches in frequency-based brain network analysis.

Conclusions:

  • Emphasizes the importance of appropriate network modeling for studying frequency-specific brain interactions.
  • Highlights that network topology, including missing connections, is crucial for interpreting synchronizability measures like λ₂.
  • Suggests that a full multilayer approach provides a more comprehensive understanding of frequency-based brain network dynamics compared to a multiplex approach.