Structure-function clustering in weighted brain networks.

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.