The Optimal Setting for Multilayer Modularity Optimization in Multilayer Brain Networks.

Area of Science:

• Neuroscience
• Network Science
• Data Analysis

Background:

• Brain networks exhibit modular integration and segregation, crucial for functioning.
• Brain networks are inherently multilayer, varying across time, frequency, subjects, and conditions.
• Multilayer community detection algorithms identify communities in time-varying networks, with spatial (γ) and temporal (ω) resolution parameters.

Purpose of the Study:

• To investigate the impact of different temporal resolution (ω) values on multilayer community detection algorithms.
• To provide guidance on selecting optimal ω values based on network properties.
• To apply and validate the findings using real functional brain network data.

Main Methods:

• Developed and utilized ad hoc benchmark graphs to test algorithm performance across diverse conditions.
• Systematically evaluated community detection algorithm performance under varying ω values.
• Applied the optimized algorithm to functional brain networks derived from electro-encephalographic (EEG) signals.

Main Results:

• Demonstrated that the choice of ω significantly affects community detection outcomes.
• Established a relationship between network features and the suitability of different ω values.
• Simulation results were consistent with the analysis of real EEG data.

Conclusions:

• The study provides a practical guide for selecting appropriate ω values in multilayer community detection.
• Understanding the role of ω enhances the analysis of dynamic brain network organization.
• The findings are validated through application to real-world electro-encephalographic data.