A thresholding method based on society modularity and role division for functional connectivity analysis.

Area of Science:

• Neuroscience
• Network Science
• Computational Biology

Background:

• Functional connectivity (FC) analysis requires inferring sparse network structures from fully connected matrices.
• Existing methods for objectively excluding weak and spurious connections in functional networks are limited.
• Current binarization techniques often alter the original network topology due to assumed constraint structures.

Purpose of the Study:

• To develop an objective method for inferring optimized and sparse network structures in functional connectivity analysis.
• To address the challenge of excluding weak and spurious connections without altering network topology.
• To evaluate a novel approach for functional network binarization that preserves network structure.

Main Methods:

• Developed the Trade-off Model between Cost and Topology under Role Division (MCT).
• MCT integrates modularity detection, node role definition, and an E-cost optimization algorithm.
• Evaluated MCT against other binarization methods using synthetic and real datasets, including resting-state EEG data.

Main Results:

• MCT demonstrated superior recovery rates for networks affected by noise compared to existing methods.
• Brain networks analyzed with MCT exhibited enhanced network efficiency and shorter characteristic path lengths, aligning with small-world characteristics.
• Application to major depression patient data revealed abnormal network topology, characterized by lower clustering coefficient (CC) and higher global efficiency (GE).

Conclusions:

• MCT provides an objective and robust method for functional network analysis.
• The developed algorithm effectively preserves network topology while optimizing for cost and adaptive value.
• This approach offers a valuable tool for future functional connectivity research and understanding neurological disorders.