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

Objective. Inferring the optimized and sparse network structure from the fully connected matrix is a key step in functional connectivity (FC) analysis. However, it is still an urgent problem to be solved, how to exclude the weak and spurious connections contained in functional networks objectively. Most existing binarization methods assume that the network has some certain constraint structures, which lead to changes in the original topology of the network. Approach. To solve this problem, we develop a Trade-off Model between Cost and Topology under Role Division (MCT), which consists of three crucial strategies, including modularity detection, definition of node role, and E-cost optimization algorithm. This algorithm weighs the physical cost and adaptive value of the network while preserving the network structure. Reliability and validity of MCT were evaluated by comparing different binarization methods (efficiency cost optimization, cluster-span threshold, threshold method, and MCT) on synthetic and real data sets. Main results. Experiment results demonstrated that the recovery rate of MCT for networks under noise interference is superior to other methods. In addition, brain networks filtered with MCT had higher network efficiency and shorter characteristic path length, which is more in line with the small world characteristics. Finally, applying MCT to resting-state electroencephalography data from patients with major depression reveals abnormal topology of the patients' connectivity networks, manifested as lower clustering coefficient (CC) and higher global efficiency (GE). Significance. This study provides an objective method for complex network analysis, which may contribute to the future of FC research.