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

Abstract

Objective. Inferring the optimized and sparse network structure from the fully connected matrix is a key step in functional connectivity analysis. However, it is still an urgent problem to be solved, how to exclude the weak and spurious connections contained in function-al networks objectively. Most existing binarisation methods assume that the network has some certain constraint structures, which lead to changes in the orig-inal topology of the network. Approach. To solve this problem, we develop a Trade-off Model between Cost and Topology under Role Division (MCT), which con-sists of three crucial strategies, including modularity detection, definition of node role, and E-cost optimiza-tion 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 net-works under noise interference is superior to other methods. In addition, brain networks filtered with MCT had higher network efficiency and shorter char-acteristic path length, which is more in line with the small world characteristics. Finally, applying MCT to resting-state EEG data from patients with major de-pression reveals abnormal topology of the patients' connectivity networks, manifested as lower CC and higher GE. Significance. This study provides an objec-tive method for complex network analysis, which may contribute to the future of functional connectivity re-search.

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

“…It can be effectively used to evaluate the level of GAD and analyze the neurodynamic mechanism of GAD. The small-world network model is well used to quantify the topological structure and dynamic characteristics of a brain functional network [ 36 , 37 ]. In addition, the optimal connection mode of the brain for various task activities could be ensured by the brain functional network with small-world characteristics by balancing and optimizing the process of functional separation and integration.…”