Synchronization Stability Model of Complex Brain Networks: An EEG Study - Yin, Guimei; Li, Haifang; Tan, Shuping; Yao, Rong; Cui, Xiaohong; Zhao, Lun (Frontiers in Psychiatry, 2020)
In this paper, from the perspective of complex network dynamics we investigated the formation of the synchronization state of the brain networks. Based on the Lyapunov stability theory of complex networks, a synchronous steady-state model suitable for application to complex dynamic brain networks is proposed. The synchronization stability problem of brain network state equation is transformed into a convex optimization problem with Block Coordinate Descent (BCD) method. By using Random Apollo Network (RAN) method as a node selection rule, the brain network constructs its subnetwork dynamically. We also analyzes the change of the synchronous stable state of the subnetwork constructed by this method with the increase of the size of the network. Simulation EEG data from alcohol addicts patients and Real experiment EEG data from schizophrenia patients were used to verify the robustness and validity of the proposed model. Differences in the synchronization characteristics of the brain networks between normal and alcoholic patients were analyzed, so as differences between normal and schizophrenia patients. The experimental results indicated that the establishment of a synchronous steady state model in this paper can be used to verify the synchronization of complex dynamic brain networks. These results will potentially be of great value in the further study of the pathogenic mechanisms of mental illness.