Synchronization Stability Model of Complex Brain Networks
An EEG Study
Bibliographic Data
| ID | 15527699 |
|---|---|
| Authors | Guimei Yin (Taiyuan Normal University), Haifang Li (0009-0004-9924-9603, Taiyuan University of Technology, corresponding author), Shuping Tan (0000-0002-1265-5093, Beijing HuiLongGuan Hospital), Rong Yao (0000-0001-5617-5393, Taiyuan University of Technology), Xiaohong Cui (Taiyuan University of Technology), Lun Zhao (0000-0003-3732-6103, Liaocheng University, corresponding author) |
| Year | 2020 |
| Volume | 11 |
| Pages | 571068-571068 |
| Publication date | 2020-12-04 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2020.571068 |
| PMID | 33343416 |
| OpenAlex | W3110283798 |
| Language | EN |
| References cited | 23 |
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 was proposed. The synchronization stability problem of brain network state equation was 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 subnet work dynamically. We also analyzes the change of the synchronous stable state of the subnet work 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 could be used to verify the synchronization of complex dynamic brain networks and potentially be of great value in the further study of the pathogenic mechanisms of mental illness
Complex network · Control theory (sociology · Electroencephalography · Lyapunov stability · Resting state fMRI · Robustness (evolution · Subnet · Synchronization (alternating current · Computer Science · Functional Brain Connectivity Studies · Neural dynamics and brain function · Neuroscience · Nonlinear Dynamics and Pattern Formation · Psychology · Artificial Intelligence
| Citation velocity | historical |
|---|---|
| Highly cited | No |