Yin Guimei, Li Haifang, Tan Shuping, Yao Rong, Cui Xiaohong, Zhao Lun
College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.
Department of Computer Science, Taiyuan Normal University, Jinzhong, China.
Front Psychiatry. 2020 Dec 4;11:571068. doi: 10.3389/fpsyt.2020.571068. eCollection 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 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.
在本文中，我们从复杂网络动力学的角度研究了脑网络同步状态的形成。基于复杂网络的李雅普诺夫稳定性理论，提出了一种适用于复杂动态脑网络的同步稳态模型。利用块坐标下降（BCD）方法将脑网络状态方程的同步稳定性问题转化为一个凸优化问题。通过使用随机阿波罗网络（RAN）方法作为节点选择规则，脑网络动态构建其子网。我们还分析了用这种方法构建的子网的同步稳定状态随网络规模增加的变化。使用酒精成瘾患者的模拟脑电数据和精神分裂症患者的真实实验脑电数据来验证所提出模型的鲁棒性和有效性。分析了正常人和酒精成瘾患者脑网络同步特征的差异，以及正常人和精神分裂症患者之间的差异。实验结果表明，本文建立的同步稳态模型可用于验证复杂动态脑网络的同步性，并且在进一步研究精神疾病的致病机制方面可能具有重要价值。
