神经系统疾病与认知动力学 (I) : 癫痫发作的动力学与控制

研究表明癫痫发作过程与神经系统本身的非线性动力学行为密切相关. 因此, 开展癫痫发作的非线性网络动力学建模与调控问题的研究, 有助于理解癫痫临床表征的动力学机理和定位致痫灶网络, 进而设计有效的网络调控策略. 本文回顾了癫痫脑神经疾病网络动力学与控制方面的研究进展, 系统总结了本文作者近年来在癫痫发作动力学建模分析及其调控等方面取得的研究成果. 首先, 基于海马齿状回CA3区环路神经元网络模型, 分析了影响颞叶癫痫发作的分子和网络结构因素, 阐释了癫痫发作转迁的动力学机制. 其次, 由于脑神经系统的集群编码特性, 基于神经场模型和平均场模型建模方法完善了皮质?基底节?丘脑环路网络动力学理论框架, 并基于此框架分析了失神癫痫发作转迁的动力学分岔机制, 探讨了不同类型癫痫发作的转迁路径, 发现了失神癫痫发作转迁的多稳态共存现象, 揭示了时滞对失神癫痫同步发作的控制效果, 设计了丰富有效的癫痫深脑刺激调控策略, 给出了电刺激调控失神癫痫发作的动力学解释. 最后, 通过数据驱动的统计建模和神经元群模型动力学建模分析, 提出了局灶癫痫致痫灶定位及寻找有效控制癫痫发作网络关键节点的理论新方法. 这些研究成果为理解难治性癫痫发作动力学本质及在临床诊疗的应用方面提供重要理论支撑. 最后对进一步研究给出若干建议. 

Neurological disease and cognitive dynamics (I): Dynamics and control of epileptic seizures

Studies have shown that the process of epileptic seizures is closely related to the nonlinear dynamics of the nervous system itself. Therefore, the study of nonlinear network dynamics modeling and regulation of epileptic seizures is helpful in understanding the dynamic mechanism of clinical manifestations of epilepsy, locating the epileptic foci network, and then designing effective network regulation strategies. This article reviews the research progress in network dynamics and control of epileptic neurological diseases and systematically summarizes our research results in recent years in the modeling and analysis of epileptic seizure dynamics and their regulation. Firstly, based on the neuron network model of the hippocampal dentate gyrus-CA3 loop, the molecular and network structural factors that affect temporal lobe seizures were analyzed, and the dynamic mechanism of seizure transition was explained. Secondly, due to the cluster coding characteristics of the brain nervous system, based on the methods of both the neural field model and mean field model, the network dynamics framework of the basal ganglia-thalamocortical (BGCT) circuit was improved. Based on this framework, the dynamic bifurcation mechanism of absence epileptic seizure transition was analyzed, the transition path of different types of seizures was explored, and the multi-stable coexistence phenomenon of absence seizure transition was discovered. The effect of time delay on the synchronization seizures was also revealed. We also designed rich and effective deep brain stimulation (DBS) control strategies for epilepsy and gave a dynamic explanation of electrical stimulation to control absence epileptic seizures. Finally, based on the data-driven statistical modeling and the dynamics analysis of the neuronal population model, new theoretical methods for the foci localization of focal epileptics and finding the key network nodes for effectively controlling seizures were proposed. These results provide important theoretical support for understanding the dynamic nature of refractory seizures and their application in clinical diagnosis and treatment. Lastly, some suggestions are given for further research.