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Volume 52 Issue 2
Jun.  2022
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Article Navigation >  Advances in Mechanics  > 2022  >  52(2): 339-396
Han F, Fan D G, Zhang L Y, Wang Q Y. Neurological disease and cognitive dynamics (I): Dynamics and control of epileptic seizures. Advances in Mechanics, 2022, 52(2): 339-396 doi: 10.6052/1000-0992-21-064
Citation: Han F, Fan D G, Zhang L Y, Wang Q Y. Neurological disease and cognitive dynamics (I): Dynamics and control of epileptic seizures. Advances in Mechanics, 2022, 52(2): 339-396 doi: 10.6052/1000-0992-21-064
shu

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

doi: 10.6052/1000-0992-21-064
  • 1.

    College of Information Science and Technology, Donghua University, Shanghai 201620, China

  • 2.

    School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China

  • 3.

    Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China

  • 4.

    School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China

More Information
  • Corresponding author: nmqingyun@163.com
  • Received Date: 2021-12-13
  • Accepted Date: 2022-01-26
    • Available Online: 2022-01-27
  • Publish Date: 2022-06-25
    Abstract
  • 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.

     

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