{"title":"Complex Network Analysis of Hippocampal Regulation in the Mouse Brain Network to Control Epileptic Seizures.","authors":"Xiaojun Zhou, Yuan Wang, Bailu Si","doi":"10.1109/TNSRE.2025.3616957","DOIUrl":null,"url":null,"abstract":"<p><p>For focal epilepsy, modeling the virtual brain through large-scale network dynamics to customize treatments is currently a very promising approach. However, after obtaining the epileptic brain connectome of subjects, most researches were focused on exploring ways to help clinicians to better perform brain resections. From the perspective of complex networks, we explore the possibility of utilizing the strength of network coupling to treat seizures non-destructively. We use the Epileptor model to construct heterogeneous dynamic networks with epileptogenic zones and design global indices appropriate for this model to describe systemic seizures. Based on these, we explored the effects of epileptogenic proportion and global coupling strength on different artificial networks, and finally verified on a real Allen mouse connectome that the enhancement of coupling strength can effectively control epilepsy. Our simulations found that as the epileptogenic proportion increased, seizure propagation steadily improved for the small-world and the scale-free networks, while the random network jumped from a sustaining state of global suppression to a state of global bursting. As for the increase in global coupling strength, the small-world network maintained a steady spread, while both the random and scale-free networks had their seizures significantly controlled. Subsequently, we validated the suppression in the Allen mouse focal seizure by boosting the coupling strength a little in its hippocampal formation. Our study shows that the structural nature of networks significantly affects seizure propagation and synchronization. The topology of the random network is significantly anti-epileptic, and others are easy to maintain. Coupling strength is an effective way to control epilepsy in both random and scale-free networks. Thus, we give an idea of using the structural nature of networks to control seizures non-destructively, while this may also be the theoretical basis for other cognitive training therapies, such as emotional or exercise in controlling epilepsy by training projection strengths from different brain regions.</p>","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"PP ","pages":""},"PeriodicalIF":5.2000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/TNSRE.2025.3616957","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
For focal epilepsy, modeling the virtual brain through large-scale network dynamics to customize treatments is currently a very promising approach. However, after obtaining the epileptic brain connectome of subjects, most researches were focused on exploring ways to help clinicians to better perform brain resections. From the perspective of complex networks, we explore the possibility of utilizing the strength of network coupling to treat seizures non-destructively. We use the Epileptor model to construct heterogeneous dynamic networks with epileptogenic zones and design global indices appropriate for this model to describe systemic seizures. Based on these, we explored the effects of epileptogenic proportion and global coupling strength on different artificial networks, and finally verified on a real Allen mouse connectome that the enhancement of coupling strength can effectively control epilepsy. Our simulations found that as the epileptogenic proportion increased, seizure propagation steadily improved for the small-world and the scale-free networks, while the random network jumped from a sustaining state of global suppression to a state of global bursting. As for the increase in global coupling strength, the small-world network maintained a steady spread, while both the random and scale-free networks had their seizures significantly controlled. Subsequently, we validated the suppression in the Allen mouse focal seizure by boosting the coupling strength a little in its hippocampal formation. Our study shows that the structural nature of networks significantly affects seizure propagation and synchronization. The topology of the random network is significantly anti-epileptic, and others are easy to maintain. Coupling strength is an effective way to control epilepsy in both random and scale-free networks. Thus, we give an idea of using the structural nature of networks to control seizures non-destructively, while this may also be the theoretical basis for other cognitive training therapies, such as emotional or exercise in controlling epilepsy by training projection strengths from different brain regions.
期刊介绍:
Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.