小鼠脑网络海马调节控制癫痫发作的复杂网络分析。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Xiaojun Zhou, Yuan Wang, Bailu Si
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引用次数: 0

摘要

对于局灶性癫痫,通过大规模网络动态建模虚拟大脑来定制治疗是目前一种非常有前途的方法。然而,在获得被试癫痫脑连接组后,大多数研究都集中在探索如何帮助临床医生更好地进行脑切除术。从复杂网络的角度,我们探讨了利用网络耦合强度非破坏性治疗癫痫发作的可能性。我们使用癫痫模型构建具有癫痫区的异构动态网络,并设计适合该模型的全局指数来描述全身癫痫发作。在此基础上,我们探索了致痫比例和全局耦合强度对不同人工网络的影响,最终在真实Allen小鼠连接组上验证了耦合强度的增强可以有效控制癫痫。我们的模拟发现,随着致痫比例的增加,小世界和无标度网络的癫痫传播稳步改善,而随机网络从持续的全局抑制状态跃升到全局爆发状态。在全局耦合强度增加方面,小世界网络保持稳定的传播,而随机和无标度网络的癫痫发作均得到明显控制。随后,我们在Allen小鼠局灶性癫痫中通过略微增强海马结构中的偶联强度来验证其抑制作用。我们的研究表明,网络的结构性质显著影响癫痫发作的传播和同步。随机网络的拓扑结构具有显著的抗癫痫性,其他拓扑结构易于维护。无论在随机网络还是无标度网络中,耦合强度都是控制癫痫的有效方法。因此,我们提出了一种利用网络的结构性质来非破坏性地控制癫痫发作的想法,同时这也可能是其他认知训练疗法的理论基础,例如通过训练来自不同大脑区域的投射强度来控制癫痫的情绪或运动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Complex Network Analysis of Hippocampal Regulation in the Mouse Brain Network to Control Epileptic Seizures.

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.

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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
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.
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