An exploratory computational analysis in mice brain networks of widespread epileptic seizure onset locations along with potential strategies for effective intervention and propagation control

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-02-08 DOI:10.3389/fncom.2024.1360009
Juliette Courson, Mathias Quoy, Yulia Timofeeva, Thanos Manos
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Abstract

Mean-field models have been developed to replicate key features of epileptic seizure dynamics. However, the precise mechanisms and the role of the brain area responsible for seizure onset and propagation remain incompletely understood. In this study, we employ computational methods within The Virtual Brain framework and the Epileptor model to explore how the location and connectivity of an Epileptogenic Zone (EZ) in a mouse brain are related to focal seizures (seizures that start in one brain area and may or may not remain localized), with a specific focus on the hippocampal region known for its association with epileptic seizures. We then devise computational strategies to confine seizures (prevent widespread propagation), simulating medical-like treatments such as tissue resection and the application of an anti-seizure drugs or neurostimulation to suppress hyperexcitability. Through selectively removing (blocking) specific connections informed by the structural connectome and graph network measurements or by locally reducing outgoing connection weights of EZ areas, we demonstrate that seizures can be kept constrained around the EZ region. We successfully identified the minimal connections necessary to prevent widespread seizures, with a particular focus on minimizing surgical or medical intervention while simultaneously preserving the original structural connectivity and maximizing brain functionality.

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对小鼠大脑网络中广泛的癫痫发作位置进行探索性计算分析,以及有效干预和传播控制的潜在策略
目前已开发出平均场模型来复制癫痫发作动态的关键特征。然而,人们对负责癫痫发作和传播的脑区的确切机制和作用仍然知之甚少。在这项研究中,我们在虚拟大脑框架和 Epileptor 模型中采用计算方法,探索小鼠大脑中致痫区(EZ)的位置和连通性与局灶性癫痫发作(癫痫发作从一个脑区开始,可能会也可能不会保持局部性)的关系,并特别关注因与癫痫发作相关而闻名的海马区。然后,我们设计出限制癫痫发作(防止广泛传播)的计算策略,模拟类似医疗的治疗方法,如组织切除、应用抗癫痫药物或神经刺激来抑制过度兴奋。通过有选择性地移除(阻断)由结构连接组和图网络测量结果提供的特定连接,或局部降低 EZ 区域的外向连接权重,我们证明癫痫发作可以被限制在 EZ 区域周围。我们成功地确定了防止大范围癫痫发作所需的最小连接,重点是最大限度地减少手术或药物干预,同时保留原有的结构连接并最大限度地提高大脑功能。
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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