A model for focal seizure onset, propagation, evolution, and progression
A model for focal seizure onset, propagation, evolution, and progression
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DOI：
10.7554/elife.50927
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发表时间：
2020-03-23
期刊：
影响因子：
7.7
通讯作者：
Abbott, L. F.
中科院分区：
文献类型：
--
作者：
Liou, Jyun-you;Smith, Elliot H.;Abbott, L. F.
我们开发了一个神经网络模型，可以解释人类局灶性癫痫发作的主要因素。这些包括强直-阵挛转变，临床符号学进展缓慢和相应的癫痫发作范围扩张，广泛的脑电图同步，以及癫痫发作接近终止时的初始节律减慢。这些是通过在自适应神经网络中结合使用依赖的抑制耗尽来重现的，该神经网络除了接收局部复发投射外，还接收全局反馈抑制。我们的模型提出了可能强调常见脑电图发作模式和癫痫持续状态的机制，并假设突触可塑性在癫痫灶的出现中起作用。包括随机噪声时，发作活动的复杂模式和双稳态发作终点出现。随着临床和实验工具的快速发展，我们相信该模型可以为未来探索癫痫发作机制和临床治疗提供路线图和潜在的硅测试平台。
We developed a neural network model that can account for major elements common to human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology and corresponding seizure territory expansion, widespread EEG synchronization, and slowing of the ictal rhythm as the seizure approaches termination. These were reproduced by incorporating usage-dependent exhaustion of inhibition in an adaptive neural network that receives global feedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that may underline common EEG seizure onset patterns and status epilepticus, and postulates a role for synaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bistable seizure end-points arise when stochastic noise is included. With the rapid advancement of clinical and experimental tools, we believe that this model can provide a roadmap and potentially an in silico testbed for future explorations of seizure mechanisms and clinical therapies.