Much attention has been dedicated to clinical research of focal epilepsy, but the ability to derive a successful seizure control strategy based on unique dynamical features of the electroencephalogram is an unsolved problem. In this work, we introduce a basic model of spontaneous seizure dynamics and construct from it to a network model of focal-onset seizure dynamics. The full model is composed of coupled oscillators with scale-free network connectivity and a common slow variable. We find that global parameter changes and variation of the connectivity can drive the model from a quiescent state to recurrent seizures, and, eventually, to a permanent-seizure-state. Based on network synchronization features we design a stimulation scheme for the control of the fraction of nodes with strongest phase locking is proposed. Simulations lead to the identification of optimal stimuli for a given type of dynamics. Our results contribute to the development of a rational strategy for the non-surgical treatment of drug-resistant epilepsy.

中文翻译：

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基于刺激的控制的癫痫源网络模型的动态特性。

局灶性癫痫的临床研究已经引起了广泛的关注，但是基于脑电图独特的动力学特征得出成功的癫痫发作控制策略的能力尚未解决。在这项工作中，我们介绍了自发性癫痫发作动态的基本模型，并从中构建了焦点发作性癫痫发作动态的网络模型。完整模型由具有无标度网络连接和公共慢变量的耦合振荡器组成。我们发现，全局参数的变化和连通性的变化可以使模型从静止状态变为复发性癫痫发作，并最终变为永久性癫痫发作状态。基于网络同步特征，设计了一种激励方案，用于控制锁相最强的节点。通过仿真可以确定给定类型的动力学的最佳刺激。我们的研究结果有助于开发一种耐药性癫痫非手术治疗的合理策略。