Neural oscillations are considered to be brain-specific signatures of information processing and communication in the brain. They also reflect pathological brain activity in neurological disorders, thus offering a basis for diagnoses and forecasting. Epilepsy is one of the most common neurological disorders, characterized by abnormal synchronization and desynchronization of the oscillations in the brain. About one third of epilepsy cases are pharmacoresistant, and as such emphasize the need for novel therapy approaches, where brain stimulation appears to be a promising therapeutic option. The development of brain stimulation paradigms, however, is often based on generalized assumptions about brain dynamics, although it is known that significant differences occur between patients and brain states. We developed a framework to extract individualized predictive models of epileptic network dynamics directly from EEG data. The models are based on the dominant coherent oscillations and their dynamical coupling, thus combining an established interpretation of dynamics through neural oscillations, with accurate patient-specific features. We show that it is possible to build a direct correspondence between the models of brain-network dynamics under periodic driving, and the mechanism of neural entrainment via periodic stimulation. When our framework is applied to EEG recordings of patients in status epilepticus (a brain state of perpetual seizure activity), it yields a model-driven predictive analysis of the therapeutic performance of periodic brain stimulation. This suggests that periodic brain stimulation can drive pathological states of epileptic network dynamics towards a healthy functional brain state.

翻译：神经振荡被认为是大脑信息处理与通信的脑特异性标志，同时也反映了神经系统疾病中的病理性脑活动，为诊断和预测提供了基础。癫痫是最常见的神经系统疾病之一，其特征是脑内振荡的异常同步与去同步化。约三分之一的癫痫病例具有药物抵抗性，因此迫切需要新型治疗方法，其中脑刺激被视为极具前景的治疗选择。然而，脑刺激范式的发展通常基于对大脑动力学的普遍假设，尽管已知患者间及不同脑状态间存在显著差异。我们开发了一个框架，可直接从脑电图数据中提取癫痫网络动力学的个体化预测模型。该模型基于主导相干振荡及其动力学耦合，将基于神经振荡的经典动力学解释与精确的患者特异性特征相结合。研究证明，在周期性驱动下，脑网络动力学模型与通过周期性刺激实现的神经夹带机制之间可建立直接对应关系。当将该框架应用于癫痫持续状态（一种持续痫性发作的脑状态）患者的脑电图记录时，能够对周期性脑刺激的治疗效果进行模型驱动的预测分析。这表明周期性脑刺激可将癫痫网络动力学的病理状态驱动至健康的功能性脑状态。