Lehnertz Klaus, Bröhl Timo, Wrede Randi von
Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany; Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53175 Bonn, Germany.
Department of Epileptology, University of Bonn Medical Centre, Venusberg Campus 1, 53127 Bonn, Germany; Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn, Germany.
Neurobiol Dis. 2023 Jun 1;181:106098. doi: 10.1016/j.nbd.2023.106098. Epub 2023 Mar 29.
Epilepsy is now conceptualized as a network disease. The epileptic brain network comprises structurally and functionally connected cortical and subcortical brain regions - spanning lobes and hemispheres -, whose connections and dynamics evolve in time. With this concept, focal and generalized seizures as well as other related pathophysiological phenomena are thought to emerge from, spread via, and be terminated by network vertices and edges that also generate and sustain normal, physiological brain dynamics. Research over the last years has advanced concepts and techniques to identify and characterize the evolving epileptic brain network and its constituents on various spatial and temporal scales. Network-based approaches further our understanding of how seizures emerge from the evolving epileptic brain network, and they provide both novel insights into pre-seizure dynamics and important clues for success or failure of measures for network-based seizure control and prevention. In this review, we summarize the current state of knowledge and address several important challenges that would need to be addressed to move network-based prediction and control of seizures closer to clinical translation.
癫痫现在被概念化为一种网络疾病。癫痫脑网络由结构和功能上相连的皮质和皮质下脑区组成，这些脑区跨越脑叶和半球，其连接和动态随时间演变。基于这一概念，局灶性和全身性癫痫发作以及其他相关的病理生理现象被认为是由网络顶点和边产生、传播并终止的，这些顶点和边也产生并维持正常的生理脑动态。过去几年的研究推动了相关概念和技术的发展，以在各种空间和时间尺度上识别和表征不断演变的癫痫脑网络及其组成部分。基于网络的方法增进了我们对癫痫发作如何从不断演变的癫痫脑网络中产生的理解，它们不仅为发作前动态提供了新的见解，还为基于网络的癫痫控制和预防措施的成败提供了重要线索。在这篇综述中，我们总结了当前的知识状态，并阐述了为使基于网络的癫痫预测和控制更接近临床转化而需要解决的几个重要挑战。
