College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, United Kingdom
Translational Research Exchange @ Exeter (TREE), University of Exeter, Exeter, EX4 4QD, United Kingdom.
eNeuro. 2019 Aug 12;6(4). doi: 10.1523/ENEURO.0059-19.2019. Print 2019 Jul/Aug.
Large-scale brain networks are increasingly recognized as important for the generation of seizures in epilepsy. However, how a network evolves from a healthy state through the process of epileptogenesis remains unclear. To address this question, here, we study longitudinal epicranial background EEG recordings (30 electrodes, EEG free from epileptiform activity) of a mouse model of mesial temporal lobe epilepsy. We analyze functional connectivity networks and observe that over the time course of epileptogenesis the networks become increasingly asymmetric. Furthermore, computational modelling reveals that a set of nodes, located outside of the region of initial insult, emerges as particularly important for the network dynamics. These findings are consistent with experimental observations, thus demonstrating that ictogenic mechanisms can be revealed on the EEG, that computational models can be used to monitor unfolding epileptogenesis and that both the primary focus and epileptic network play a role in epileptogenesis.
大规模脑网络越来越被认为对癫痫发作的产生很重要。然而，一个网络如何从健康状态通过癫痫发生的过程演变仍然不清楚。为了解决这个问题，我们在这里研究了内侧颞叶癫痫模型的纵向颅外背景 EEG 记录（30 个电极，无癫痫样活动的 EEG）。我们分析了功能连接网络，观察到在癫痫发生的过程中，网络变得越来越不对称。此外，计算模型揭示了一组位于初始损伤区域之外的节点，这些节点对于网络动态特别重要。这些发现与实验观察一致，因此表明癫痫发生的机制可以在 EEG 上揭示，计算模型可以用于监测癫痫发生的发展，并且原发性焦点和癫痫网络都在癫痫发生中起作用。
