College of Engineering, Mathematics and Physical Sciences, University of Exeter , Exeter , UK.
Institute of Psychiatry, King's College London , London , UK.
Front Neurol. 2014 Dec 8;5:261. doi: 10.3389/fneur.2014.00261. eCollection 2014.
Recent clinical work has implicated network structure as critically important in the initiation of seizures in people with idiopathic generalized epilepsies. In line with this idea, functional networks derived from the electroencephalogram (EEG) at rest have been shown to be significantly different in people with generalized epilepsy compared to controls. In particular, the mean node degree of networks from the epilepsy cohort was found to be statistically significantly higher than those of controls. However, the mechanisms by which these network differences can support recurrent transitions into seizures remain unclear. In this study, we use a computational model of the transition into seizure dynamics to explore the dynamic consequences of these differences in functional networks. We demonstrate that networks with higher mean node degree are more prone to generating seizure dynamics in the model and therefore suggest a mechanism by which increased mean node degree of brain networks can cause heightened ictogenicity.
最近的临床工作表明，网络结构在特发性全面性癫痫患者癫痫发作的起始中至关重要。与这一观点一致，从静息状态脑电图（EEG）得出的功能网络在患有全面性癫痫的人群与对照组之间存在显著差异。特别是，癫痫组网络的平均节点度被发现明显高于对照组。然而，这些网络差异如何支持反复发作进入癫痫发作的机制仍不清楚。在这项研究中，我们使用癫痫发作动力学的计算模型来探索功能网络这些差异的动态后果。我们证明，具有较高平均节点度的网络更容易在模型中产生癫痫发作动力学，因此表明大脑网络的平均节点度增加可能导致致痫性增强的机制。
