Ponten S C, Bartolomei F, Stam C J
Department of Clinical Neurophysiology, VU University Medical Centre, 1007 MB Amsterdam, The Netherlands.
Clin Neurophysiol. 2007 Apr;118(4):918-27. doi: 10.1016/j.clinph.2006.12.002. Epub 2007 Feb 20.
Neuronal networks with a so-called "small-world" topography (characterized by strong clustering in combination with short path lengths) are known to facilitate synchronization, and possibly seizure generation. We tested the hypothesis that real functional brain networks during seizures display small-world features, using intracerebral recordings of mesial temporal lobe seizures.
We used synchronization likelihood (SL) to characterize synchronization patterns in intracerebral EEG recordings of 7 patients for 5 periods of interest: interictal, before-, during- and after rapid discharges (in which the last two periods are ictal) and postictal. For each period, graphs (abstract network representations) were reconstructed from the synchronization matrix and characterized by a clustering coefficient C (measure of local connectedness) and a shortest path length L (measure of overall network integration). Results were also compared with those obtained from random networks.
The neuronal network changed during seizure activity, with an increase of C and L most prominent in the alpha, theta and delta frequency bands during and after the seizure.
During seizures, the neuronal network moves in the direction of a more ordered configuration (higher C combined with a slightly, but significantly, higher L) compared to the more randomly organized interictal network, even after correcting for changes in synchronization strength.
Analysis of neuronal networks during seizures may provide insight into seizure genesis and development.
具有所谓“小世界”拓扑结构（其特征为紧密聚集与短路径长度相结合）的神经元网络已知有助于同步，并且可能引发癫痫发作。我们使用内侧颞叶癫痫的脑内记录来检验癫痫发作期间真实功能性脑网络呈现小世界特征这一假设。
我们使用同步似然性（SL）来表征7例患者脑内脑电图记录在5个感兴趣时期的同步模式：发作间期、快速放电前、快速放电期间、快速放电后（其中后两个时期为发作期）以及发作后期。对于每个时期，从同步矩阵重建图（抽象网络表示），并通过聚类系数C（局部连通性度量）和最短路径长度L（整体网络整合度量）进行表征。结果还与从随机网络获得的结果进行了比较。
癫痫发作活动期间神经元网络发生变化，在癫痫发作期间及之后，α、θ和δ频段的C和L增加最为显著。
与组织更为随机的发作间期网络相比（即使校正同步强度变化后），癫痫发作期间神经元网络朝着更有序的配置方向发展（更高的C与略高但显著的L相结合）。
癫痫发作期间神经元网络的分析可能有助于深入了解癫痫发作的起源和发展。
