Department of Mathematics and Statistics, Boston University, 111 Cummington Street, Boston, MA 02215, USA.
J Neurosci. 2010 Jul 28;30(30):10076-85. doi: 10.1523/JNEUROSCI.6309-09.2010.
Epileptic seizures reflect a pathological brain state characterized by specific clinical and electrical manifestations. The proposed mechanisms are heterogeneous but united by the supposition that epileptic activity is hypersynchronous across multiple scales, yet principled and quantitative analyses of seizure dynamics across space and throughout the entire ictal period are rare. To more completely explore spatiotemporal interactions during seizures, we examined electrocorticogram data from a population of male and female human patients with epilepsy and from these data constructed dynamic network representations using statistically robust measures. We found that these networks evolved through a distinct topological progression during the seizure. Surprisingly, the overall synchronization changed only weakly, whereas the topology changed dramatically in organization. A large subnetwork dominated the network architecture at seizure onset and preceding termination but, between, fractured into smaller groups. Common network characteristics appeared consistently for a population of subjects, and, for each subject, similar networks appeared from seizure to seizure. These results suggest that, at the macroscopic spatial scale, epilepsy is not so much a manifestation of hypersynchrony but instead of network reorganization.
癫痫发作反映了一种病理状态的大脑，其特征是特定的临床和电表现。提出的机制是多种多样的，但都有一个共同的假设，即癫痫活动在多个尺度上是超同步的，但对发作过程中整个时空的癫痫动力学进行有原则和定量的分析却很少见。为了更全面地探索发作期间的时空相互作用，我们检查了来自一群男性和女性癫痫患者的皮层脑电图数据，并从这些数据中使用统计上可靠的方法构建了动态网络表示。我们发现，这些网络在发作过程中通过一个独特的拓扑进展演变。令人惊讶的是，整体同步性变化很小，而组织的拓扑结构却发生了巨大的变化。一个大的子网在发作开始和结束前主导着网络结构，但在发作期间，它分裂成更小的组。常见的网络特征在一个人群中始终存在，并且对于每个个体，从一次发作到另一次发作都出现了相似的网络。这些结果表明，在宏观空间尺度上，癫痫不是超同步的表现，而是网络重组的表现。
