首页> 外文期刊>Clinical neurophysiology >Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures.
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Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures.

机译:小世界网络和癫痫病:脑内记录的颞中叶癫痫发作的图论分析。

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OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. SIGNIFICANCE: Analysis of neuronal networks during seizures may provide insight into seizure genesis and development.
机译:目的:已知神经网络具有所谓的“小世界”形貌(以强聚类结合短路径长度为特征),可以促进同步,并可能引起癫痫发作。我们使用大脑中记录的颞叶颞叶癫痫发作测试了癫痫发作期间真正的功能性大脑网络显示小世界特征的假设。方法:我们使用同步可能性(SL)来表征7个患者的脑电图记录中5个感兴趣时段的同步模式:发作间,放电前,发作中,发作后和放电后(其中最近两个时期均为发作)和发作后。对于每个周期,从同步矩阵中重建图(抽象网络表示形式),并以聚类系数C(本地连接性度量)和最短路径长度L(整体网络集成度量)为特征。结果也与从随机网络获得的结果进行了比较。结果:癫痫发作过程中神经元网络发生了变化,在癫痫发作期间和之后,C和L的增加在α,θ和δ频段上最为明显。结论:在癫痫发作期间,即使校正同步强度的变化,神经元网络也会朝着更有序配置的方向(较高的C结合略有但明显较高的L的方向)移动。意义:癫痫发作期间对神经网络的分析可能有助于了解癫痫发作的发生和发展。

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