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Epileptic seizures from abnormal networks: Why some seizures defy predictability

机译:来自异常网络的癫痫发作:为什么某些癫痫发作无法预测

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Seizure prediction has proven to be difficult in clinically realistic environments. Is it possible that fluctuations in cortical firing could influence the onset of seizures in an ictal zone? To test this, we have now used neural network simulations in a computational model of cortex having a total of 65,536 neurons with intercellular wiring patterned after histological data. A spatially distributed Poisson driven background input representing the activity of neighboring cortex affected 1% of the neurons. Gamma distributions were fit to the interbursting phase intervals, a non-parametric test for randomness was applied, and a dynamical systems analysis was performed to search for period-1 orbits in the intervals. The non-parametric analysis suggests that intervals are being drawn at random from their underlying joint distribution and the dynamical systems analysis is consistent with a nondeterministic dynamical interpretation of the generation of bursting phases. These results imply that in a region of cortex with abnormal connectivity analogous to a seizure focus, it is possible to initiate seizure activity with fluctuations of input from the surrounding cortical regions. These findings suggest one possibility for ictal generation from abnormal focal epileptic networks. This mechanism additionally could help explain the difficulty in predicting partial seizures in some patients.
机译:在临床现实环境中,癫痫发作的预测已被证明是困难的。皮质放电的波动是否有可能影响发作区癫痫发作的发生?为了测试这一点,我们现在在皮层的计算模型中使用了神经网络模拟,该模型具有总共65,536个神经元,并在组织学数据后对细胞间布线进行了图案化。泊松驱动的空间分布背景输入代表相邻皮质的活动影响了1%的神经元。伽马分布适合于相生相间隔,应用了非参数随机性检验,并进行了动力学系统分析以搜索间隔中的周期1轨道。非参数分析表明,间隔是从其潜在的联合分布中随机抽取的,而动力学系统分析与爆发相生成的不确定性动力学解释是一致的。这些结果表明,在具有与癫痫发作相似的异常连通性的皮质区域中,有可能在周围皮质区域的输入波动的情况下启动癫痫发作活动。这些发现提示从异常局灶性癫痫网络产生发作的一种可能性。该机制还可以帮助解释在某些患者中预测部分发作的困难。

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