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脑内癫痫发作间期放电的建模:网络相互作用的证据。

Modeling of intracerebral interictal epileptic discharges: Evidence for network interactions.

机构信息

Academic Center for Epileptology Kempenhaeghe & Maastricht University Medical Center, The Netherlands; Department of Mathematics & Computer Science, Eindhoven University of Technology, The Netherlands.

Academic Center for Epileptology Kempenhaeghe & Maastricht University Medical Center, The Netherlands; Department of Biomedical Engineering, Eindhoven University of Technology, The Netherlands.

出版信息

Clin Neurophysiol. 2018 Jun;129(6):1276-1290. doi: 10.1016/j.clinph.2018.03.021. Epub 2018 Apr 11.

Abstract

OBJECTIVE

The interictal epileptic discharges (IEDs) occurring in stereotactic EEG (SEEG) recordings are in general abundant compared to ictal discharges, but difficult to interpret due to complex underlying network interactions. A framework is developed to model these network interactions.

METHODS

To identify the synchronized neuronal activity underlying the IEDs, the variation in correlation over time of the SEEG signals is related to the occurrence of IEDs using the general linear model. The interdependency is assessed of the brain areas that reflect highly synchronized neural activity by applying independent component analysis, followed by cluster analysis of the spatial distributions of the independent components. The spatiotemporal interactions of the spike clusters reveal the leading or lagging of brain areas.

RESULTS

The analysis framework was evaluated for five successfully operated patients, showing that the spike cluster that was related to the MRI-visible brain lesions coincided with the seizure onset zone. The additional value of the framework was demonstrated for two more patients, who were MRI-negative and for whom surgery was not successful.

CONCLUSIONS

A network approach is promising in case of complex epilepsies.

SIGNIFICANCE

Analysis of IEDs is considered a valuable addition to routine review of SEEG recordings, with the potential to increase the success rate of epilepsy surgery.

摘要

目的

与发作放电相比,立体定向脑电图(SEEG)记录中的发作间期癫痫放电(IEDs)通常更为丰富,但由于复杂的潜在网络相互作用,难以解释。本文开发了一个框架来模拟这些网络相互作用。

方法

为了识别 IEDs 下的同步神经元活动,使用广义线性模型将 SEEG 信号随时间的相关变化与 IEDs 的发生相关联。通过应用独立成分分析来评估反映高度同步神经活动的脑区的相互依赖性,然后对独立成分的空间分布进行聚类分析。棘波簇的时空相互作用揭示了脑区的领先或滞后。

结果

该分析框架在五名成功手术的患者中进行了评估,结果表明与 MRI 可见脑损伤相关的棘波簇与癫痫发作起始区相吻合。对于另外两名 MRI 阴性且手术不成功的患者,该框架的附加价值得到了证明。

结论

在复杂癫痫的情况下,网络方法具有很大的应用潜力。

意义

IEDs 的分析被认为是常规 SEEG 记录审查的有价值的补充,有可能提高癫痫手术的成功率。

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