INSERM, U642, University of Rennes 1, LTSI, and Neurology Department, University Hospital, Rennes, France.
Ann Neurol. 2012 Mar;71(3):342-52. doi: 10.1002/ana.22610.
In partial epilepsies, interictal epileptic spikes (IESs) and fast ripples (FRs) represent clinically relevant biomarkers characteristic of epileptogenic networks. However, their specific significance and the pathophysiological changes leading to either FRs or IESs remain elusive. The objective of this study was to analyze the conditions in which hyperexcitable networks can generate either IESs or FRs and to reveal shared or distinct mechanisms that underlie both types of events.
This study is the first to comparatively analyze mechanisms that induce either IESs or FRs using an approach that combines computational modeling and experimental data (in vivo and in vitro). A detailed CA1 hippocampal network model is introduced. A parameter sensitivity analysis was conducted to determine which model parameters (cell related and network related) allow the most accurate simulation of FRs and IESs.
Our model indicates that although FRs and IESs share certain common mechanisms (shifted gamma-aminobutyric acid [GABA]A reversal potential, altered synaptic transmission), there are also critical differences in terms of number of pyramidal cells involved (small vs large), spatial distribution of hyperexcitable pyramidal cells (clustered vs uniform), and firing patterns (weakly vs highly synchronized). In vitro experiments verified that subtle changes in GABAergic and glutamatergic transmission favor either FRs or IESs, as predicted by the model.
This study provides insights into the interpretation of 2 interictal markers observed in intracerebral electroencephalographic data. Depending on the degree and spatiotemporal features of hyperexcitability, not only IESs or FRs are generated but also transitions between both types of events.
在部分性癫痫中，发作间期癫痫棘波（IESs）和快涟漪（FRs）代表与致痫网络相关的临床相关生物标志物。然而，它们的具体意义以及导致 FRs 或 IESs 的病理生理变化仍然难以捉摸。本研究的目的是分析超兴奋性网络产生 IESs 或 FRs 的条件，并揭示导致这两种事件的共同或独特机制。
本研究首次使用结合计算建模和实验数据（体内和体外）的方法，对产生 IESs 或 FRs 的机制进行比较分析。引入了一个详细的 CA1 海马网络模型。进行了参数敏感性分析，以确定哪些模型参数（与细胞相关和与网络相关）允许对 FRs 和 IESs 进行最准确的模拟。
我们的模型表明，尽管 FRs 和 IESs 具有某些共同机制（γ-氨基丁酸 [GABA]A 反转电位偏移，改变突触传递），但在涉及的锥体神经元数量（小 vs 大）、超兴奋性锥体神经元的空间分布（聚类与均匀）和放电模式（弱与高度同步）方面也存在关键差异。体外实验验证了 GABA 能和谷氨酸能传递的细微变化有利于 FRs 或 IESs 的产生，这与模型的预测一致。
本研究为解释脑内脑电图数据中观察到的 2 种发作间期标志物提供了深入了解。根据超兴奋性的程度和时空特征，不仅会产生 IESs 或 FRs，还会产生这两种事件之间的转换。
