Université de Rennes, INSERM, LTSI-UMR 1099, F-35000 Rennes, France.
Assistance Publique-Hôpitaux de Marseille, Service d'Epileptologie et de Rythmologie Cérébrale, Hôpital La Timone, Marseille, France.
J Neural Eng. 2022 Sep 19;19(5). doi: 10.1088/1741-2552/ac8fb4.
In partial epilepsies, interictal epileptiform discharges (IEDs) are paroxysmal events observed in epileptogenic zone (EZ) and non-epileptogenic zone (NEZ). IEDs' generation and recurrence are subject to different hypotheses: they appear through glutamatergic and gamma-aminobutyric acidergic (GABAergic) processes; they may trigger seizures or prevent seizure propagation. This paper focuses on a specific class of IEDs, spike-waves (SWs), characterized by a short-duration spike followed by a longer duration wave, both of the same polarity. Signal analysis and neurophysiological mathematical models are used to interpret puzzling IED generation.Interictal activity was recorded by intracranial stereo-electroencephalography (SEEG) electrodes in five different patients. SEEG experts identified the epileptic and non-epileptic zones in which IEDs were detected. After quantifying spatial and temporal features of the detected IEDs, the most significant features for classifying epileptic and non-epileptic zones were determined. A neurophysiologically-plausible mathematical model was then introduced to simulate the IEDs and understand the underlying differences observed in epileptic and non-epileptic zone IEDs.Two classes of SWs were identified according to subtle differences in morphology and timing of the spike and wave component. Results showed that type-1 SWs were generated in epileptogenic regions also involved at seizure onset, while type-2 SWs were produced in the propagation or non-involved areas. The modeling study indicated that synaptic kinetics, cortical organization, and network interactions determined the morphology of the simulated SEEG signals. Modeling results suggested that the IED morphologies were linked to the degree of preserved inhibition.This work contributes to the understanding of different mechanisms generating IEDs in epileptic networks. The combination of signal analysis and computational models provides an efficient framework for exploring IEDs in partial epilepsies and classifying EZ and NEZ.
在部分性癫痫中，发作间期癫痫样放电 (IEDs) 是在致痫区 (EZ) 和非致痫区 (NEZ) 中观察到的阵发性事件。IED 的产生和复发受不同假说的影响：它们通过谷氨酸能和γ-氨基丁酸能 (GABA 能) 过程产生；它们可能引发癫痫发作或阻止癫痫发作传播。本文重点研究了一类特定的 IED，即棘波-慢波 (SWs)，其特征是短持续时间的棘波 followed 长持续时间的慢波，两者极性相同。信号分析和神经生理数学模型用于解释令人费解的 IED 产生。
通过颅内立体脑电图 (SEEG) 电极记录发作间期活动，在五名不同患者中进行。SEEG 专家确定了检测到 IED 的癫痫和非癫痫区。在量化检测到的 IED 的空间和时间特征后，确定了用于区分癫痫和非癫痫区的最显著特征。然后引入一个神经生理上合理的数学模型来模拟 IEDs，并了解在癫痫和非癫痫区 IED 中观察到的潜在差异。
根据棘波和慢波成分的形态和时间差异，确定了两种类型的 SWs。结果表明，类型-1 SWs 是在致痫区产生的，这些区域也参与了癫痫发作的起始，而类型-2 SWs 则是在传播或非参与区域产生的。模型研究表明，突触动力学、皮层组织和网络相互作用决定了模拟 SEEG 信号的形态。模型研究结果表明，IED 形态与抑制程度有关。
这项工作有助于理解癫痫网络中产生 IED 的不同机制。信号分析和计算模型的结合为探索部分性癫痫中的 IEDs 并对 EZ 和 NEZ 进行分类提供了有效的框架。
