University of Rennes, INSERM, LTSI-U1099, 35000 Rennes, France.
Neuroelectrics, Av. Tibidabo 47b, 08035 Barcelona, Spain.
Comput Biol Med. 2024 Sep;180:108934. doi: 10.1016/j.compbiomed.2024.108934. Epub 2024 Jul 29.
Understanding the pathophysiological dynamics that underline Interictal Epileptiform Events (IEEs) such as epileptic spikes, spike-and-waves or High-Frequency Oscillations (HFOs) is of major importance in the context of neocortical refractory epilepsy, as it paves the way for the development of novel therapies. Typically, these events are detected in Local Field Potential (LFP) recordings obtained through depth electrodes during pre-surgical investigations. Although essential, the underlying pathophysiological mechanisms for the generation of these epileptic neuromarkers remain unclear. The aim of this paper is to propose a novel neurophysiologically relevant reconstruction of the neocortical microcircuitry in the context of epilepsy. This reconstruction intends to facilitate the analysis of a comprehensive set of parameters encompassing physiological, morphological, and biophysical aspects that directly impact the generation and recording of different IEEs.
a novel microscale computational model of an epileptic neocortical column was introduced. This model incorporates the intricate multilayered structure of the cortex and allows for the simulation of realistic interictal epileptic signals. The proposed model was validated through comparisons with real IEEs recorded using intracranial stereo-electroencephalography (SEEG) signals from both humans and animals. Using the model, the user can recreate epileptiform patterns observed in different species (human, rodent, and mouse) and study the intracellular activity associated with these patterns.
Our model allowed us to unravel the relationship between glutamatergic and GABAergic synaptic transmission of the epileptic neural network and the type of generated IEE. Moreover, sensitivity analyses allowed for the exploration of the pathophysiological parameters responsible for the transitions between these events. Finally, the presented modeling framework also provides an Electrode Tissue Model (ETI) that adds realism to the simulated signals and offers the possibility of studying their sensitivity to the electrode characteristics.
The model (NeoCoMM) presented in this work can be of great use in different applications since it offers an in silico framework for sensitivity analysis and hypothesis testing. It can also be used as a starting point for more complex studies.
理解癫痫样棘波、棘波-慢波或高频振荡（HFO）等发作间期癫痫样事件（IEE）背后的病理生理动力学在新皮层耐药性癫痫的背景下至关重要，因为它为新疗法的发展铺平了道路。通常，这些事件是在术前研究中通过深度电极获得的局部场电位（LFP）记录中检测到的。尽管这是必不可少的，但这些癫痫神经标志物产生的潜在病理生理机制仍不清楚。本文的目的是提出一种新的神经生理学相关的癫痫皮层微电路重建。这种重建旨在方便分析一套全面的参数，包括对不同 IEE 的产生和记录有直接影响的生理、形态和生物物理方面。
引入了一种新的癫痫皮层柱的微尺度计算模型。该模型结合了皮层的复杂多层结构，允许模拟真实的发作间期癫痫信号。通过与使用颅内立体脑电图（SEEG）信号从人类和动物记录的真实 IEE 进行比较，验证了所提出的模型。使用该模型，用户可以重现不同物种（人类、啮齿动物和小鼠）中观察到的癫痫样模式，并研究与这些模式相关的细胞内活动。
我们的模型使我们能够揭示癫痫神经网络中谷氨酸能和 GABA 能突触传递与产生的 IEE 类型之间的关系。此外，敏感性分析允许探索导致这些事件之间转换的病理生理参数。最后，所提出的建模框架还提供了一个电极组织模型（ETI），它增加了模拟信号的真实性，并提供了研究其对电极特性的敏感性的可能性。
本文提出的模型（NeoCoMM）可用于不同的应用，因为它提供了一个用于敏感性分析和假设检验的计算框架。它也可以作为更复杂研究的起点。
