A unified physiological framework of transitions between seizures, sustained ictal activity and depolarization block at the single neuron level.

IF 1.5 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Journal of Computational Neuroscience Pub Date : 2022-02-01 Epub Date: 2022-01-15 DOI:10.1007/s10827-022-00811-1
Damien Depannemaecker, Anton Ivanov, Davide Lillo, Len Spek, Christophe Bernard, Viktor Jirsa
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引用次数: 15

Abstract

The majority of seizures recorded in humans and experimental animal models can be described by a generic phenomenological mathematical model, the Epileptor. In this model, seizure-like events (SLEs) are driven by a slow variable and occur via saddle node (SN) and homoclinic bifurcations at seizure onset and offset, respectively. Here we investigated SLEs at the single cell level using a biophysically relevant neuron model including a slow/fast system of four equations. The two equations for the slow subsystem describe ion concentration variations and the two equations of the fast subsystem delineate the electrophysiological activities of the neuron. Using extracellular K+ as a slow variable, we report that SLEs with SN/homoclinic bifurcations can readily occur at the single cell level when extracellular K+ reaches a critical value. In patients and experimental models, seizures can also evolve into sustained ictal activity (SIA) and depolarization block (DB), activities which are also parts of the dynamic repertoire of the Epileptor. Increasing extracellular concentration of K+ in the model to values found during experimental status epilepticus and DB, we show that SIA and DB can also occur at the single cell level. Thus, seizures, SIA, and DB, which have been first identified as network events, can exist in a unified framework of a biophysical model at the single neuron level and exhibit similar dynamics as observed in the Epileptor.Author Summary: Epilepsy is a neurological disorder characterized by the occurrence of seizures. Seizures have been characterized in patients in experimental models at both macroscopic and microscopic scales using electrophysiological recordings. Experimental works allowed the establishment of a detailed taxonomy of seizures, which can be described by mathematical models. We can distinguish two main types of models. Phenomenological (generic) models have few parameters and variables and permit detailed dynamical studies often capturing a majority of activities observed in experimental conditions. But they also have abstract parameters, making biological interpretation difficult. Biophysical models, on the other hand, use a large number of variables and parameters due to the complexity of the biological systems they represent. Because of the multiplicity of solutions, it is difficult to extract general dynamical rules. In the present work, we integrate both approaches and reduce a detailed biophysical model to sufficiently low-dimensional equations, and thus maintaining the advantages of a generic model. We propose, at the single cell level, a unified framework of different pathological activities that are seizures, depolarization block, and sustained ictal activity.

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在单个神经元水平上,癫痫发作、持续的脑电图活动和去极化阻滞之间转换的统一生理框架。
在人类和实验动物模型中记录的大多数癫痫发作可以用一个通用的现象学数学模型来描述,即癫痫患者。在该模型中,类癫痫事件(SLEs)由一个慢变量驱动,分别在癫痫发作和偏移时通过鞍节点(SN)和同斜分叉发生。在这里,我们使用生物物理相关的神经元模型,包括四个方程的慢/快系统,在单细胞水平上研究SLEs。慢子系统的两个方程描述了离子浓度的变化,快子系统的两个方程描述了神经元的电生理活动。使用细胞外K+作为慢变量,我们报道当细胞外K+达到临界值时,具有SN/同斜分叉的SLEs很容易在单细胞水平发生。在患者和实验模型中,癫痫发作也可以演变为持续的发作活动(SIA)和去极化阻滞(DB),这些活动也是癫痫患者动态功能的一部分。将模型中的细胞外K+浓度增加到实验癫痫持续状态和DB时的值,我们发现SIA和DB也可以发生在单细胞水平。因此,癫痫发作、SIA和DB,这些首次被确定为网络事件,可以存在于单个神经元水平的生物物理模型的统一框架中,并表现出与在癫痫患者中观察到的相似的动态。作者总结:癫痫是一种以发作为特征的神经系统疾病。癫痫发作的特点是在实验模型在宏观和微观尺度使用电生理记录。实验工作允许建立癫痫发作的详细分类,这可以用数学模型来描述。我们可以区分两种主要类型的模型。现象学(一般)模型只有很少的参数和变量,并允许详细的动力学研究,通常捕获在实验条件下观察到的大部分活动。但它们也有抽象的参数,使得生物学解释变得困难。另一方面,由于它们所代表的生物系统的复杂性,生物物理模型使用了大量的变量和参数。由于解的多样性,很难提取出一般的动态规则。在目前的工作中,我们整合了这两种方法,并将详细的生物物理模型简化为足够低维的方程,从而保持了通用模型的优势。我们提出,在单细胞水平上,不同病理活动的统一框架是癫痫发作,去极化阻滞和持续的癫痫活动。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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