Epilepsies as dynamical diseases of brain systems: Basic models of the transition between normal and epileptic activity
Epilepsies as dynamical diseases of brain systems: Basic models of the transition between normal and epileptic activity
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DOI：
10.1111/j.0013-9580.2003.12005.x
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发表时间：
2003-01-01
期刊：
影响因子：
5.6
通讯作者：
Velis, DN
中科院分区：
文献类型：
--
作者：
da Silva, FL;Blanes, W;Velis, DN
目的：生理系统中异常动力学的发生可以表现为特征生理变量行为的突然质变。我们假设这就是癫痫病在大脑中发生的情况。我们认为癫痫中涉及的神经元网络具有多稳态动力学（即，它们可以显示几个动态状态）。为了说明这一概念，我们可以假设，为了简单起见，至少有两种状态是可能的：一个发作间期的特征是正常的，显然是随机的，正在进行的活动的稳定状态，另一个特征是阵发性发生的同步振荡（癫痫发作）。通过使用非线性系统的数学术语，我们可以说这样的非线性系统有两个吸引子，描述系统输出的轨迹收敛到这两个吸引子，取决于初始条件和系统参数。在相空间中，对应于两个状态的吸引域被所谓的“分界线”分开。“我们提出，示意性地，正常进行和癫痫发作活动之间的过渡可以根据三个基本模型发生：模型I：在某些癫痫大脑（例如，在特发性原发性全身性癫痫的不发作中），“正常稳态”和“阵发性”吸引子之间的距离与正常脑的吸引子之间的距离相比非常小（可能是由于遗传和/或发育因素）。在前一种情况下，某些变量的离散随机波动可能足以发生向阵发性状态的转变。模型II和模型III：在其他类型的癫痫脑中（例如，边缘皮质癫痫），“正常稳态”和“阵发性”吸引子之间的距离通常相当大，使得随机波动本身通常不能触发癫痫发作。然而，在这些大脑中，神经元网络具有异常特征，其特征在于非常容易受到内源性（模型II）和/或外源性（模型III）因素的影响的不稳定参数。在这些情况下，这些临界参数可能会随着时间逐渐改变，以这种方式，吸引子可以逐渐或突然变形，结果是正常状态的吸引盆和分界线之间的距离趋于零。这可能会导致，最终，过渡到一个seizure.Results：在这些模型中的癫痫发作之前的系统的动态的变化，或者可以在EEG中检测到，因此，癫痫发作的路线可能是可预测的，或者可能是不可观察的，通过使用仅测量的动态状态。然而，值得注意的是，在某些情况下，在正在进行的EEG活动的动态变化明显之前，通过使用适当的刺激配置可以发现底层网络的兴奋性状态的变化。一个典型的例子，我们在这里讨论的模型III是光敏epilepsy.Conclusions：我们提出了一个概述，这些基本模型，结合信号分析和神经生理记录的基础上进行模拟，通过使用计算模型的神经网络。我们特别注意最近的模型研究和新的实验结果，同时分析EEG功能之前边缘系统癫痫发作和间歇性光刺激，之前过渡到阵发性癫痫活动。
Purpose: The occurrence of abnormal dynamics in a physiological system can become manifest as a sudden qualitative change in the behavior of characteristic physiologic variables. We assume that this is what happens in the brain with regard to epilepsy. We consider that neuronal networks involved in epilepsy possess multistable dynamics (i.e., they may display several dynamic states). To illustrate this concept, we may assume, for simplicity, that at least two states are possible: an interictal one characterized by a normal, apparently random, steady-state of ongoing activity, and another one that is characterized by the paroxysmal occurrence of a synchronous oscillations (seizure).Methods: By using the terminology of the mathematics of nonlinear systems, we can say that such a bistable system has two attractors, to which the trajectories describing the system's output converge, depending on initial conditions and on the system's parameters. In phase-space, the basins of attraction corresponding to the two states are separated by what is called a "separatrix." We propose, schematically, that the transition between the normal ongoing and the seizure activity can take place according to three basic models:Model I: In certain epileptic brains (e.g., in absence seizures of idiopathic primary generalized epilepsies), the distance between "normal steady-state" and "paroxysmal" attractors is very small in contrast to that of a normal brain (possibly due to genetic and/or developmental factors). In the former, discrete random fluctuations of some variables can be sufficient for the occurrence of a transition to the paroxysmal state. In this case, such seizures are not predictable.Model II and model III: In other kinds of epileptic brains (e.g., limbic cortex epilepsies), the distance between "normal steady-state" and "paroxysmal" attractors is, in general, rather large, such that random fluctuations, of themselves, are commonly not capable of triggering a seizure. However, in these brains, neuronal networks have abnormal features characterized by unstable parameters that are very vulnerable to the influence of endogenous (model II) and/or exogenous (model III) factors. In these cases, these critical parameters may gradually change with time, in such a way that the attractor can deform either gradually or suddenly, with the consequence that the distance between the basin of attraction of the normal state and the separatrix tends to zero. This can lead, eventually, to a transition to a seizure.Results: The changes of the system's dynamics preceding a seizure in these models either may be detectable in the EEG and thus the route to the seizure may be predictable, or may be unobservable by using only measurements of the dynamical state. It is thinkable, however, that in some cases, changes in the excitability state of the underlying networks may be uncovered by using appropriate stimuli configurations before changes in the dynamics of the ongoing EEG activity are evident. A typical example of model III that we discuss here is photosensitive epilepsy.Conclusions: We present an overview of these basic models, based on neurophysiologic recordings combined with signal analysis and on simulations performed by using computational models of neuronal networks. We pay especial attention to recent model studies and to novel experimental results obtained while analyzing EEG features preceding limbic seizures and during intermittent photic stimulation that precedes the transition to paroxysmal epileptic activity.