Pijn J P, Velis D N, van der Heyden M J, DeGoede J, van Veelen C W, Lopes da Silva F H
Instituut voor Epilepsiebestrijding, Meer en Bosch/De Cruquiushoeve, Heemstede, The Netherlands.
Brain Topogr. 1997 Summer;9(4):249-70. doi: 10.1007/BF01464480.
An understanding of the principles governing the behavior of complex neuronal networks, in particular their capability of generating epileptic seizures implies the characterization of the conditions under which a transition from the interictal to the ictal state takes place. Signal analysis methods derived from the theory of nonlinear dynamics provide new tools to characterize the behavior of such networks, and are particularly relevant for the analysis of epileptiform activity.
We calculated the correlation dimension, tested for irreversibility, and made recurrence plots of EEG signals recorded intracranially both during interictal and ictal states in temporal lobe epilepsy patients who were surgical candidates.
Epileptic seizure activity often, but not always, emerges as a low-dimensional oscillation. In general, the seizure behaves as a nonstationary phenomenon during which both phases of low and high complexity may occur. Nevertheless a low dimension may be found mainly in the zone of ictal onset and nearby structures. Both the zone of ictal onset and the pattern of propagation of seizure activity in the brain could be identified using this type of analysis. Furthermore, the results obtained were in close agreement with visual inspection of the EEG records.
Application of these mathematical tools provides novel insights into the spatio-temporal dynamics of "epileptic brain states". In this way it may be of practical use in the localization of an epileptogenic region in the brain, and thus be of assistance in the presurgical evaluation of patients with localization-related epilepsy.
了解支配复杂神经元网络行为的原理,尤其是其产生癫痫发作的能力,意味着要对从发作间期到发作期转变发生的条件进行表征。源自非线性动力学理论的信号分析方法为表征此类网络的行为提供了新工具,并且对于癫痫样活动的分析尤为相关。
我们计算了相关维数,测试了不可逆性,并对作为手术候选对象的颞叶癫痫患者在发作间期和发作期颅内记录的脑电图信号制作了递归图。
癫痫发作活动常常(但并非总是)表现为低维振荡。一般而言,发作表现为一种非平稳现象,在此期间可能会出现低复杂度和高复杂度两个阶段。然而,低维主要可能出现在发作起始区域和附近结构中。使用这种分析类型可以识别出发作起始区域以及大脑中癫痫发作活动的传播模式。此外,所获得的结果与脑电图记录的目视检查结果非常吻合。
这些数学工具的应用为“癫痫脑状态”的时空动力学提供了新的见解。通过这种方式,它可能在大脑中癫痫源区的定位方面具有实际用途,从而有助于对与定位相关癫痫患者进行术前评估。