Brain network analysis of seizure evolution
Brain network analysis of seizure evolution
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DOI：
10.5735/086.045.0504
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发表时间：
2008-10-29
影响因子：
0.7
通讯作者：
Anderson, Michael L.
中科院分区：
文献类型：
--
作者：
Chaovalitwongse, Wanpracha A.;Suharitdamrong, Wichai;Anderson, Michael L.
人脑是最复杂的生物系统之一。神经科学家试图通过对神经元兴奋性和突触传递的详细分析来了解大脑的功能。在这项研究中，我们提出了一个网络分析框架来研究癫痫发作的演变。我们应用一种源自信息理论的信号处理方法来研究神经元活动的同步性，这可以通过脑电图（EEG）记录来捕捉。提出了两种网络理论方法对脑网络的同步进行全局建模。我们观察到一些与癫痫发作发展有关的独特模式，这可以用来阐明癫痫发作前一段时间内由癫痫发生过程控制的脑功能。所提出的框架可以提供大脑网络的全局结构模式，并可用于动态系统（如大脑）的模拟研究，以预测即将发生的事件（如癫痫发作）。为了将来分析长期的脑电图记录，我们讨论了如何应用马尔可夫链蒙特卡罗（MCMC）方法来估计团参数。这种MCMC框架非常适合这项工作，因为癫痫进化可以被认为是一个具有不可观察状态变量和非线性的系统。
The human brain is one of the most complex biological systems. Neuroscientists seek to understand the brain function through detailed analysis of neuronal excitability and synaptic transmission. In this study, we propose a network analysis framework to stud), the evolution of epileptic seizures. We apply a signal processing approach, derived from information theory, to investigate the synchronization of neuronal activities, which can be captured by electroencephalogram (EEG) recordings. Two network-theoretic approaches are proposed to globally model the synchronization of the brain network. We observe some unique patterns related to the development of epileptic seizures, which can be used to illuminate the brain function governed by the epileptogenic process during the period before a seizure. The proposed framework can provide a global structural patterns in the brain network and may be used in the simulation study of dynamical systems (e.g. the brain) to predict oncoming events (e.g. seizures). To analyze long-term EEG recordings in the future, we discuss how the Markov-Chain Monte Carlo (MCMC) methodology can be applied to estimate the clique parameters. This MCMC framework fits very well with this work as the epileptic evolution can be considered to be a system with unobservable state variables and nonlinearities.