A stochastic model for EEG microstate sequence analysis
A stochastic model for EEG microstate sequence analysis
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DOI：
10.1016/j.neuroimage.2014.10.014
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发表时间：
2015-01-01
期刊：
影响因子：
5.7
通讯作者：
Schneider, Gaby
中科院分区：
文献类型：
--
作者：
Gaertner, Matthias;Brodbeck, Verena;Schneider, Gaby
假设对自发静息状态神经元活动的分析可以深入了解大脑功能。研究静息状态活动的一种无创技术是脑电图 (EEG) 以及随后的微观状态分析。该技术将记录的脑电图信号简化为一系列原型地形图，假设其能够捕获信号的重要时空特性。在对健康受试者清醒状态和睡眠三个阶段的脑电图微状态统计分析中，我们观察到微状态转换矩阵中的简单结构。它可以用一阶马尔可夫链来描述，其中从当前状态（即映射）到不同映射的转移概率不依赖于当前映射。所得的转移矩阵与观察到的转移矩阵高度一致，仅需要约 2% 的传质（1/2 L-1 距离）。在第二部分中，我们介绍了一个扩展框架，其中简单的马尔可夫链用于对潜在的底层时间连续过程进行推断。这个过程无法直接观察到，因此通常是根据全局场功率的局部最大值给出的脑电图信号的离散采样点来估计的。因此，我们提出了一种称为采样标记间隔（SMI）模型的简单随机模型，它将观察到的微观状态序列与假设的背景间隔基本过程联系起来，从而补充了专注于分析可观察微观状态序列的方法。 (C) 2014 Elsevier Inc. 保留所有权利。
The analysis of spontaneous resting state neuronal activity is assumed to give insight into the brain function. One noninvasive technique to study resting state activity is electroencephalography (EEG) with a subsequent microstate analysis. This technique reduces the recorded EEG signal to a sequence of prototypical topographical maps, which is hypothesized to capture important spatio-temporal properties of the signal. In a statistical EEG microstate analysis of healthy subjects in wakefulness and three stages of sleep, we observed a simple structure in the microstate transition matrix. It can be described with a first order Markov chain in which the transition probability from the current state (i.e., map) to a different map does not depend on the current map. The resulting transition matrix shows a high agreement with the observed transition matrix, requiring only about 2% of mass transport (1/2 L-1-distance). In the second part, we introduce an extended framework in which the simple Markov chain is used to make inferences on a potential underlying time continuous process. This process cannot be directly observed and is therefore usually estimated from discrete sampling points of the EEG signal given by the local maxima of the global field power. Therefore, we propose a simple stochastic model called sampled marked intervals (SMI) model that relates the observed sequence of microstates to an assumed underlying process of background intervals and thus, complements approaches that focus on the analysis of observable microstate sequences. (C) 2014 Elsevier Inc. All rights reserved.