EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects
EEG Microstates Temporal Dynamics Differentiate Individuals with Mood and Anxiety Disorders From Healthy Subjects
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DOI：
10.3389/fnhum.2019.00056
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发表时间：
2019-02-26
影响因子：
2.9
通讯作者：
Victor, Teresa A.
中科院分区：
文献类型：
--
作者：
Al Zoubi, Obada;Mayeli, Ahmed;Victor, Teresa A.
脑电图（EEG）以高时间分辨率测量大脑的电生理时空活动。脑电信号的多通道和宽带分析被称为脑电微态（EEG-ms），可以表征这种动态神经元活动。由于越来越多的证据表明EEG-ms与功能磁共振成像（fMRI）发现的心理活动和大规模脑网络有关，因此受到了广泛的关注。空间独立的EEG-ms是准静止的地形（例如，稳定的，持续几十毫秒），通常分为四个典型类别（微状态a到D）。通过将脑电信号聚类在脑电信号全局场强（GFP）最大值附近，可以识别出脑电信号。我们检查了情绪和焦虑（MA）障碍受试者（n = 61）和健康对照组（n = 52）的脑电图-质谱特性和动态。在两组中，我们发现四种不同的EEG-ms (A到D)，在队列中没有差异。这表明在队列中缺乏显著的皮质结构异常，否则会影响脑电图-质谱的地形。然而，正如脑电图-质谱特性所反映的那样，两组的脑网络动态显著不同。与HC相比，MA队列在EEG-ms B和D之间的转换概率较低，而从a到D和从B到C的转换概率较高，并且在微观状态C的平均持续时间上有显著的趋势。此外，我们利用最近引入的理论方法来分析EEG-ms的时间依赖性。结果表明，与HC基团相比，MA基团的转移矩阵具有更高的对称性和平稳性。此外，我们发现微观状态之间的时间依赖性有所提高，特别是MA组在微观状态B中。各组间脑电图-质谱时间依赖性的改变表明，情绪障碍和焦虑障碍的脑异常反映了异常的神经动力学和某些脑状态的时间停留（即情绪障碍和焦虑障碍受试者在不同脑状态之间切换的动态性较弱）。
Electroencephalography (EEG) measures the brain's electrophysiological spatio-temporal activities with high temporal resolution. Multichannel and broadband analysis of EEG signals is referred to as EEG microstates (EEG-ms) and can characterize such dynamic neuronal activity. EEG-ms have gained much attention due to the increasing evidence of their association with mental activities and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). Spatially independent EEG-ms are quasi-stationary topographies (e.g., stable, lasting a few dozen milliseconds) typically classified into four canonical classes (microstates A through D). They can be identified by clustering EEG signals around EEG global field power (GFP) maxima points. We examined the EEG-ms properties and the dynamics of cohorts of mood and anxiety (MA) disorders subjects (n = 61) and healthy controls (HCs; n = 52). In both groups, we found four distinct classes of EEG-ms (A through D), which did not differ among cohorts. This suggests a lack of significant structural cortical abnormalities among cohorts, which would otherwise affect the EEG-ms topographies. However, both cohorts' brain network dynamics significantly varied, as reflected in EEG-ms properties. Compared to HC, the MA cohort features a lower transition probability between EEG-ms B and D and higher transition probability from A to D and from B to C, with a trend towards significance in the average duration of microstate C. Furthermore, we harnessed a recently introduced theoretical approach to analyze the temporal dependencies in EEG-ms. The results revealed that the transition matrices of MA group exhibit higher symmetrical and stationarity properties as compared to HC ones. In addition, we found an elevation in the temporal dependencies among microstates, especially in microstate B for the MA group. The determined alteration in EEG-ms temporal dependencies among the cohorts suggests that brain abnormalities in mood and anxiety disorders reflect aberrant neural dynamics and a temporal dwelling among ceratin brain states (i.e., mood and anxiety disorders subjects have a less dynamicity in switching between different brain states).