Children's Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA, 90027, USA.
Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Stefan-Meier-Straße 8, 79104, Freiburg, Germany.
Brain Topogr. 2024 Jul;37(4):621-645. doi: 10.1007/s10548-023-01003-5. Epub 2023 Sep 11.
Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.
微状态分析是一种多变量方法，可用于研究人类大脑活动的 EEG 记录中大规模神经网络的时间动态。为了满足对这种方法的极大兴趣，我们提供了第一个用于标准化识别、可视化和量化静息状态 EEG 数据中微状态的开源 EEGLAB 工具箱的全面更新版本。该工具箱允许科学家们：(i) 使用地形聚类方法识别个体、平均和总平均微状态图，(ii) 检查数据质量并检测异常图，(iii) 根据已发表的地图可视化、排序和标记个体、平均和总平均微状态图，(iv) 比较组和总平均微状态图的地形相似性并量化共享方差，(v) 获取个体 EEG 中微状态类的时间动态，(vi) 导出这些微状态的时间动态的定量信息以进行统计检验，最后，(vii) 使用拓扑方差分析（TANOVA）测试组和条件之间的地形差异。在这里，我们使用 34 个公开可用的静息状态 EEG 记录的示例数据集逐步介绍该工具箱，以便您跟随本教程进行操作。本文的目的是：(a) 为科学界提供一个标准化的、免费的静息状态微状态分析工具箱，(b) 通过逐步教程使研究人员能够使用微状态分析的最佳实践，以及 (c) 通过提供以前不可用的功能和关于微状态分析中所需的关键决策的建议，提高微状态研究的方法学标准。
