Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom; Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
Neuroimage. 2022 Sep;258:119346. doi: 10.1016/j.neuroimage.2022.119346. Epub 2022 May 31.
+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and χ analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.
微状态是一个用于脑功能微状态分析的 MATLAB 工具箱。它建立在前人 EEG 微状态文献和工具箱的基础上，包括源空间微状态分析的算法。微状态包括在静息态和基于任务的数据中进行个体和群体水平脑微状态分析的代码，包括事件相关电位/场。该工具箱包含用于可视化和执行微状态序列统计分析的功能，包括新颖的高级统计方法，如关联功能连接模式的统计检验、集群置换地形 ANOVA 和刺激响应中微状态概率的 χ 分析。此外，工具箱中还包含模拟微状态序列及其相关 M/EEG 数据的代码，可用于生成具有真实微状态的人工数据，并验证该方法。微状态与广泛使用的 M/EEG 处理工具箱集成，包括 Fieldtrip、SPM、LORETA/sLORETA、EEGLAB 和 Brainstorm，以提高可用性，并包括用于脑状态估计的预存在工具箱的包装器，如隐马尔可夫模型 (HMM-MAR) 和独立成分分析 (FastICA)，以帮助与这些技术进行直接比较。在本文中，我们首先介绍微状态，然后使用开放访问数据集进行示例分析，以演示和验证该方法。这些分析的每个分析的 MATLAB 实时脚本都包含在微状态中，用作教程并帮助重现本文档中呈现的结果。
