The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China; National Engineering Research Center for Healthcare Devices, Guangzhou 510500, China; The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an 710049, China.
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; The Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China.
Neuroimage. 2021 May 1;231:117861. doi: 10.1016/j.neuroimage.2021.117861. Epub 2021 Feb 13.
Electroencephalogram (EEG) microstate analysis is a promising and effective spatio-temporal method that can segment signals into several quasi-stable classes, providing a great opportunity to investigate short-range and long-range neural dynamics. However, there are still many controversies in terms of reproducibility and reliability when selecting different parameters or datatypes. In this study, five electrode configurations (91, 64, 32, 19, and 8 channels) were used to measure the reliability of microstate analysis at different electrode densities during propofol-induced sedation. First, the microstate topography and parameters at five different electrode densities were compared in the baseline (BS) condition and the moderate sedation (MD) condition, respectively. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were introduced to quantify the consistency of the microstate parameters. Second, statistical analysis and classification between BS and MD were performed to determine whether the microstate differences between different conditions remained stable at different electrode densities, and ICC was also calculated between the different conditions to measure the consistency of the results in a single condition. The results showed that in both the BS or MD condition, respectively, there were few significant differences in the microstate parameters among the 91-, 64-, and 32-channel configurations, with most of the differences observed between the 19- or 8-channel configurations and the other configurations. The ICC and CV data also showed that the consistency among the 91-, 64-, and 32-channel configurations was better than that among all five electrode configurations after including the 19- and 8-channel configurations. Furthermore, the significant differences between the conditions in the 91-channel configuration remained stable at the 64- and 32-channel resolutions, but disappeared at the 19- and 8-channel resolutions. In addition, the classification and ICC results showed that the microstate analysis became unreliable with fewer than 20 electrodes. The findings of this study support the hypothesis that microstate analysis of different brain states is more reliable with higher electrode densities; the use of a small number of channels is not recommended.
脑电图（EEG）微状态分析是一种很有前途和有效的时空方法，可以将信号分为几个准稳定的类别，为研究短程和长程神经动力学提供了极好的机会。然而，在选择不同参数或数据类型时，其可重复性和可靠性仍存在许多争议。在这项研究中，使用了五种电极配置（91、64、32、19 和 8 个通道），在异丙酚诱导镇静期间，在不同的电极密度下，测量微状态分析的可靠性。首先，在基线（BS）和中度镇静（MD）条件下，分别比较了五种不同电极密度下的微状态地形图和参数。引入组内相关系数（ICC）和变异系数（CV）来量化微状态参数的一致性。其次，对 BS 和 MD 进行了统计分析和分类，以确定不同条件下的微状态差异在不同电极密度下是否仍然稳定，并计算了不同条件之间的 ICC，以衡量单个条件下结果的一致性。结果表明，在 BS 或 MD 条件下，91、64 和 32 通道配置之间的微状态参数差异很小，大多数差异出现在 19 或 8 通道配置与其他配置之间。ICC 和 CV 数据还表明，在包含 19 或 8 个通道配置后，91、64 和 32 通道配置之间的一致性优于所有五个电极配置之间的一致性。此外，在 91 通道配置中，条件之间的显著差异在 64 和 32 通道分辨率下保持稳定，但在 19 和 8 通道分辨率下消失。此外，分类和 ICC 结果表明，微状态分析的可靠性随着电极数量的减少而降低，少于 20 个电极时，微状态分析不可靠。本研究的结果支持了这样一种假设，即不同脑状态的微状态分析在电极密度较高时更可靠；不建议使用少量通道。
