Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.
Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden.
Brain Topogr. 2024 Mar;37(2):218-231. doi: 10.1007/s10548-023-00993-6. Epub 2023 Jul 29.
Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.
在过去的十年中,EEG 静息态微状态分析已从一个利基领域发展成为一种广泛使用且被广泛接受的方法。越来越多的经验发现开始产生生物和心理状态与特定微状态类别的总体关联模式。然而,目前,不同研究中明显相似的微状态类别的这种交叉参考通常是由个别作者通过“目测”打印的模板图来完成的,缺乏系统的程序。为了提高未来研究结果的可靠性和有效性,我们提出了一种工具,可以系统地从尽可能多的已发表研究中收集模板图的实际数据,并将其全部作为空间相似性矩阵呈现。该工具还允许导入新的模板图,并从正在进行或已发表的研究中系统地提取与特定微状态图相关的发现。该工具还允许导入新的模板图,并系统地提取文献中与特定微状态图相关的发现。对 40 组包含的模板图的分析表明:(i)研究之间的模板图具有高度的相似性,(ii)相似的模板图与趋同的经验发现相关,(iii)可以从个别研究中提取代表性的元微状态。我们希望这个工具将有助于更全面、客观和综合地表示微状态的发现。