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. 2021 Oct 26;11:21036. doi: 10.1038/s41598-021-00508-y

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Summary of analyses steps. Group independent components (IC) analysis decomposes resting-state data of the total 70 subjects into 100 IC components. GICA back-reconstruction is used to estimate subject-specific IC maps and time courses. From the stationary functional connectivity (FC) maps, subject-specific time courses were segmented into 55 s (22 TRs) and moved by 1 TR (2.5 s) = 213 windows. These windows were used to calculate the FC covariance matrices using the sliding window approach. Secondly, these matrices (per subject) were used to classify FC into clustered FC patterns (states) using a k-means clustering approach. From this technique, we obtained temporal metrics such as fractional time windows, the mean of the dwell time per state and number of transitions. Finally, we applied the meta-state dynamic method that models the windowed FC time courses from the sliding window approach as weighted sums of maximally independent connectivity patterns (CP)³⁰. Each CP is recast as a discretized vector of CP weights called meta-state. We used a k-means clustering algorithm for data reduction. We obtained the following metrics: number of meta-states, change of meta-states, meta-state span and total distance between clusters (i.e., CP). The Surf Ice software (https://www.nitrc.org/projects/surfice/) was used to plot the brains, and connectivity matrices were extracted from Matlab R2020b (The MathWorks, Inc., Natick, Massachusetts).