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. 2022 May 16;13:2693. doi: 10.1038/s41467-022-29775-7

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© The Author(s) 2022

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — a Empirical edge communities obtained by k-means clustering of the eFC computed on the HCP dataset. The edge labels are reshaped into an NxN matrix where each (i, j) entry represents the community label of the edge linking i to j. For each edge, the representative community assignment shown here is the statistical mode (i.e., the most common label) across 100 HCP subjects. b Predicted edge communities obtained by k-means clustering of the eFC predicted by the static null model. The agreement with the empirical labels in panel a is exact on 84% of all 19900 edges (74% if GSR is applied). c Empirical edge cluster similarity on the HCP dataset. The similarity of edge communities involving nodes i and j is computed as the fraction of matching elements between the corresponding two rows of the edge community matrix in panel a. d Predicted edge cluster similarity based on the static null model. The Pearson correlation with the empirical edge cluster similarity in panel c is r = 0.96 (r = 0.95 if GSR is applied).