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. 2022 Jun 2;5:532. doi: 10.1038/s42003-022-03466-x

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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. 4 — a Top: static structure-function coupling is estimated using the functional connectivity matrix derived from the whole resting-state time-series²³, and compared with dynamic coupling. The dynamic structure-function coupling of node i corresponds to the ith row of the dynamic coupling matrix, while the static coupling corresponds to the ith element of static coupling vector. Middle: dynamic values represented as a time-series (black line) that fluctuates around the single static coupling value (blue line). Bottom: dynamic coupling values are represented as a scattered distribution of points (black) around the static coupling value (blue point). The two are compared in different cortical annotations using three summary statistics: b the probability of having a larger dynamic coupling value compared to the static coupling, c the bias, and d the variance of the dynamic coupling to reproduce the static values.