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. 2020 Oct 23;3:606. doi: 10.1038/s42003-020-01331-3

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© The Author(s) 2020

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. 1 — (1) Constructing surrogate data of the connectome by permuting structural connectivity weights of the actual data with geometric and/or topological constraints depicted in Fig. 2. Blue lines represent regional structural connections (A: anterior; P: posterior; L: left; R: right). (2) Simulating spontaneous oscillatory neural signal using phase oscillator models coupled based on structural connectivity weights and lengths in the connectome. (3) Converting simulated neural signal into simulated rs-fMRI signal using a hemodynamic model. (4) Computing time-resolved functional connectivity of simulated rs-fMRI signal using tapered sliding windows. (5) Calculating global network measures of time-resolved functional connectivity to track dynamic fluctuations between segregated and integrated patterns of functional connectivity. Small circles represent brain regions, and lines between them indicate regional functional connections.