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. 2022 Jun 29;5:638. doi: 10.1038/s42003-022-03576-6

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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 The plots show the level of Kuramoto amplitude turbulence at different spatial scales, from λ = 0.01 (100 mm) to λ = 0. 3 (3 mm) in steps of 0.03, and show the comparison between brain states for λ = 0.01, λ = 0.12 and λ = 0.3. The meditation state showed significant increases in Kuramoto amplitude turbulence compared to the resting state only on higher scales. The DS shows significantly lower Kuramoto amplitude turbulence than the resting state across all spatial scales. By contrast, the Kuramoto amplitude turbulence showed significant decreases in R_MCS and R_UWS states in lower lambda scales but significant increases in higher scales compared to R_CNT. b The plots were computed as the linear fit of the mean level of Kuramoto amplitude turbulence at each scale for the three brain states for the DOC dataset (i.e., R_CNT, R_MCS, and R_UWS) and two brain states for sleep and meditation datasets (i.e., W, DS, and R, M, respectively). The plots display the obtained slopes as a function of the scale. In particular, DOC showed negative slopes at lower scales and increased with the scales up to positive slopes. The sleep dataset presented negative slopes at lower scales, increased up to λ = 0.12, and a negative slope value was kept almost constant. The meditation dataset also increased with scale but presented less variability than the other datasets. Dashed vertical lines indicate the scales displayed in A and the horizontal red dashed line highlights the zero slope. c We computed the transfer correlation (|A^λ|), which measures how the information travels across space at different spatial scales, i.e., we show the results as a constant k - |A^λ|, with k = 3 |A^λ|. The meditation state presents no significant differences on any scale compared to the resting state. In contrast, the transfer correlation significantly decreased for DS and R_MCS, R_UWS states compared to the resting state across all scales. d We performed the same computation as in panel B for the transfer correlation measure. In this case, DOC and sleep datasets presented a similar slope-scale relationship, whereas the meditation dataset presented less variability across scales. In the figure, P-values were assessed using the Wilcoxon rank-sum test and corrected for multiple comparisons, *P < 0.05, **P < 0.01 and ***P < 0.001.