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. 2023 Mar 20;3(3):254–263. doi: 10.1038/s43588-023-00410-9

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© The Author(s) 2023

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. 3 — a, Top: extreme events identified on a single sensor (red dots) using e = ± 3 s.d. (horizontal lines). Bottom: the resulting raster of extreme events shown across 273 MEG sensors of a single subject. b, Top: A_ϵ is defined as the total number of extreme events across all sensors in a time bin ϵ_n = nT, a multiple of the sampling interval T. Bottom: representative sequences of network excitation extracted from the raster in the top panel for increasing ϵ_n. c, Rescaled distributions P(A_ϵ) for a range of ϵ_n (different plot symbols) collapse onto a single non-exponential master curve for both the data (green symbols represent the average over subjects) and model simulated at baseline parameters, with K = 100 subsystems of n_sub = 1,000 neurons each (violet symbols) (N = 10⁵). The corresponding distribution in phase-scrambled MEG signals shows an exponential behavior, with absence of high excitation events (the brown line represents surrogate data). Inset: rescaled P(A_ϵ) (green symbols represent the average over n = 14 MEG subjects, whereas violet symbols represent the average over the model simulations) and respective s.d. (colored areas) shown for ϵ = 2T. d, Rescaled distributions of quiescence durations, P(I_ϵ) collapse onto a single master curve for different values of ϵ. The plotting conventions and model simulation details are the same as in c. Top inset: rescaled P(I_ϵ) (green symbols represent the average of n = 14 MEG subjects, whereas violet symbols are the average over model simulations) and respective s.d. (colored area) shown for ϵ = 2T. Bottom inset: the probability P₀ of finding a quiescent time bin approximately scales as $P_{0} = \exp (- r_{0} ϵ^{β_{I}})$ with ϵ; β_I = 0.582 ± 0.013 and β_I = 0.610 ± 0.012 for the data and model, respectively; β_I = 0.996 ± 0.001 for surrogate data. The exponent β_I was estimated via an ordinary linear least-square fit y = ax + b, where $y = ln (- ln P_{0})$ and $x = ln (ϵ)$ .

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