Escrichs, AniraSanz Perl, YonatanUribe, CarmeCamara, EstelaTürker, BasakPyatigorskaya, NadyaLópez-González, Ane, 1993-Pallavicini, CarlaPanda, RajanikantAnnen, JitkaGosseries, OliviaLaureys, StevenNaccache, LionelSitt, JacoboLaufs, HelmutTagliazucchi, EnzoKringelbach, Morten L.Deco, Gustavo2023-03-062023-03-062022Escrichs A, Sanz Perl Y, Uribe C, Camara E, Türker B, Pyatigorskaya N, López-González A, Pallavicini C, Panda R, Annen J, Gosseries O, Laureys S, Naccache L, Sitt JD, Laufs H, Tagliazucchi E, Kringelbach ML, Deco G. Unifying turbulent dynamics framework distinguishes different brain states. Commun Biol. 2022;5:638. DOI: 10.1038/s42003-022-03576-62399-3642http://hdl.handle.net/10230/56062Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto’s turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures. This allowed studying of the information encoding capabilities in given brain states. Overall, this framework demonstrates that elements from turbulence theory provide excellent tools for describing and differentiating between brain states.application/pdfeng© The Author(s) 2022. 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/info:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s42003-022-03576-6Computational neuroscienceFunctional magnetic resonance imaginginfo:eu-repo/semantics/openAccess