Rué-Queralt, JoanStevner, AngusTagliazucchi, EnzoLaufs, HelmutKringelbach, Morten L.Deco, GustavoAtasoy, Selen2022-07-012022-07-012021Rué-Queralt J, Stevner A, Tagliazucchi E, Laufs H, Kringelbach ML, Deco G, Atasoy S. Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep. Commun Biol. 2021;4:854. DOI: 10.1038/s42003-021-02369-72399-3642http://hdl.handle.net/10230/53650Current state-of-the-art functional magnetic resonance imaging (fMRI) offers remarkable imaging quality and resolution, yet, the intrinsic dimensionality of brain dynamics in different states (wakefulness, light and deep sleep) remains unknown. Here we present a method to reveal the low dimensional intrinsic manifold underlying human brain dynamics, which is invariant of the high dimensional spatio-temporal representation of the neuroimaging technology. By applying this intrinsic manifold framework to fMRI data acquired in wakefulness and sleep, we reveal the nonlinear differences between wakefulness and three different sleep stages, and successfully decode these different brain states with a mean accuracy across participants of 96%. Remarkably, a further group analysis shows that the intrinsic manifolds of all participants share a common topology. Overall, our results reveal the intrinsic manifold underlying the spatiotemporal dynamics of brain activity and demonstrate how this manifold enables the decoding of different brain states such as wakefulness and various sleep stages.application/pdfeng© The Author(s) 2021 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://doi.org/10.1038/s42003-021-02369-7ConsciousnessDynamical systemsNetwork modelsinfo:eu-repo/semantics/openAccess