Article
What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics
Author/s | Soler Toscano, Fernando
![]() ![]() ![]() ![]() ![]() ![]() ![]() Galadí García, Javier Alejandro Escrichs, Anira Sanz Perl, Y. López González, Ane Sitt, Jacobo D. Annen, Jitka Gosseries, Olivia Thibaut, Aurore Panda, Rajanikant Esteban, Francisco J. Laureys, Steven Kringelbach, M.L. Langa Rosado, José Antonio ![]() ![]() ![]() ![]() ![]() ![]() ![]() Deco, Gustavo |
Department | Universidad de Sevilla. Departamento de Filosofía y Lógica y Filosofía de la Ciencia Universidad de Sevilla. Departamento de Ecuaciones Diferenciales y Análisis Numérico |
Publication Date | 2022 |
Deposit Date | 2023-08-03 |
Published in |
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Abstract | The self-organising global dynamics underlying brain states emerge from complex recursive
nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating ... The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or ‘information structure’), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision. |
Funding agencies | Junta de Andalucía Ministerio de Ciencia, Innovación y Universidades (MICINN). España Universidad de Jaén Fundación Alicia Koplowitz Unión Europea. Horizonte 2020 Swiss National Science Foundation Fonds de la Recherche Scientifique (FNRS). Bélgica National Natural Science Foundation of China |
Project ID. | P20_00592
![]() PGC2018-096540-B-I00 ![]() US-1254251 ![]() MSALAS-2022-19827 ![]() PAIUJA-EI_CTS02_2021 ![]() OTR08262-2021 ![]() PID2019-105772GB-I00 /AEI/10.13039/501100011033 ![]() Marie Sklodowska-Curie grant 896354 ![]() Sinergia grant no. 170873 ![]() Human Brain Project SGA3 ![]() Luminous project H2020-FETOPEN-2014-2015-RIA ![]() FP7-HEALTH-602150 ![]() Joint Research Project 81471100 ![]() EU-2020-MSCA-RISE-778234 ![]() |
Citation | Soler Toscano, F., Galadí García, J.A., Escrichs, A., Sanz Perl, Y., López González, A., Sitt, J.D.,...,Deco, G. (2022). What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. PLoS Computational Biology, 18 (9), e1010412, 1-20. https://doi.org/10.1371/journal.pcbi.1010412. |
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