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What lies underneath: precise classification of brain states using time-dependent topological structure of dynamics

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dc.contributor.author Soler-Toscano, Fernando
dc.contributor.author Galadí, Javier Alejandro
dc.contributor.author Escrichs, Anira
dc.contributor.author Sanz Perl, Yonatan
dc.contributor.author López-González, Ane
dc.contributor.author Sitt, Jacobo
dc.contributor.author Annen, Jitka
dc.contributor.author Gosseries, Olivia
dc.contributor.author Thibaut, Aurore
dc.contributor.author Panda, Rajanikant
dc.contributor.author Esteban, Francisco J.
dc.contributor.author Laureys, Steven
dc.contributor.author Kringelbach, Morten L.
dc.contributor.author Langa, Jose Antonio
dc.contributor.author Deco, Gustavo
dc.date.accessioned 2023-03-06T07:33:49Z
dc.date.available 2023-03-06T07:33:49Z
dc.date.issued 2022
dc.identifier.citation Soler-Toscano F, Galadí JA, Escrichs A, Sanz Perl Y, López-González A, Sitt JD, Annen J, Gosseries O, Thibaut A, Panda R, Esteban FJ, Laureys S, Kringelbach ML, Langa JA, Deco G. What lies underneath: precise classification of brain states using time-dependent topological structure of dynamics. PLoS Comput Biol. 2022;18(9):e1010412. DOI: 10.1371/journal.pcbi.1010412
dc.identifier.issn 1553-734X
dc.identifier.uri http://hdl.handle.net/10230/56060
dc.description.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 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.
dc.description.sponsorship FST, JAG, FJE and JAL are supported by Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía (Grant number P20_00592). FJE and JAL are supported by Ministerio de Ciencia, Innovación y Universidades (PGC2018-096540-B-I00). FST, JAG, FJE and JAL are supported by Consejería de Economía, Conocimiento, Empresas y Universidad, Junta de Andalucía (US-1254251). FST is supported by Junta de Andalucía (HUM-609). JAG is supported by NextGenerationEU (Ayudas Margarita Salas, MSALAS-2022-19827). FJE is supported by Universidad de Jaén (PAIUJA-EI_CTS02_2021), Junta de Andalucía (BIO-302) and Fundación Alicia Koplowitz (OTR08262-2021). GD and AE were supported by the Spanish Research Project AWAKENING: using whole-brain models perturbational approaches for predicting external stimulation to force transitions between different brain states, ref. PID2019-105772GB-I00 /AEI/10.13039/501100011033, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU). YSP was supported by European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant 896354. ALG and GD were supported by Swiss National Science Foundation Sinergia grant no. 170873. The study was further supported by the FNRS PDR project (T.0134.21; OG, AT and SL); the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3; GD, AE, JA, OG, AT, RP and SL), and the Luminous project (H2020-FETOPEN-2014-2015-RIA; JA, OG, AT, RP and SL); the European Union’s FP7 Programme (FP7-HEALTH-602150; JA, OG, AT, RP and SL); the H2020 Marie Skłodowska-Curie Actions (EU-2020-MSCA-RISE-778234; OG, AT and SL); and the National Natural Science Foundation of China (Joint Research Project 81471100; JA, OG, AT, RP and SL).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Public Library of Science (PLoS)
dc.relation.ispartof PLOS Computational Biology. 2022;18(9):e1010412.
dc.relation.isreferencedby https://doi.org/10.1371/journal.pcbi.1010412.s001
dc.rights © 2022 Soler-Toscano et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Biologia
dc.subject.other Cartografia cerebral
dc.title What lies underneath: precise classification of brain states using time-dependent topological structure of dynamics
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1010412
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/896354
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/945539
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/602150
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/778234
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PGC2018-096540-B-I00
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-105772GB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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