Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep

Autores
Stevner, A. B. A.; Vidaurre, D.; Cabral, J.; Rapuano, K.; Nielsen, S. F. V.; Tagliazucchi, Enzo Rodolfo; Laufs, H.; Vuust, P.; Deco, G.; Woolrich, M. W.; Van Someren, E.; Kringelbach, M. L.
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.
Fil: Stevner, A. B. A.. University of Oxford; Reino Unido. University Aarhus; Dinamarca
Fil: Vidaurre, D.. University of Oxford; Reino Unido
Fil: Cabral, J.. University of Oxford; Reino Unido. Universidade do Minho; Portugal
Fil: Rapuano, K.. Dartmouth College; Estados Unidos
Fil: Nielsen, S. F. V.. Technical University of Denmark; Dinamarca
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Netherlands Institute for Neuroscience; Países Bajos. University Hospital Schleswig Holstein; Alemania. Goethe Universitat Frankfurt; Alemania
Fil: Laufs, H.. University Hospital Schleswig Holstein; Alemania. Goethe Universitat Frankfurt; Alemania
Fil: Vuust, P.. University Aarhus; Dinamarca
Fil: Deco, G.. Universitat Pompeu Fabra; España. Institució Catalana de la Recerca i Estudis Avançats; España. Max Planck Institute for Human Cognitive and Brain Sciences; Alemania. Monash University; Australia
Fil: Woolrich, M. W.. University of Oxford; Reino Unido
Fil: Van Someren, E.. Vrije Universiteit Amsterdam; Países Bajos. Netherlands Institute for Neuroscience; Países Bajos
Fil: Kringelbach, M. L.. University of Oxford; Reino Unido. University Aarhus; Dinamarca. Universidade do Minho; Portugal
Materia
Consciousness
Neuroimaging
Dynamics
Sleep
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/147697
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleepStevner, A. B. A.Vidaurre, D.Cabral, J.Rapuano, K.Nielsen, S. F. V.Tagliazucchi, Enzo RodolfoLaufs, H.Vuust, P.Deco, G.Woolrich, M. W.Van Someren, E.Kringelbach, M. L.ConsciousnessNeuroimagingDynamicsSleephttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.Fil: Stevner, A. B. A.. University of Oxford; Reino Unido. University Aarhus; DinamarcaFil: Vidaurre, D.. University of Oxford; Reino UnidoFil: Cabral, J.. University of Oxford; Reino Unido. Universidade do Minho; PortugalFil: Rapuano, K.. Dartmouth College; Estados UnidosFil: Nielsen, S. F. V.. Technical University of Denmark; DinamarcaFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Netherlands Institute for Neuroscience; Países Bajos. University Hospital Schleswig Holstein; Alemania. Goethe Universitat Frankfurt; AlemaniaFil: Laufs, H.. University Hospital Schleswig Holstein; Alemania. Goethe Universitat Frankfurt; AlemaniaFil: Vuust, P.. University Aarhus; DinamarcaFil: Deco, G.. Universitat Pompeu Fabra; España. Institució Catalana de la Recerca i Estudis Avançats; España. Max Planck Institute for Human Cognitive and Brain Sciences; Alemania. Monash University; AustraliaFil: Woolrich, M. W.. University of Oxford; Reino UnidoFil: Van Someren, E.. Vrije Universiteit Amsterdam; Países Bajos. Netherlands Institute for Neuroscience; Países BajosFil: Kringelbach, M. L.. University of Oxford; Reino Unido. University Aarhus; Dinamarca. Universidade do Minho; PortugalNature Publishing Group2019-03-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/147697Stevner, A. B. A.; Vidaurre, D.; Cabral, J.; Rapuano, K.; Nielsen, S. F. V.; et al.; Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep; Nature Publishing Group; Nature Communications; 10; 1; 04-3-2019; 1-142041-1723CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41467-019-08934-3info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-019-08934-3info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:55:35Zoai:ri.conicet.gov.ar:11336/147697instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:55:35.483CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
title Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
spellingShingle Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
Stevner, A. B. A.
Consciousness
Neuroimaging
Dynamics
Sleep
title_short Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
title_full Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
title_fullStr Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
title_full_unstemmed Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
title_sort Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
dc.creator.none.fl_str_mv Stevner, A. B. A.
Vidaurre, D.
Cabral, J.
Rapuano, K.
Nielsen, S. F. V.
Tagliazucchi, Enzo Rodolfo
Laufs, H.
Vuust, P.
Deco, G.
Woolrich, M. W.
Van Someren, E.
Kringelbach, M. L.
author Stevner, A. B. A.
author_facet Stevner, A. B. A.
Vidaurre, D.
Cabral, J.
Rapuano, K.
Nielsen, S. F. V.
Tagliazucchi, Enzo Rodolfo
Laufs, H.
Vuust, P.
Deco, G.
Woolrich, M. W.
Van Someren, E.
Kringelbach, M. L.
author_role author
author2 Vidaurre, D.
Cabral, J.
Rapuano, K.
Nielsen, S. F. V.
Tagliazucchi, Enzo Rodolfo
Laufs, H.
Vuust, P.
Deco, G.
Woolrich, M. W.
Van Someren, E.
Kringelbach, M. L.
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Consciousness
Neuroimaging
Dynamics
Sleep
topic Consciousness
Neuroimaging
Dynamics
Sleep
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.
Fil: Stevner, A. B. A.. University of Oxford; Reino Unido. University Aarhus; Dinamarca
Fil: Vidaurre, D.. University of Oxford; Reino Unido
Fil: Cabral, J.. University of Oxford; Reino Unido. Universidade do Minho; Portugal
Fil: Rapuano, K.. Dartmouth College; Estados Unidos
Fil: Nielsen, S. F. V.. Technical University of Denmark; Dinamarca
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina. Netherlands Institute for Neuroscience; Países Bajos. University Hospital Schleswig Holstein; Alemania. Goethe Universitat Frankfurt; Alemania
Fil: Laufs, H.. University Hospital Schleswig Holstein; Alemania. Goethe Universitat Frankfurt; Alemania
Fil: Vuust, P.. University Aarhus; Dinamarca
Fil: Deco, G.. Universitat Pompeu Fabra; España. Institució Catalana de la Recerca i Estudis Avançats; España. Max Planck Institute for Human Cognitive and Brain Sciences; Alemania. Monash University; Australia
Fil: Woolrich, M. W.. University of Oxford; Reino Unido
Fil: Van Someren, E.. Vrije Universiteit Amsterdam; Países Bajos. Netherlands Institute for Neuroscience; Países Bajos
Fil: Kringelbach, M. L.. University of Oxford; Reino Unido. University Aarhus; Dinamarca. Universidade do Minho; Portugal
description The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting the heterogeneities of stage N1 sleep and wakefulness before and after sleep.
publishDate 2019
dc.date.none.fl_str_mv 2019-03-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/147697
Stevner, A. B. A.; Vidaurre, D.; Cabral, J.; Rapuano, K.; Nielsen, S. F. V.; et al.; Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep; Nature Publishing Group; Nature Communications; 10; 1; 04-3-2019; 1-14
2041-1723
CONICET Digital
CONICET
url http://hdl.handle.net/11336/147697
identifier_str_mv Stevner, A. B. A.; Vidaurre, D.; Cabral, J.; Rapuano, K.; Nielsen, S. F. V.; et al.; Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep; Nature Publishing Group; Nature Communications; 10; 1; 04-3-2019; 1-14
2041-1723
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.nature.com/articles/s41467-019-08934-3
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-019-08934-3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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