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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/147697
Ver los metadatos del registro completo
id |
CONICETDig_f40dbeccae155e01b1d10eb2b4076d98 |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/147697 |
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 Técnicas2025-09-29T09:59:49Zoai: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:34982025-09-29 09:59:50.28CONICET 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 |
_version_ |
1844613772779454464 |
score |
13.070432 |