Connectivity dynamics from wakefulness to sleep
- Autores
- Damaraju, Eswar; Tagliazucchi, Enzo Rodolfo; Laufs, Helmut; Calhoun, Vince D.
- Año de publicación
- 2020
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.
Fil: Damaraju, Eswar. Instituto Tecnológico de Georgia; Estados Unidos
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
Fil: Laufs, Helmut. Goethe Universitat Frankfurt; Alemania
Fil: Calhoun, Vince D.. Instituto Tecnológico de Georgia; Estados Unidos - Materia
-
Neuroimaging
Sleep
Dynamics
Consciousness - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/146095
Ver los metadatos del registro completo
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Connectivity dynamics from wakefulness to sleepDamaraju, EswarTagliazucchi, Enzo RodolfoLaufs, HelmutCalhoun, Vince D.NeuroimagingSleepDynamicsConsciousnesshttps://purl.org/becyt/ford/1.7https://purl.org/becyt/ford/1Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction.Fil: Damaraju, Eswar. Instituto Tecnológico de Georgia; Estados UnidosFil: 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; ArgentinaFil: Laufs, Helmut. Goethe Universitat Frankfurt; AlemaniaFil: Calhoun, Vince D.. Instituto Tecnológico de Georgia; Estados UnidosElsevier2020-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/146095Damaraju, Eswar; Tagliazucchi, Enzo Rodolfo; Laufs, Helmut; Calhoun, Vince D.; Connectivity dynamics from wakefulness to sleep; Elsevier; Neuroimage; 220; 10-2020; 1-201053-8119CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S1053811920305334info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2020.117047info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:32:23Zoai:ri.conicet.gov.ar:11336/146095instacron: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 09:32:23.702CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Connectivity dynamics from wakefulness to sleep |
title |
Connectivity dynamics from wakefulness to sleep |
spellingShingle |
Connectivity dynamics from wakefulness to sleep Damaraju, Eswar Neuroimaging Sleep Dynamics Consciousness |
title_short |
Connectivity dynamics from wakefulness to sleep |
title_full |
Connectivity dynamics from wakefulness to sleep |
title_fullStr |
Connectivity dynamics from wakefulness to sleep |
title_full_unstemmed |
Connectivity dynamics from wakefulness to sleep |
title_sort |
Connectivity dynamics from wakefulness to sleep |
dc.creator.none.fl_str_mv |
Damaraju, Eswar Tagliazucchi, Enzo Rodolfo Laufs, Helmut Calhoun, Vince D. |
author |
Damaraju, Eswar |
author_facet |
Damaraju, Eswar Tagliazucchi, Enzo Rodolfo Laufs, Helmut Calhoun, Vince D. |
author_role |
author |
author2 |
Tagliazucchi, Enzo Rodolfo Laufs, Helmut Calhoun, Vince D. |
author2_role |
author author author |
dc.subject.none.fl_str_mv |
Neuroimaging Sleep Dynamics Consciousness |
topic |
Neuroimaging Sleep Dynamics Consciousness |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.7 https://purl.org/becyt/ford/1 |
dc.description.none.fl_txt_mv |
Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction. Fil: Damaraju, Eswar. Instituto Tecnológico de Georgia; Estados Unidos 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 Fil: Laufs, Helmut. Goethe Universitat Frankfurt; Alemania Fil: Calhoun, Vince D.. Instituto Tecnológico de Georgia; Estados Unidos |
description |
Interest in time-resolved connectivity in fMRI has grown rapidly in recent years. The most widely used technique for studying connectivity changes over time utilizes a sliding windows approach. There has been some debate about the utility of shorter versus longer windows, the use of fixed versus adaptive windows, as well as whether observed resting state dynamics during wakefulness may be predominantly due to changes in sleep state and subject head motion. In this work we use an independent component analysis (ICA)-based pipeline applied to concurrent EEG/fMRI data collected during wakefulness and various sleep stages and show: 1) connectivity states obtained from clustering sliding windowed correlations of resting state functional network time courses well classify the sleep states obtained from EEG data, 2) using shorter sliding windows instead of longer non-overlapping windows improves the ability to capture transition dynamics even at windows as short as 30 s, 3) motion appears to be mostly associated with one of the states rather than spread across all of them 4) a fixed tapered sliding window approach outperforms an adaptive dynamic conditional correlation approach, and 5) consistent with prior EEG/fMRI work, we identify evidence of multiple states within the wakeful condition which are able to be classified with high accuracy. Classification of wakeful only states suggest the presence of time-varying changes in connectivity in fMRI data beyond sleep state or motion. Results also inform about advantageous technical choices, and the identification of different clusters within wakefulness that are separable suggest further studies in this direction. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-10 |
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/11336/146095 Damaraju, Eswar; Tagliazucchi, Enzo Rodolfo; Laufs, Helmut; Calhoun, Vince D.; Connectivity dynamics from wakefulness to sleep; Elsevier; Neuroimage; 220; 10-2020; 1-20 1053-8119 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/146095 |
identifier_str_mv |
Damaraju, Eswar; Tagliazucchi, Enzo Rodolfo; Laufs, Helmut; Calhoun, Vince D.; Connectivity dynamics from wakefulness to sleep; Elsevier; Neuroimage; 220; 10-2020; 1-20 1053-8119 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S1053811920305334 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.neuroimage.2020.117047 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
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