Predictability of arousal in mouse slow wave sleep by accelerometer data
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/jspui/handle/123456789/25433 https://doi.org/10.1371/journal.pone.0176761 |
Resumo: | Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep. |
id |
UFRN_c1837224d5820eb4101e19d486eb517d |
---|---|
oai_identifier_str |
oai:https://repositorio.ufrn.br:123456789/25433 |
network_acronym_str |
UFRN |
network_name_str |
Repositório Institucional da UFRN |
repository_id_str |
|
spelling |
Soares, Bruno LobãoLima, Gustavo Zampier dos SantosLopes, Sergio RobertoPrado, Thiago LimaNascimento, George Carlos doFontenele-Araujo, JohnCorso, Gilberto2018-06-16T15:04:40Z2018-06-16T15:04:40Z2017-05-18SOARES, Bruno Lobão et al. Predictability of arousal in mouse slow wave sleep by accelerometer data. PLoS One, v. 12, p. e0176761, 2017. Disponível em: <http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176761>. Acesso em: 27 mar. 2018.1932-6203https://repositorio.ufrn.br/jspui/handle/123456789/25433https://doi.org/10.1371/journal.pone.0176761engImperial College London, UNITED KINGDOMwakefulness into sleepPredictability of arousal in mouse slow wave sleep by accelerometer datainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleArousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNTEXTPredictability of arousal in mouse slow_2017.pdf.txtPredictability of arousal in mouse slow_2017.pdf.txtExtracted texttext/plain57402https://repositorio.ufrn.br/bitstream/123456789/25433/3/Predictability%20of%20arousal%20in%20mouse%20slow_2017.pdf.txt78a2680cb9056ecd377ea52282ca4c7bMD53THUMBNAILPredictability of arousal in mouse slow_2017.pdf.jpgPredictability of arousal in mouse slow_2017.pdf.jpgIM Thumbnailimage/jpeg11176https://repositorio.ufrn.br/bitstream/123456789/25433/4/Predictability%20of%20arousal%20in%20mouse%20slow_2017.pdf.jpg516733f43542cdda698bf23d869fdbc6MD54TEXTPredictability of arousal in mouse slow_2017.pdf.txtPredictability of arousal in mouse slow_2017.pdf.txtExtracted texttext/plain57402https://repositorio.ufrn.br/bitstream/123456789/25433/3/Predictability%20of%20arousal%20in%20mouse%20slow_2017.pdf.txt78a2680cb9056ecd377ea52282ca4c7bMD53THUMBNAILPredictability of arousal in mouse slow_2017.pdf.jpgPredictability of arousal in mouse slow_2017.pdf.jpgIM Thumbnailimage/jpeg11176https://repositorio.ufrn.br/bitstream/123456789/25433/4/Predictability%20of%20arousal%20in%20mouse%20slow_2017.pdf.jpg516733f43542cdda698bf23d869fdbc6MD54ORIGINALPredictabilityArousalMouse_Soares_2017.pdfPredictabilityArousalMouse_Soares_2017.pdfapplication/pdf16822092https://repositorio.ufrn.br/bitstream/123456789/25433/1/PredictabilityArousalMouse_Soares_2017.pdf0f8f351492da4406260402f6f3904da1MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.ufrn.br/bitstream/123456789/25433/2/license.txt8a4605be74aa9ea9d79846c1fba20a33MD52123456789/254332021-11-09 17:32:30.493oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-11-09T20:32:30Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Predictability of arousal in mouse slow wave sleep by accelerometer data |
title |
Predictability of arousal in mouse slow wave sleep by accelerometer data |
spellingShingle |
Predictability of arousal in mouse slow wave sleep by accelerometer data Soares, Bruno Lobão wakefulness into sleep |
title_short |
Predictability of arousal in mouse slow wave sleep by accelerometer data |
title_full |
Predictability of arousal in mouse slow wave sleep by accelerometer data |
title_fullStr |
Predictability of arousal in mouse slow wave sleep by accelerometer data |
title_full_unstemmed |
Predictability of arousal in mouse slow wave sleep by accelerometer data |
title_sort |
Predictability of arousal in mouse slow wave sleep by accelerometer data |
author |
Soares, Bruno Lobão |
author_facet |
Soares, Bruno Lobão Lima, Gustavo Zampier dos Santos Lopes, Sergio Roberto Prado, Thiago Lima Nascimento, George Carlos do Fontenele-Araujo, John Corso, Gilberto |
author_role |
author |
author2 |
Lima, Gustavo Zampier dos Santos Lopes, Sergio Roberto Prado, Thiago Lima Nascimento, George Carlos do Fontenele-Araujo, John Corso, Gilberto |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Soares, Bruno Lobão Lima, Gustavo Zampier dos Santos Lopes, Sergio Roberto Prado, Thiago Lima Nascimento, George Carlos do Fontenele-Araujo, John Corso, Gilberto |
dc.subject.por.fl_str_mv |
wakefulness into sleep |
topic |
wakefulness into sleep |
description |
Arousals can be roughly characterized by punctual intrusions of wakefulness into sleep. In a standard perspective, using human electroencephalography (EEG) data, arousals are associated to slow-wave rhythms and K-complex brain activity. The physiological mechanisms that give rise to arousals during sleep are not yet fully understood. Moreover, subtle body movement patterns, which may characterize arousals both in human and in animals, are usually not detectable by eye perception and are not in general present in sleep studies. In this paper, we focus attention on accelerometer records (AR) to characterize and predict arousal during slow wave sleep (SWS) stage of mice. Furthermore, we recorded the local field potentials (LFP) from the CA1 region in the hippocampus and paired with accelerometer data. The hippocampus signal was also used here to identify the SWS stage. We analyzed the AR dynamics of consecutive arousals using recurrence technique and the determinism (DET) quantifier. Recurrence is a fundamental property of dynamical systems, which can be exploited to characterize time series properties. The DET index evaluates how similar are the evolution of close trajectories: in this sense, it computes how accurate are predictions based on past trajectories. For all analyzed mice in this work, we observed, for the first time, the occurrence of a universal dynamic pattern a few seconds that precedes the arousals during SWS sleep stage based only on the AR signal. The predictability success of an arousal using DET from AR is nearly 90%, while similar analysis using LFP of hippocampus brain region reveal 88% of success. Noteworthy, our findings suggest an unique dynamical behavior pattern preceding an arousal of AR data during sleep. Thus, the employment of this technique applied to AR data may provide useful information about the dynamics of neuronal activities that control sleep-waking switch during SWS sleep period. We argue that the predictability of arousals observed through DET(AR) can be functionally explained by a respiratory-driven modification of neural states. Finally, we believe that the method associating AR data with other physiologic events such as neural rhythms can become an accurate, convenient and non-invasive way of studying the physiology and physiopathology of movement and respiratory processes during sleep. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017-05-18 |
dc.date.accessioned.fl_str_mv |
2018-06-16T15:04:40Z |
dc.date.available.fl_str_mv |
2018-06-16T15:04:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
SOARES, Bruno Lobão et al. Predictability of arousal in mouse slow wave sleep by accelerometer data. PLoS One, v. 12, p. e0176761, 2017. Disponível em: <http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176761>. Acesso em: 27 mar. 2018. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/jspui/handle/123456789/25433 |
dc.identifier.issn.none.fl_str_mv |
1932-6203 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1371/journal.pone.0176761 |
identifier_str_mv |
SOARES, Bruno Lobão et al. Predictability of arousal in mouse slow wave sleep by accelerometer data. PLoS One, v. 12, p. e0176761, 2017. Disponível em: <http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0176761>. Acesso em: 27 mar. 2018. 1932-6203 |
url |
https://repositorio.ufrn.br/jspui/handle/123456789/25433 https://doi.org/10.1371/journal.pone.0176761 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Imperial College London, UNITED KINGDOM |
publisher.none.fl_str_mv |
Imperial College London, UNITED KINGDOM |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRN instname:Universidade Federal do Rio Grande do Norte (UFRN) instacron:UFRN |
instname_str |
Universidade Federal do Rio Grande do Norte (UFRN) |
instacron_str |
UFRN |
institution |
UFRN |
reponame_str |
Repositório Institucional da UFRN |
collection |
Repositório Institucional da UFRN |
bitstream.url.fl_str_mv |
https://repositorio.ufrn.br/bitstream/123456789/25433/3/Predictability%20of%20arousal%20in%20mouse%20slow_2017.pdf.txt https://repositorio.ufrn.br/bitstream/123456789/25433/4/Predictability%20of%20arousal%20in%20mouse%20slow_2017.pdf.jpg https://repositorio.ufrn.br/bitstream/123456789/25433/3/Predictability%20of%20arousal%20in%20mouse%20slow_2017.pdf.txt https://repositorio.ufrn.br/bitstream/123456789/25433/4/Predictability%20of%20arousal%20in%20mouse%20slow_2017.pdf.jpg https://repositorio.ufrn.br/bitstream/123456789/25433/1/PredictabilityArousalMouse_Soares_2017.pdf https://repositorio.ufrn.br/bitstream/123456789/25433/2/license.txt |
bitstream.checksum.fl_str_mv |
78a2680cb9056ecd377ea52282ca4c7b 516733f43542cdda698bf23d869fdbc6 78a2680cb9056ecd377ea52282ca4c7b 516733f43542cdda698bf23d869fdbc6 0f8f351492da4406260402f6f3904da1 8a4605be74aa9ea9d79846c1fba20a33 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN) |
repository.mail.fl_str_mv |
|
_version_ |
1797777009205575680 |