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dc.contributor.authorComsa, Iulia
dc.contributor.authorBekinschtein, Tristan
dc.contributor.authorChennu, Srivas
dc.date.accessioned2018-12-20T00:32:09Z
dc.date.available2018-12-20T00:32:09Z
dc.date.issued2019-03
dc.identifier.issn0896-0267
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/287271
dc.description.abstractAs we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of alpha networks and the emergence of theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectNeural Pathways
dc.subjectHumans
dc.subjectElectroencephalography
dc.subjectAlpha Rhythm
dc.subjectTheta Rhythm
dc.subjectAcoustic Stimulation
dc.subjectBrain Mapping
dc.subjectData Interpretation, Statistical
dc.subjectBehavior
dc.subjectPsychomotor Performance
dc.subjectReaction Time
dc.subjectAdult
dc.subjectFemale
dc.subjectMale
dc.subjectYoung Adult
dc.subjectMachine Learning
dc.subjectSleepiness
dc.titleTransient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.
dc.typeArticle
prism.endingPage331
prism.issueIdentifier2
prism.publicationDate2019
prism.publicationNameBrain Topogr
prism.startingPage315
prism.volume32
dc.identifier.doi10.17863/CAM.34578
dcterms.dateAccepted2018-11-22
rioxxterms.versionofrecord10.1007/s10548-018-0689-9
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-03
dc.contributor.orcidComsa, Iulia [0000-0002-1322-4164]
dc.contributor.orcidBekinschtein, Tristan [0000-0001-5501-8628]
dc.contributor.orcidChennu, Srivas [0000-0002-6840-2941]
dc.identifier.eissn1573-6792
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-11-29
datacite.issupplementedby.urlhttps://doi.org/10.17863/CAM.33597


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International