Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.

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cam.issuedOnline2018-11-29
datacite.issupplementedby.urlhttps://doi.org/10.17863/CAM.33597
dc.contributor.authorComsa, Iulia M
dc.contributor.authorBekinschtein, Tristan A
dc.contributor.authorChennu, Srivas
dc.contributor.orcidBekinschtein, Tristan A [0000-0001-5501-8628]
dc.contributor.orcidChennu, Srivas [0000-0002-6840-2941]
dc.date.accessioned2018-12-20T00:32:09Z
dc.date.available2018-12-20T00:32:09Z
dc.date.issued2019-03
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.identifier.doi10.17863/CAM.34578
dc.identifier.eissn1573-6792
dc.identifier.issn0896-0267
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/287271
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.publisher.urlhttp://dx.doi.org/10.1007/s10548-018-0689-9
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBrain connectivity
dc.subjectDrowsiness
dc.subjectEEG microstates
dc.subjectResponsiveness
dc.subjectAcoustic Stimulation
dc.subjectAdult
dc.subjectAlpha Rhythm
dc.subjectBehavior
dc.subjectBrain Mapping
dc.subjectData Interpretation, Statistical
dc.subjectElectroencephalography
dc.subjectFemale
dc.subjectHumans
dc.subjectMachine Learning
dc.subjectMale
dc.subjectNeural Pathways
dc.subjectPsychomotor Performance
dc.subjectReaction Time
dc.subjectSleepiness
dc.subjectTheta Rhythm
dc.subjectYoung Adult
dc.titleTransient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.
dc.typeArticle
dcterms.dateAccepted2018-11-22
prism.endingPage331
prism.issueIdentifier2
prism.publicationDate2019
prism.publicationNameBrain Topogr
prism.startingPage315
prism.volume32
rioxxterms.licenseref.startdate2019-03
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.typeJournal Article/Review
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1007/s10548-018-0689-9
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