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dc.contributor.authorComsa, Iulia-Maria
dc.date.accessioned2019-03-12T09:46:02Z
dc.date.available2019-03-12T09:46:02Z
dc.date.issued2019-05-18
dc.date.submitted2018-04-26
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/290496
dc.description.abstractHow do we lose and regain consciousness? The space between healthy wakefulness and unconsciousness encompasses a series of gradual and rapid changes in brain activity. In this thesis, I investigate computational measures applicable to the electroencephalogram to quantify the loss and recovery of consciousness from the perspective of modern theoretical frameworks. I examine three different transitions of consciousness caused by natural, pharmacological and pathological factors: sleep, sedation and coma. First, I investigate the neural dynamics of falling asleep. By combining the established methods of phase-lag brain connectivity and EEG microstates in a group of healthy subjects, a unique microstate is identified, whose increased duration predicts behavioural unresponsiveness to auditory stimuli during drowsiness. This microstate also uniquely captures an increase in frontoparietal theta connectivity, a putative marker of the loss of consciousness prior to sleep onset. I next examine the loss of behavioural responsiveness in healthy subjects undergoing mild and moderate sedation. The Lempel-Ziv compression algorithm is employed to compute signal complexity and symbolic mutual information to assess information integration. An intriguing dissociation between responsiveness and drug level in blood during sedation is revealed: responsiveness is best predicted by the temporal complexity of the signal at single- channel and low-frequency integration, whereas drug level is best predicted by the complexity of spatial patterns and high-frequency integration. Finally, I investigate brain connectivity in the overnight EEG recordings of a group of patients in acute coma. Graph theory is applied on alpha, theta and delta networks to find that increased variability in delta network integration early after injury predicts the eventual coma recovery score. A case study is also described where the re-emergence of frontoparietal connectivity predicted a full recovery long before behavioural improvement. The findings of this thesis inform prospective clinical applications for tracking states of consciousness and advance our understanding of the slow and fast brain dynamics underlying its transitions. Collating these findings under a common theoretical framework, I argue that the diversity of dynamical states, in particular in temporal domain, and information integration across brain networks are fundamental in sustaining consciousness.
dc.description.sponsorshipMy PhD was funded by the Cambridge Trust and a MariaMarina award from Lucy Cavendish College.
dc.language.isoen
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectconsciousness
dc.subjectneuroscience of consciousness
dc.subjectstates of consciousness
dc.subjectlevels of consciousness
dc.subjectimpaired consciousness
dc.subjectonset of sleep
dc.subjectsedation
dc.subjectcoma
dc.subjectEEG
dc.subjectEEG microstates
dc.subjectbrain connectivity
dc.subjectfrontoparietal connectivity
dc.subjecttemporal brain dynamics
dc.subjectgraph theory
dc.subjectLempel-Ziv complexity
dc.subjectneural complexity
dc.subjectneural integration
dc.subjectbrain networks
dc.subjectspectral power
dc.subjectspectral connectivity
dc.subjectweighted phase lag index
dc.titleTracking brain dynamics across transitions of consciousness
dc.typeThesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridge
dc.publisher.departmentDepartment of Clinical Neurosciences
dc.date.updated2019-03-11T00:38:41Z
dc.identifier.doi10.17863/CAM.37723
dc.contributor.orcidComsa, Iulia-Maria [0000-0002-1322-4164]
dc.publisher.collegeLucy Cavendish
dc.type.qualificationtitlePhD in Clinical Neurosciences
cam.supervisorChennu, Srivas
cam.supervisorBekinschtein, Tristan Andres
cam.supervisor.orcidChennu, Srivas [0000-0002-6840-2941]
cam.supervisor.orcidBekinschtein, Tristan Andres [0000-0001-5501-8628]
cam.thesis.fundingfalse


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