Detection of sleep and wake substates through unsupervised machine learning in rat hippocampus and primary motor cortex LFP recordings

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dc.contributor.authorBalogun, Pauline
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorTatsuno, Masami
dc.date.accessioned2020-02-20T16:39:03Z
dc.date.available2020-02-20T16:39:03Z
dc.date.issued2019
dc.degree.levelMastersen_US
dc.description.abstractThis project investigated whether unsupervised machine learning could detect differences in global or local microstates across different rodent brain, in the effects of procedural learning, and clustering validity indices effectiveness. Previously obtained local field potential recordings of M1 and the hippocampus of freely-behaving male rats under naïve and task conditions, including transcranial direct and alternating current stimulation (tDCS; tACS), were analyzed and used to assess several methods. Two local SWS-like REM microstates were detected along with five global microstates. Learning suppressed cortical SWS-like REM microstates, but tDCS negated this effect. Calinski-Harabasz evaluated clusters had the highest sensitivity, specificity and total accuracy. Local and global brain states were effectively detected using PCA and clustering, and measures of phase-amplitude coupling were sensitive to the task conditions. These changes could underlie consolidation windows for procedural learning with potential intervention by tDCS although these results are limited to due the quality of the dataset.en_US
dc.identifier.urihttps://hdl.handle.net/10133/5685
dc.language.isoen_USen_US
dc.proquest.subject0317en_US
dc.proquest.subject0800en_US
dc.proquest.subject0463en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : Universtiy of Lethbridge, Department of Neuroscienceen_US
dc.publisher.departmentDepartment of Neuroscienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectneuroscienceen_US
dc.subjectk-meansen_US
dc.subjectmachine learningen_US
dc.subjecthippocampusen_US
dc.subjectmemoryen_US
dc.subjectprimary motor cortexen_US
dc.subjectclusteringen_US
dc.subjectcluster validity indexen_US
dc.subjectPCAen_US
dc.subjectbehaviouren_US
dc.subjecttACSen_US
dc.subjecttDCSen_US
dc.subjecttPACen_US
dc.subjectHippocampus (Brain) -- Researchen_US
dc.subjectMemory -- Researchen_US
dc.subjectMotor cortex -- Researchen_US
dc.subjectSleep -- Researchen_US
dc.subjectSleep-wake cycle -- Researchen_US
dc.subjectRats as laboratory animalsen_US
dc.subjectCluster analysis -- Computer programsen_US
dc.subjectDissertations, Academicen_US
dc.titleDetection of sleep and wake substates through unsupervised machine learning in rat hippocampus and primary motor cortex LFP recordingsen_US
dc.typeThesisen_US
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