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Employing Intracranial EEG Data to Decipher Sleep Neural Dynamics

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dc.contributor.authorKvavilashvili, Andrew Tomazen
dc.contributor.committeechairVijayan, Sujithen
dc.contributor.committeememberEnglish, Daniel F.en
dc.contributor.committeememberWilliams, Della C.en
dc.contributor.committeememberDiana, Rachel A.en
dc.contributor.departmentTranslational Biology, Medicine, and Healthen
dc.date.accessioned2023-01-25T09:00:31Zen
dc.date.available2023-01-25T09:00:31Zen
dc.date.issued2023-01-24en
dc.description.abstractOver the course of a typical night, sleep is comprised of multiple different stages that involve changes in brainwave patterns. Intracranial EEG (iEEG) is an invasive brain recording technique used in hospital settings in epileptic patients to determine the focus of their seizure activity. The intracranial data recorded allows one to directly observe the neural activity of deep brain structures such as the hippocampus and to detect single unit activity and local field potentials, thus providing a level of physiological detail normally available only in animal studies. In this thesis we employ intracranial data to advance our understanding of sleep neural dynamics in humans, and to this end its focus is in two areas : (1) developing a way of sleep scoring iEEG data and (2) investigating the neural dynamics of a particular waveform found during sleep, the sleep spindle, and its role in memory consolidation. Typically, iEEG recordings do not include electrooculogram or electromyogram recordings, which are normally needed for sleep scoring—especially for scoring rapid-eye movement (REM) sleep. We identified differences in alpha power between wake and REM sleep to develop a methodological way to reliably differentiate between wake and REM sleep states. We also wanted to investigate the neural dynamics involved with a particular brainwave seen during sleep, the sleep spindle, which is thought to be important for sleep-mediated memory consolidation. Historically, sleep spindles were thought to occur synchronously across the cortex, but recent findings using iEEG have identified that sleep spindles can also be local. We utilized intracranial EEG to confirm previous findings that iEEG can identify local sleep spindles. In addition to identifying local sleep spindles, we aimed to investigate the potential role that sleep spindles have on learning and memory using standard targeted memory reactivation paradigms for iii both procedural and declarative memories. We found that local sleep spindles occurred at a specific time following auditory stimulation for both procedural and declarative memories. This work has opened up the use of iEEG recordings to investigations of REM sleep dynamics and laid the groundwork for examining the role of local sleep spindles in memory consolidation.en
dc.description.abstractgeneralDuring a night of sleep, our brain goes through different stages that exhibit changes in brainwave patterns. Intracranial EEG (iEEG) is an invasive brain recording technique used in hospital settings in epileptic patients to determine the focus of their seizure activity; this particular brain recording technique allows one to observe the brain activity of deep brain structures. By using iEEG data, we aimed to (1) develop a way of sleep scoring iEEG data and (2) investigate the neural dynamics of a particular waveform found during sleep, the sleep spindle, and its role in memory consolidation.  Electrooculograms (EOG) are used to record the electrical activity of eye movements, and electromyograms (EMG) are used to measure muscle activity. Both of these recording techniques, in addition to EEG, are needed for sleep scoring, especially rapid eye movement (REM) sleep. However, typical iEEG recordings do not have EOGs and EMGs applied to the patient. Using iEEG data, we were able to identify differences in a specific brainwave, the alpha rhythm, between wakeful brain activity and REM sleep brain activity. Furthermore, we were able to use this difference to reliably score REM sleep in iEEG data without the need for EOGs and EMGs.  We also wanted to investigate the brainwave changes in a particular waveform, the sleep spindle, that has been thought to be important for sleep-mediated memory consolidation. Previous research using typical EEG recordings showed that sleep spindles occur synchronously across the cortex, but recent findings using iEEG have identified that sleep spindles can also occur asynchronously across the cortex. We replicated previous research showing that these local sleep spindles are identifiable using iEEG recordings. In addition to identifying local sleep spindles, we investigated the potential role that sleep spindles have on learning and memory. To do so, we used standard targeted memory reactivation paradigms for two types of memory: declarative and procedural memory. We found that local sleep spindles occurred at a specific time following auditory stimulation for both procedural and declarative memories.  This work has opened up the use of iEEG recordings to investigations of REM sleep dynamics and laid the groundwork for examining the role of local sleep spindles in memory consolidation.  en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:36404en
dc.identifier.urihttp://hdl.handle.net/10919/113413en
dc.language.isoenen
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSleepen
dc.subjectiEEGen
dc.subjectAlpha Waveen
dc.subjectSleep Spindleen
dc.subjectREM Sleepen
dc.titleEmploying Intracranial EEG Data to Decipher Sleep Neural Dynamicsen
dc.typeThesisen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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