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dc.contributor.advisorOjemann, Jeffrey G
dc.contributor.authorCasimo, Kaitlyn
dc.date.accessioned2018-07-31T21:09:18Z
dc.date.submitted2018
dc.identifier.otherCasimo_washington_0250E_18670.pdf
dc.identifier.urihttp://hdl.handle.net/1773/42196
dc.descriptionThesis (Ph.D.)--University of Washington, 2018
dc.description.abstractResting state brain connectivity is thought to represent ongoing cognitive processes, including memory consolidation, that occur outside of the context of a specific task. While the existence of synchronous brain activity at rest is well documented, its specific properties have not been thoroughly investigated with electrocorticography, which is notable for its high resolution in fast frequencies thought to represent local neuronal firing rates, the fixed placement of electrodes that do not shift over the course of days, and a variety of other properties that support unique insights into brain function relative to other electrophysiological methods in humans. Our understanding of resting state connectivity depends on thoroughly characterizing its properties, such as over the course of minutes, between multiple sessions, and after a demanding behavioral task. The goal of my work is to use electrocorticography to evaluate various features of resting state connectivity, including multiple connectivity methods to capture different features of the signals over a wide range of frequency bands. I identified regionally specific patterns of phase synchrony over the course in a single session of the resting state, including lower frequencies dominating the patterns in frontal regions and higher frequencies dominating in parietal regions, and a frontal-to-parietal directional flow of synchrony. I examined how patterns of connectivity vary between sessions of the resting state, and found that strong connectivity in phase locking and amplitude correlation predicts high stability across the sessions, but high stability is not exclusively associated with strong connectivity. I also found that within-region connectivity was more stable than connectivity between different regions, and individual regions expressed different relationships between connectivity and stability. Finally, I found that changes in connectivity patterns of inferior parietal lobule, and to a lesser extent primary motor cortex, following the execution of a novel task especially differentiated individuals who successfully performed the task from those who did not. Surprisingly, I did not find any pre-task connectivity patterns that were predictive of task performance, in any frequency band or brain region, with any of the connectivity measures I tested. My findings contribute to a deeper understanding of the specific properties of resting state connectivity, the way it spontaneously varies over time, and the changes that arise specifically from learning a new task. The methods I used to evaluate connectivity and change over time can be applied with ease to a wide range of other conditions or to data from additional brain regions in order to develop a wide and robust profile of resting state connectivity features. Understanding the varying properties of the resting state over space and time will enable us to better understand the functions of the resting state specifically, and the brain independent of task execution more broadly.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsnone
dc.subjectbehavioral neuroscience
dc.subjectbrain connectivity
dc.subjectcomputational neuroscience
dc.subjectelectrophysiology
dc.subjecthuman neuroscience
dc.subjectNeurosciences
dc.subject.otherBehavioral neuroscience
dc.titleSpontaneous and task-related changes in resting state connectivity
dc.typeThesis
dc.embargo.termsRestrict to UW for 1 year -- then make Open Access
dc.embargo.lift2019-07-31T21:09:18Z


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