Systems neuroscience and neural engineering have relied mainly on the task-based experimental paradigm to understand brain activity. While this method has proved fruitful, it fails to capture the full variability of unstructured and naturalistic neural activity. In this thesis, we explore the value of investigating multiday unstructured intracranial electrophysiology recordings in humans. Using stereotactic-electroencephalography (sEEG) and electrocorticography (ECoG) electrodes, we analyze days of neural recordings to investigate how internal and external states, such as autonomic tone and behavior, correlate to neural activity. Firstly, we determine whether coarsely labeled unstructured behavioral contexts or states are discriminable in the neural activity space. Subjects were not instructed to perform any task; therefore, only spontaneous behaviors were analyzed. Controls to determine whether temporal correlations and time of day effects impact the separability of behavioral states were investigated, concluding that both the time of day and behavior have a combined effect on neural activity. Secondly, once we determined that these behavioral states are separable, the cause of this separability was further investigated. In other words, what neural signal characteristics are responsible for our ability to decode abstract behavioral states? Both long term signal characteristics and spatiotemporal dynamics contribute information regarding naturalistic behavior, showing that outside the lab, neural activity has multiple axes of variability that could be used to discriminate between different states. In the final section of this work, we investigate the neural correlates to autonomic tone during sleep, leveraging multiple days of unstructured neural activity to make physiological conclusions regarding the connection between the central and autonomic nervous systems.
|Commitee:||Halgren, Eric, Kreutz-Delgado, Kenneth, Pal, Piya, Rao, Bhaskar|
|School:||University of California, San Diego|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- California|
|Source:||DAI-B 82/7(E), Dissertation Abstracts International|
|Subjects:||Electrical engineering, Neurosciences|
|Keywords:||Brain computer interfaces, Computational Neuroscience, Digital signal processing, Machine learning, Neural engineering, Systems neuroscience|
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