Towards a novel lesioning method to causally study neuronal population dynamics underlying motor control
- Systems neuroscience currently lacks both a mechanistic understanding of how neuronal activity drives behavior and high-spatiotemporal-resolution insight into how the brain responds when neurons are lost due to disease or injury. While there is some knowledge of the correlation between loss of neurons and changes in functional behavior, to date, there is a limited understanding of the changes in neuronal activity in response to loss of neurons and subsequent recovery, which also limits the ability to develop a causal explanation of the relationship between neuronal activity and behavior. This dissertation takes a step towards addressing these outstanding questions by introducing a novel causal investigation method, neuroelectrophysiology-compatible electrolytic lesioning, which allows for both direct inactivation of neuronal activity and the collection of electrophysiology on physiologically relevant timescales for adaptation and recovery. This method was used successfully to create several lesions in two awake-behaving animals, removing neurons from cortex to affect both neuronal activity and behavioral performance on a skilled reaching task. These minor, heterogeneous behavioral effects last for days to weeks before the animal recovers back to baseline behavioral performance. In order to understand the changes taking place in the neuronal activity after lesioning and also as the animal behaviorally recovers, this dissertation explores analyses at two levels of supervision. A supervised offline decoding analysis uses changes in decoding performance to uncover aspects of neuronal population activity that remain or are changed after lesioning. An unsupervised analysis develops two information theoretic metrics that can be calculated on high-dimensional data -- one measure of complexity and one of efficiency at the action potential level -- and evaluates their effectiveness as biomarkers of both loss of neurons and behavioral deficit. Ultimately, results from these studies may be applied to develop assistive brain-computer interfaces that are robust to neuronal loss or to advance rehabilitation methods for diseases that lead to neuronal loss, including stroke and Alzheimer's disease.
|Type of resource
|electronic resource; remote; computer; online resource
|1 online resource.
|Bray, Iliana Erteza
|Degree committee member
|Degree committee member
|Stanford University, School of Engineering
|Stanford University, Department of Electrical Engineering
|Statement of responsibility
|Iliana Erteza Bray.
|Submitted to the Department of Electrical Engineering.
|Thesis Ph.D. Stanford University 2023.
- © 2023 by Iliana Erteza Bray
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