Novel regulators of learning and plasticity in motor system circuits
Abstract/Contents
- Abstract
- Motor learning is the process by which animals integrate sensorimotor information from the world in order to update future motor actions and improve desired outcomes. Neuroscience research in the past decades has identified brain structures and neuronal circuits involved in this process, and revealed that motor learning is a highly distributed process that involves precise spatiotemporal coordination and refinement throughout the corticobasal ganglia network. Reflecting this widespread involvement of brain circuitry, experimental approaches to study motor learning encompass a wide array of scope and techniques including in vivo electrical recordings or imaging, synaptic-level electrophysiology, genetic pathway analyses, and more. The work presented in this dissertation focuses on understanding two key activity-dependent mechanisms by which the synapses of circuits involved in motor control and motor learning change. Chapter 1 provides an overview of the motor learning field, with particular focus on the literature surrounding functional and structural synaptic plasticity of the synapses that I primarily focused on for my PhD work: (a) the synapses on neurons in primary motor cortex and (b) their projections into the striatum. In Chapter 2, I show that the stability of newly formed dendritic spines in motor cortex is the greatest predictor of motor learning, and that artificially increasing their stability in wildtype mice is sufficient to enhance the acquisition of motor skills. To do this, I studied PirB-/- mice and used chronic in vivo two-photon imaging of dendritic spine dynamics (in M1) while training mice on a reaching task. I showed that pharmacologically increasing the stability of newly formed spines in M1 (by selectively blocking PirB function genetically or with a decoy receptor) during training is sufficient to improve their learning of this task. In Chapter 3, I show that mice lacking all 3 isoforms of Synuclein (Syn-tKOs) exhibit an abolishment of endocannabinoid (eCB) plasticity in the striatum. Combining electrophysiological recordings with pharmacology and viral strategies, I dissected this synaptic phenotype and found that synucleins are required postsynaptically for eCB release, where activity-dependent membrane interaction of synucleins (likely with SNAREs) is needed for retrograde eCB signaling. In Chapter 4, I touch on conclusions and future directions based on this work. I place my findings in the context of the larger fields of motor learning and synaptic plasticity, and end with implications for promising translational therapeutic science.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Albarran, Eddy |
|---|---|
| Degree supervisor | Ding, Jun (Jun B.) |
| Degree supervisor | Shatz, Carla J |
| Thesis advisor | Ding, Jun (Jun B.) |
| Thesis advisor | Shatz, Carla J |
| Thesis advisor | Buckwalter, Marion |
| Thesis advisor | Luo, Liqun |
| Thesis advisor | Malenka, Robert |
| Degree committee member | Buckwalter, Marion |
| Degree committee member | Luo, Liqun |
| Degree committee member | Malenka, Robert |
| Associated with | Stanford University, Neurosciences Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Eddy Albarran. |
|---|---|
| Note | Submitted to the Neurosciences Program. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/cc194cm5449 |
Access conditions
- Copyright
- © 2021 by Eddy Albarran
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...