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Understanding the role of striosomes in learning a decision-making task

Author(s)
Drammis, Sabrina Mariel.
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Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Ann Graybiel.
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
Learning to approach for rewards and avoid for costs is a necessary skill for species survival. In this thesis, I investigate the role of the striosomal compartment of the rodent basal ganglia while learning a cost-benefit decision making task. I analyze data from animals of different age groups and animals with expressive Huntington's disease (HD) gene. My goal is to understand the computational role of striosomes in learning, and how aging and Huntington's disorder affect the anatomical structure of striosomes and thus the ability to learn. By analyzing Ca⁺⁺ (Calcium) neuron population recordings, behavior recordings, and histological brain slice images of mice, I find that: 1) the activity of spiny projection neurons (SPNs) in striosomes correlate with learning ability; 2) an animal's attentiveness to the task is critical for learning; 3) anatomical circuit disassembly, specifically reduced connectivity of inhibitory Parvalbumin (PV) interneurons on SPNs, is found in animal groups with lower learning ability. These results help elucidate the unclear role of striosomes in learning and provide ideas for future research directions that could inspire neurological treatments for neuro-degradation in aging and disorder.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
 
Cataloged from student-submitted PDF of thesis.
 
Includes bibliographical references (pages 74-77).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/129873
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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