The structure and function of neural circuits supporting incentivized inhibition
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Description
Creators/Contributors
- Author/Creator
- Leong, Josiah King Yun, author.
- Contributor
- Knutson, Brian degree supervisor.
- Gotlib, Ian H., degree committee member.
- Poldrack, Russell A., degree committee member.
- Stanford University. Department of Psychology.
Contents/Summary
- Summary
- What humans choose and how their brains make choices are central questions in psychology and neuroscience. Neuroimaging research is advancing models of functional brain activity that can not only correlate with, but can also predict, value-based choices in humans. Physiological research in animals suggests that monosynaptic axons structurally connect the brain circuits predictive of choice. However, similar structural connections have only recently been characterized in humans, and researchers are now starting to explore links from structural brain connections to functional brain activity to value-based choices. This dissertation first reviews recent attempts to link structural white-matter tracts to individual differences in value-based choices. Next, two peer-reviewed articles present studies that characterize novel white-matter tracts and link the structural coherence of the tracts to functional activity at tract endpoints to value-based behaviors. A third study presents applications of the neuroimaging tools in a clinical context to characterize brain structural deficits in patients who used stimulant drugs, and further to predict patient relapse after intervention. The dissertation concludes with guidelines for researchers interested in linking multimodal neuroimaging measurements to individual differences in value-based choice. Together this work demonstrates the promise of combining measures of brain structure, brain function, and behavior to better explain how the brain chooses.
Bibliographic information
- Publication date
- 2019
- Copyright date
- 2019
- Note
- Submitted to the Department of Psychology.
- Note
- Thesis Ph.D. Stanford University 2019.
