Item Details

title

DETECTION OF OXYTOCIN AND VASOPRESSIN USING FAST-SCAN CYCLIC VOLTAMMETRY AND MACHINE LEARNING METHODS

author

Spry, Kasey Potts

abstract

Oxytocin and vasopressin are peptides that have historically been implicated in peripheral systems but recently their functions and dynamics in the central nervous system have begun to be investigated. Oxytocin and vasopressin are believed to be involved in social cognition and social cognitive behaviors, but their mechanisms, neural underpinnings, and functions have not been fully understood thus far. One reason for this deficiency of understanding may be a lack of tools and analysis to properly detect and monitor oxytocin and vasopressin in the brain. This study attempted to detect and monitor oxytocin and vasopressin using fast-scan cyclic voltammetry with carbon fiber and tungsten electrodes and machine learning techniques. Oxytocin and vasopressin were detected and characterized using carbon fiber electrodes and within electrode models. Further effort is needed to optimize characterization of carbon fiber between electrode models, tungsten within electrode models, and tungsten between electrode models. This study sought to provide a mechanism to better understand oxytocin and vasopressin’s dynamics and functions in the brain with the hope that this knowledge will lead to a better understanding and potential treatments of neuropsychiatric disorders.

subject

Computational Modeling

Fast-scan cyclic voltammetry (FSCV)

Machine Learning

Oxytocin

Vasopressin

contributor

Kishida, Kenneth T (committee chair)

Martin, Thomas J (committee member)

date

2022-07-11T19:17:44Z (accessioned)

2022-07-11T19:17:44Z (available)

2022 (issued)

degree

Neuroscience (discipline)

identifier

http://hdl.handle.net/10339/101027 (uri)

language

en (iso)

publisher

Wake Forest University

type

Thesis