Improving Signal Identification for Fast-Scan Cyclic Voltammetry Public Deposited

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  • March 20, 2019
Creator
  • Johnson, Justin
    • Affiliation: College of Arts and Sciences, Department of Chemistry
Abstract
  • Fast-scan cyclic voltammetry (FSCV) is a powerful analytical tool for monitoring the in vivo concentration dynamics of electroactive neurotransmitters. Coupled with the use of microelectrodes, the approach allows for unsurpassed spatiotemporal resolution and is readily amendable to studies in freely moving animals. However, since its inception, the issue of selectivity has been of central concern. Correct identification and isolation of neurotransmitters signals is critical to interpretation of FSCV data, and much of the progress in the field has focused on methods of improving the ability to do this more robustly. Here, this work is expanded upon to both improve the use of existing methods (i.e. principal component analysis-inverse least squares regression, or PCA-ILS) and introduce new tools (i.e. multivariate curve resolution, or MCR-ALS, and convolution-based removal of non-faradaic currents) for this purpose. Chapter 1 presents the historical context of this work, highlighting the methods that have been successfully developed and employed for isolating catecholamine signals. In Chapter 2, the evaluation of the pitfalls of common methods of model training (i.e. the use of non-experimental training data) for PCA-ILS is discussed, with focus on elucidating the source of errors that arise from this approach. To help avoid these pitfalls, MCR-ALS, a method that does not require independent training data, characterized for use with FSCV data as a possible alternative. Next, Chapters 4 and 5 focus on the introduction, and exploration of the possibilities afforded by, the use of convolution to predict and remove non-faradaic currents from FSCV data. Chapter 4 specifically focuses on optimization of this method and the removal of ionic interferences from background-subtracted data collected with standard waveforms. Chapter 5 builds on this work to explore possible modifications to the experimental protocol (i.e. the use of high scan rates and higher waveform holding potentials) that tailor to this convolution-based procedure, which allows for the removal of the majority of the background current. The potential of this latter approach for simultaneous monitoring of information about phasic and basal levels of dopamine is then evaluated.
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Rights statement
  • In Copyright
Advisor
  • Carelli, Regina
  • Jorgenson, James
  • Manis, Paul B.
  • Wightman, R. Mark
  • Lockett, Matthew
Degree
  • Doctor of Philosophy
Degree granting institution
  • University of North Carolina at Chapel Hill Graduate School
Graduation year
  • 2017
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