ingest cdrApp 2017-08-15T22:51:40.703Z d91e81c8-5a8a-4e8a-976c-cad4e396e5ee modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-08-15T22:52:24.393Z Setting exclusive relation modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-08-15T22:52:33.836Z Setting exclusive relation addDatastream MD_TECHNICAL fedoraAdmin 2017-08-15T22:52:43.042Z Adding technical metadata derived by FITS modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-08-15T22:53:01.528Z Setting exclusive relation addDatastream MD_FULL_TEXT fedoraAdmin 2017-08-15T22:53:11.293Z Adding full text metadata extracted by Apache Tika modifyDatastreamByValue RELS-EXT fedoraAdmin 2017-08-15T22:53:30.520Z Setting exclusive relation modifyDatastreamByValue RELS-EXT cdrApp 2017-08-22T13:57:17.721Z Setting exclusive relation modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-25T07:22:13.828Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-01-27T07:44:37.381Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-03-14T04:10:45.831Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-05-17T15:52:37.564Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-11T02:37:07.824Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-07-17T22:57:45.978Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-08-15T19:07:00.868Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-21T19:28:14.791Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-09-26T22:43:27.029Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2018-10-11T23:20:04.021Z modifyDatastreamByValue MD_DESCRIPTIVE cdrApp 2019-03-20T17:07:57.524Z Justin Johnson Author Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. Summer 2017 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul Manis Thesis advisor text Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. Summer 2017 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul Manis Thesis advisor text Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. Summer 2017 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul Manis Thesis advisor text Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. 2017-08 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul Manis Thesis advisor text Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul Manis Thesis advisor text 2017-08 Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul Manis Thesis advisor text 2017-08 Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul Manis Thesis advisor text 2017-08 Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation Chemistry R. Mark Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul B. Manis Thesis advisor text 2017-08 University of North Carolina at Chapel Hill Degree granting institution Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul Manis Thesis advisor text 2017-08 Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. 2017 Analytical chemistry Chemometrics; Fast-scan cyclic voltammetry; Microelectrodes; Neurotransmission; Signal isolation eng Doctor of Philosophy Dissertation Chemistry R. Mark Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul B. Manis Thesis advisor text 2017-08 University of North Carolina at Chapel Hill Degree granting institution Justin Johnson Creator Department of Chemistry College of Arts and Sciences Improving Signal Identification for Fast-Scan Cyclic Voltammetry 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. 2017 Analytical chemistry Chemometrics, Fast-scan cyclic voltammetry, Microelectrodes, Neurotransmission, Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution Chemistry R. Mark Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul B. Manis Thesis advisor text 2017-08 Justin Johnson Creator Department of Chemistry College of Arts and Sciences 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. 2017 Analytical chemistry Chemometrics; Fast-scan cyclic voltammetry; Microelectrodes; Neurotransmission; Signal isolation eng Doctor of Philosophy Dissertation University of North Carolina at Chapel Hill Graduate School Degree granting institution R. Mark Wightman Thesis advisor Matthew Lockett Thesis advisor James Jorgenson Thesis advisor Regina Carelli Thesis advisor Paul B. Manis Thesis advisor text 2017-08 Johnson_unc_0153D_17299.pdf uuid:546e07da-3b4b-4a31-b107-54cda6d12fb2 2017-07-25T20:45:06Z proquest 2019-08-15T00:00:00 application/pdf 6236800 yes