Year: 2024
Type: dissertation
Abstract: Fast scan cyclic voltammetry (FSCV) on carbon fiber microelectrodes (CFMEs) allows sub-second monitoring of neurotransmitter fluctuations. In FSCV, the ‘background current’ is operationally defined as signal that does not correlate with neurotransmission. The background current differs in lesser-known ways between different electrodes in different recording environments. Principal component analysis (PCA) and elastic-net regularized generalized linear and logistic regression (GLMNET) were used to create prediction models capable of characterizing select identifiers of the background current environment.FSCV data on CFMEs was collected from several rodent brain regions using either Silver/Silver Chloride (Ag/AgCl) or stainless-steel (SS) reference electrodes. CFMEs with a carbon fiber length of 60 to 190 μm were placed in flow chambers in one of five conditions: phosphate-buffered solution (PBS) flowing only, artificial cerebrospinal fluid (ACSF) flowing only, caudate with ACSF, cortex with ACSF, or corpus callosum with ACSF. CFMEs were subject to a 10-minute pre-cycle followed by a 30-minute main cycle protocol.\nAverage background current and current drift were observed using the raw voltammetry data. GLMNET regression models proved effective at predicting condition and reference class for testing data from electrodes used in training the model. Future work is needed to improve prediction methods on electrodes not used to train GLMNET models.
Author: Fontana, Ross Joseph
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7045917015