Optimization of in vivo electrochemical detection of neurotransmitters and application to neurological pathologies
Year: 2025
Type: article
Abstract: Depression is one of the leading causes of disability worldwide. Diagnosis and treatment are difficult due to a lack of understanding of the underlying causes and mechanisms of the disorder. Several theories have been proposed over the years, including the monoamine, neuroplasticity, and inflammation theories of depression. The neurotransmitters serotonin and histamine are of particular interest in depression research. Techniques that can accurately capture neurotransmitter dynamics in the brain are constantly being developed and refined, such as fast scan cyclic voltammetry (FSCV) and its offshoot fast scan controlled adsorption voltammetry (FSCAV). The objective of this dissertation is to apply innovative approaches to improve FSCV and FSCAV measurements for histamine and serotonin. To begin this thesis, I introduce neurotransmission, serotonin and histamine signaling, and techniques to measure neurotransmission. Next, I review the three theories of depression and emphasize essential analytical techniques used to study the disorder and showcase how inflammation affects the neurotransmitters dopamine, serotonin, and norepinephrine in the brain. I then detail the general methods used in this research. I then describe an innovative electrode surface modification using amino acids to enhance extracellular serotonin measurements with FSCAV. Subsequently, I introduce the development of FSCAV for histamine detection and describe how anesthetics and an H3R targeting drug alter extracellular histamine concentrations. Additionally, I describe a new approach to replicate in vivo conditions in vitro. I conclude by summarizing the work done in this thesis and my perspectives on future work.
DOI: 10.25560/122092
Author: Batey, Lauren
Institution: Imperial College London
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7083319119
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