Novel Electrochemical Methods for Human Neurochemistry

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Date
2020-10-14
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Publisher
Virginia Tech
Abstract

Computational psychiatry describes psychological phenomena as abnormalities in biological computations. Current available technologies span multiple organizational and temporal domains, but there remains a knowledge gap with respect to neuromodulator dynamics in humans. Recent efforts by members of the Montague Laboratory and collaborators adapted fast scan cyclic voltammetry (FSCV) from rodent experiments for use in human patients already receiving brain surgery. The process of modifying established FSCV methods for clinical application has led improved model building strategies, and a new "random burst" sensing protocol. The advent of random burst sensing raises questions about the capabilities of in-vivo electrochemistry techniques, while opening introducing possibilities for novel approaches. Through a series of in-vitro experiments, this study aims to explore and validate novel electrochemical sensing approaches. Initial expository experiments tested assumptions about waveform design to detect dopamine concentrations by reducing amplitude and duration of forcing functions, as well as distinguishing norepinephrine concentrations. Next, large data sets collected on mixtures of dopamine, serotonin and pH validated a newly proposed "low amplitude random burst sensing" protocol, for both within-probe and out-of-probe modeling. Data collected on the same set of solutions also attempted to establish an order-millisecond random burst sensing approach. Preliminary endeavors into using convolutional neural networks also provided an example of an alternative modeling strategy. The results of this work challenge existing assumptions of neurochemistry, while demonstrating the capabilities of new neurochemical sensing approaches. This study will also act as a springboard for emerging technological developments in human neurochemistry.

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Keywords
voltammetry, machine learning, computational psychiatry, dopamine, serotonin, norepinephrine, electrochemistry, neurochemistry, convolutional neural networks
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