Demon Voltammetry and Analysis software: Analysis of cocaine-induced alterations in dopamine signaling using multiple kinetic measures

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Abstract

The fast sampling rates of fast scan cyclic voltammetry make it a favorable method for measuring changes in brain monoamine release and uptake kinetics in slice, anesthetized, and freely moving preparations. The most common analysis technique for evaluating changes in dopamine signaling uses well-established Michaelis-Menten kinetic methods that can accurately model dopamine release and uptake parameters across multiple experimental conditions. Nevertheless, over the years, many researchers have turned to other measures to estimate changes in dopamine release and uptake, yet to our knowledge no systematic comparison amongst these measures has been conducted. To address this lack of uniformity in kinetic analyses, we have created the Demon Voltammetry and Analysis software suite, which is freely available to academic and non-profit institutions. Here we present an explanation of the Demon Voltammetry acquisition and analysis features, and demonstrate its utility for acquiring voltammetric data under in vitro, in vivo anesthetized, and freely moving conditions. Additionally, the software was used to compare the sensitivity of multiple kinetic measures of release and uptake to cocaine-induced changes in electrically evoked dopamine efflux in nucleus accumbens core slices. Specifically, we examined and compared tau, full width at half height, half-life, T 20, T 80, slope, peak height, calibrated peak dopamine concentration, and area under the curve to the well-characterized Michaelis-Menten parameters, dopamine per pulse, maximal uptake rate, and apparent affinity. Based on observed results we recommend tau for measuring dopamine uptake and calibrated peak dopamine concentration for measuring dopamine release. © 2011 Elsevier B.V.

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Yorgason, J. T., España, R. A., & Jones, S. R. (2011). Demon Voltammetry and Analysis software: Analysis of cocaine-induced alterations in dopamine signaling using multiple kinetic measures. Journal of Neuroscience Methods, 202(2), 158–164. https://doi.org/10.1016/j.jneumeth.2011.03.001

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