2016
DOI: 10.1021/acschemneuro.6b00262
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Automated Algorithm for Detection of Transient Adenosine Release

Abstract: Spontaneous adenosine release events have been discovered in the brain that last only a few seconds. The identification of these adenosine events from fast-scan cyclic voltammetry (FSCV) data is difficult due to the random nature of adenosine release. In this study, we develop an algorithm that automatically identifies and characterizes adenosine transient features, including event time, concentration, and duration. Automating the data analysis reduces analysis time from 10–18 hours to about 40 minutes per exp… Show more

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Cited by 25 publications
(38 citation statements)
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“…A training set was created for each animal using the seven largest transients in the pre-drug data [ 34 ]. For some of the data, an automated analysis program was used that identifies random adenosine transients from FSCV data sets [ 35 ].…”
Section: Methodsmentioning
confidence: 99%
“…A training set was created for each animal using the seven largest transients in the pre-drug data [ 34 ]. For some of the data, an automated analysis program was used that identifies random adenosine transients from FSCV data sets [ 35 ].…”
Section: Methodsmentioning
confidence: 99%
“…Transients collected for TTX experiments were analyzed using a new automated algorithm and compared with control slices analyzed in the same manner. 52 All statistics were performed using Graphpad Prism 6. Mean values are given ± standard error of the mean (SEM).…”
Section: Methodsmentioning
confidence: 99%
“…The Venton group recently published the first steps toward an automated data analysis algorithm for FSCV. 114 This approach assesses FSCV data for chemical information by first choosing a time to describe the background. After background subtraction, the algorithm identifies fluctuations in the concentration of the chosen analyte from the background-subtracted data.…”
Section: Fscv: Under Construction…mentioning
confidence: 99%