Open AccessStructural Similarity Image Analysis for Detection of Adenosine and Dopamine in Fast-Scan Cyclic Voltammetry Color Plots
Author(s) -
Pumidech Puthongkham,
Julian Rocha,
Jason R. Borgus,
Mallikarjunarao Ganesana,
Ying Wang,
Yuanyu Chang,
Andreas Gahlmann,
B. Jill Venton
Publication year - 2020
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.0c01214
Subject(s) - chemistry , cyclic voltammetry , adenosine , analyte , biological system , pattern recognition (psychology) , artificial intelligence , analytical chemistry (journal) , chromatography , electrode , computer science , biochemistry , electrochemistry , biology
Fast-scan cyclic voltammetry (FSCV) is widely used for in vivo detection of neurotransmitters, but identifying analytes, particularly mixtures, is difficult. Data analysis has focused on identifying dopamine from cyclic voltammograms, but it would be better to analyze all the data in the three-dimensional FSCV color plot. Here, the goal was to use image analysis-based analysis of FSCV color plots for the first time, specifically the structural similarity index (SSIM), to identify rapid neurochemical events. Initially, we focused on identifying spontaneous adenosine events, as adenosine cyclic voltammograms have a primary oxidation at 1.3 V and a secondary oxidation peak that grows in over time. Using SSIM, sample FSCV color plots were compared with reference color plots, and the SSIM cutoff score was optimized to distinguish adenosine. High-pass digital filtering was also applied to remove the background drift and lower the noise, which produced a better LOD. The SSIM algorithm detected more adenosine events than a previous algorithm based on current versus time traces, with 99.5 ± 0.6% precision, 95 ± 3% recall, and 97 ± 2% F 1 score ( n = 15 experiments from three researchers). For selectivity, it successfully rejected signals from pH changes, histamine, and H 2 O 2 . To prove it is a broad strategy useful beyond adenosine, SSIM analysis was optimized for dopamine detection and is able to detect simultaneous events with dopamine and adenosine. Thus, SSIM is a general strategy for FSCV data analysis that uses three-dimensional data to detect multiple analytes in an efficient, automated analysis.