Abstract
Drosophila melanogaster, the fruit fly, uses the neurotransmitters dopamine and octopamine to mediate learning, enabling adaptive behaviors such as reward seeking and punishment avoidance. Their colocalization in the mushroom bodies makes it challenging to study their individual contributions. Fast-scan cyclic voltammetry allows subsecond monitoring of neurotransmitter dynamics, but simultaneous detection of dopamine and octopamine remains difficult due to overlapping oxidation and reduction peaks in their voltammograms. Traditional signal separation methods, such as principal component regression, assume fixed voltammogram shapes across time. However, this assumption fails for octopamine, which exhibits time-varying voltammograms due to secondary oxidation processes at the same potential as dopamine oxidation. In this study, we use a deep learning-based regression approach that analyzes color plots to separate dopamine and octopamine signals collected in Drosophila. Using the distinct primary oxidation peak of octopamine as input, a modified U-Net architecture was trained as a regression model to predict the secondary oxidation peak and subtract it from the dopamine-octopamine mixture to isolate dopamine contributions. The method achieved normalized root-mean-square errors of 0.06 for dopamine and 0.08 for octopamine, calculated against ground truth components from computationally generated mixtures. Thus, estimation errors are under 10% and there is reliable signal separation. Applications to experimentally measured mixtures demonstrate accurate signal decomposition, with the predicted dopamine and octopamine concentrations showing strong agreement (r = 0.93, CCC = 0.93) in scatter plot analysis. Thus, machine learning provides a robust framework to deconvolute overlapping electrochemical signals from octopamine and dopamine, facilitating simultaneous neurochemical detection.
