Novel, User-Friendly Experimental and Analysis Strategies for Fast Voltammetry: Next Generation FSCAV with Artificial Neural Networks

Abstract

Fast-scan adsorption-controlled voltammetry (FSCAV) was recently derived from fast-scan cyclic voltammetry to estimate the absolute concentrations of neurotransmitters by using the innate adsorption properties of carbon fiber microelectrodes. This technique has improved our knowledge of serotonin dynamics in vivo. However, the analysis of FSCAV data is laborious and technically challenging. First, each electrode requires post-experimental in vitro calibration. Second, current analysis methods are semi-manual and time-consuming and require a steep learning curve. Finally, the calibration methods used do not adapt to nonlinear electrode responses. In this work, we provide freely accessible computational solutions to these issues. First, we design an artificial neural network (ANN) and train it with a large data set (calibrations from 140 electrodes by six different researchers) to achieve calibration-free estimations and improve predictive error. We discuss the power of the ANN to obtain a low predictive error without electrode-specific calibrations as a function of being able to predict the sensitivity of the electrode. We use the ANN to successfully predict the absolute serotonin concentrations of real in vivo data. Finally, we create a fast and user-friendly, fully automated analysis web platform to simplify and reduce the expertise required for the postanalysis of FSCAV signals.

Maximizing Electrochemical Information: A Perspective on Background-Inclusive Fast Voltammetry

“…No training paradigm can yet mimic the complex environment in the brain. However, even for a single analyte such as dopamine, a voltammetry technique paired with a suitable machine learning model that better bridges this in vitro–in vivo “generalization gap” would be extremely powerful; the state-of-the-art model in the field is moving toward this approach. ,− Background-inclusive models appear to be a critical step in reducing the generalization gap due to the underutilized information content in background currents, as discussed by Movassaghi and co-workers “As such, differences in the Helmholtz double layer, mass transport, analyte concentrations and adsorption, and other dynamic electrode surface properties occurring during an applied pulse are considered potential sources of analyte-specific information.…”

Developing an electrochemical sensor for thein vivomeasurements of dopamine

“…Despite this disadvantage, recent advancements in FSCV capabilities have combined the concept of rapid anodic stripping voltammetry to aid in quantification of basal levels of neurotransmitters. [120][121][122] Additionally, the stability of the background current is vital for accurate removal and ultimately accurate quantification in vivo. FSCV suffers tremendously from background drift, especially in biological samples.…”

Serotonin is a common thread linking different classes of antidepressants

“…FSCV files were exported from WCCV software and filtered, calibrated and analyzed using The Analysis Kid (87).…”