(Invited) Drift Subtraction for Fast-Scan Cyclic Voltammetry Using Double-Waveform Partial Least Squares Regression

Abstract

Background-subtracted fast-scan cyclic voltammetry (FSCV) provides a method for detecting molecular fluctuations with high spatiotemporal resolution in the brain of awake and behaving animals. The rapid scan rates generate large background currents that are subtracted to reveal changes in analyte concentration. Although these background currents are relatively stable, small changes occur over time. These changes, referred to as electrochemical drift (ECD), result in background-subtraction artifacts that constrain the utility of FSCV, particularly when quantifying chemical changes that gradually occur over long measurement times (minutes). The voltammetric features of ECD are varied and can span the entire potential window, convoluting the signal from any targeted analyte. We present a straightforward method for extending the duration of a single FSCV recording window. Voltammetric waveforms are implemented in pairs. An initial, abbreviated waveform is used to capture ECD information that can serve as a predictor for the ECD contribution to the subsequent full voltammetric scan using partial least squares regression (PLSR). This double-waveform partial-least-squares regression (DW-PLSR) paradigm permits reliable subtraction of the ECD component to the voltammetric data. This talk will demonstrate how DW-PLSR can be used to improve quantification of adenosine, dopamine, and hydrogen peroxide fluctuations occurring >10 min from the initial background position, both in vitro and in vivo. The results reveal the power of this tool for evaluating and interpreting both rapid (seconds) and gradual (minutes) chemical changes captured in FSCV recordings over extended durations.