Improving in Situ Electrode Calibration with Principal Component Regression for Fast-Scan Cyclic Voltammetry

Abstract

Fast-scan cyclic voltammetry with a carbon-fiber microelectrode is an increasingly popular technique for in vivo measurements of electroactive neurotransmitters, most notably dopamine. Calibration of these electrodes is essential for many uses, but it is complicated by the many factors that affect an electrode's sensitivity when it is implanted in neural tissue. Experienced practitioners of fast-scan cyclic voltammetry are well aware that an electrode's sensitivity to dopamine depends on both the size and shape of the electrode's background waveform. In vitro electrode calibration is still the standard method, although a strategy for in situ calibration based on the size of the electrode's background waveform has previously been published. We reasoned that the accuracy and transferability of in situ calibration could be improved by using principal component regression to capture information contained in the shape of the background waveform. We use leave-one-out cross-validation to estimate the ability of this strategy to predict unknown electrodes and to compare its performance with that of the total-background-current strategy. The principal-component-regression strategy has significantly greater predictive performance than the total-background-current strategy, and the resulting calibration models can be transferred across independent laboratories. Importantly, multivariate quality-control statistics establish the applicability of the strategy to in vivo data. Adoption of the principal-component-regression strategy for in situ calibration will improve the interpretation of in vivo fast-scan cyclic voltammetry data.