Affiliation: College of Arts and Sciences, Department of Chemistry
Fast-scan cyclic voltammetry is an electroanalytical technique used to probe neuromodulator signaling dynamics in vivo. The popularity of fast-scan cyclic voltammetry has grown in recent years because of its ability to address various neurobiology research interests in a simple, rapid, sensitive, manner in vivo in real time. However, there still remain challenges associated with the identification and detection of neuromodulators in vivo. Here, the application of principal component regression with residual analysis to in vivo fast-scan cyclic voltammetry data is presented for the first time in a straightforward, non-mathematical context. Changing the estimation of rank from the 99.5% cumulative variance method to Malinowski's F-test better separates relevant information from noise contained in the training set cyclic voltammograms. This allows the residual analysis procedure to function more accurately in determining whether the calibration model was applicable for the unknown data set being predicted. The presence of electrode drift is shown to dramatically alter concentration prediction when it is not included during the construction of the calibration model. Several tools including a residual color plot, the pseudoinverse of the principal component regression calibration matrix, and Cook's distance are shown to successfully improve the accuracy and robustness of training set construction and concentration prediction. In addition, the sensitivity of fast-scan cyclic voltammetry is increased by increasing the scan rate of the applied voltage waveform. Analog background subtraction allows some of the charging current to be neutralized, preventing saturation of the system. The in vitro and in vivo sensitivities are significantly improved, approaching a sub-nanomolar limit of detection. Scanning to a potential of 1.3 V requires waveform modification to maintain the increased sensitivity, but the surface integrity of the carbon-fiber microelectrode is altered. Taken together, these improvements allow for a more sensitive detection scheme and a more robust and accurate quantitation methodology associated with the detection of neuromodulators in vivo with fast-scan cyclic voltammetry.