Interpreting Dynamic Interfacial Changes at Carbon Fiber Microelectrodes Using Electrochemical Impedance Spectroscopy.

Carbon-fiber microelectrodes are instrumental tools in neuroscience used for the electroanalysis of neurochemical dynamics and recordings of neural activity. However, performance is variable and dependent on fabrication strategies, the biological response to implantation, and the physical and chemical composition of the recording environment. This presents an analytical challenge, as electrode performance is difficult to quantitatively assess in situ, especially when electrodes are permanently implanted or cemented in place. We previously reported that electrode impedance directly impacts electrochemical performance for molecular sensing. In this work, we investigate the impacts of individual components of the electrochemical system on impedance. Equivalent circuit models for glass- and silica-insulated carbon-fiber microelectrodes were determined using electrochemical impedance spectroscopy (EIS). The models were validated based on the ability to assign individual circuit elements to physical properties of the electrochemical system. Investigations were performed to evaluate the utility of the models in providing feedback on how changes in ionic strength and carbon fiber material alter impedance properties. Finally, EIS measurements were used to investigate the electrode/solution interface prior to, during, and following implantation in live brain tissue. A significant increase in impedance and decrease in capacitance occur during tissue exposure and persist following implantation. Electrochemical conditioning, which occurs continually during fast-scan cyclic voltammetry recordings, etches and renews the carbon surface, mitigating these effects. Overall, the results establish EIS as a powerful method for characterization of carbon-fiber microelectrodes, providing unprecedented insight into how real-world factors affect the electrode/solution interface.

Drift Subtraction for Fast-Scan Cyclic Voltammetry Using Double-Waveform Partial-Least-Squares Regression.

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 do occur over time. These changes, referred to as electrochemical drift, 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 electrochemical drift are varied and can span the entire potential window, potentially obscuring the signal from any targeted analyte. We present a straightforward method for extending the duration of a single FSCV recording window. First, we have implemented voltammetric waveforms in pairs that consist of a smaller triangular sweep followed by a conventional voltammetric scan. The initial, abbreviated waveform is used to capture drift information that can serve as a predictor for the contribution of electrochemical drift 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 drift component to the voltammetric data. Here, DW-PLSR is 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 demonstrate that DW-PLSR is a powerful tool for evaluating and interpreting both rapid (seconds) and gradual (minutes) chemical changes captured in FSCV recordings over extended durations.

Electrochemical Selectivity Achieved Using a Double Voltammetric Waveform and Partial Least Squares Regression: Differentiating Endogenous Hydrogen Peroxide Fluctuations from Shifts in pH.

Hydrogen peroxide (H2O2) is a reactive oxygen species that serves as an important signaling molecule in normal brain function. At the same time, excessive H2O2 concentrations contribute to myriad pathological consequences resulting from oxidative stress. Studies to elucidate the diverse roles that H2O2 plays in complex biological environments have been hindered by the lack of robust methods for probing dynamic H2O2 fluctuations in living systems with molecular specificity. Background-subtracted fast-scan cyclic voltammetry at carbon-fiber microelectrodes provides a method of detecting rapid H2O2 fluctuations with high temporal and spatial resolution in brain tissue. However, H2O2 fluctuations can be masked by local changes in pH (ΔpH), because the voltammograms for these species can have significant peak overlap, hindering quantification. We present a method for removing ΔpH-related contributions from complex voltammetric data. By employing two distinct potential waveforms per scan, one in which H2O2 is electrochemically silent and a second in which both ΔpH and H2O2 are redox active, a clear distinction between H2O2 and ΔpH signals is established. A partial least-squares regression (PLSR) model is used to predict the ΔpH signal and subtract it from the voltammetric data. The model has been validated both in vitro and in vivo using k-fold cross-validation. The data demonstrate that the double waveform PLSR model is a powerful tool that can be used to disambiguate and evaluate naturally occurring H2O2 fluctuations in vivo.

Background Signal as an in Situ Predictor of Dopamine Oxidation Potential: Improving Interpretation of Fast-Scan Cyclic Voltammetry Data.

Background-subtracted fast-scan cyclic voltammetry (FSCV) has emerged as a powerful analytical technique for monitoring subsecond molecular fluctuations in live brain tissue. Despite increasing utilization of FSCV, efforts to improve the accuracy of quantification have been limited due to the complexity of the technique and the dynamic recording environment. It is clear that variable electrode performance renders calibration necessary for accurate quantification; however, the nature of in vivo measurements can make conventional postcalibration difficult, or even impossible. Analyte-specific voltammograms and scaling factors that are critical for quantification can shift or fluctuate in vivo. This is largely due to impedance changes, and the effects of impedance on these measurements have not been characterized. We have previously reported that the background current can be used to predict electrode-specific scaling factors in situ. In this work, we employ model circuits to investigate the impact of impedance on FSCV measurements. Additionally, we take another step toward in situ electrode calibration by using the oxidation potential of quinones on the electrode surface to accurately predict the oxidation potential for dopamine at any point in an electrochemical experiment, as both are dependent on impedance. The model, validated both in adrenal slice and live brain tissue, enables information encoded in the shape of the background voltammogram to determine electrochemical parameters that are critical for accurate quantification. This improves data interpretation and provides a significant next step toward more automated methods for in vivo data analysis.