We previously reported on the use of fast cyclic square wave voltammetry (FCSWV) as a new voltammetric technique. Fourier transform electrochemical impedance spectroscopy (FTEIS) has recently been utilized to provide information that enables a detailed analytical description of an electrified interface. In this study, we report on attempts to combine FTEIS with FCSWV (FTEIS−FCSWV) and demonstrate the feasibility of FTEIS−FCSWV in the in vivo detection of neurotransmitters, thus giving a new type of electrochemical impedance information such as biofouling on the electrode surface. From FTEIS−FCSWV, three new equivalent circuit element voltammograms, consisting of charge-transfer resistance (R ct ), solution-resistance (R s ), and double-layer capacitance (C dl ) voltammograms were constructed and investigated in the phasic changes in dopamine (DA) concentrations. As a result, all R ct , R s , and C dl voltammograms showed different DA redox patterns and linear trends for the DA concentration (R 2 > 0.99). Furthermore, the R ct voltammogram in FTEIS− FCSWV showed lower limit of detection (21.6 ± 15.8 nM) than FSCV (35.8 ± 17.4 nM). FTEIS−FCSWV also showed significantly lower prediction errors than FSCV in selectivity evaluations of unknown mixtures of catecholamines. Finally, C dl from FTEIS− FCSWV showed a significant relationship with fouling effect on the electrode surface by showing decreased DA sensitivity in both flow injection analysis experiment (r = 0.986) and in vivo experiments. Overall, this study demonstrates the feasibility of FTEIS− FCSWV, which could offer a new type of neurochemical spectroscopic information concerning electrochemical monitoring of neurotransmitters in the brain, and the ability to estimate the degree of sensitivity loss caused by biofouling on the electrode surface.

Fast-scan cyclic voltammetry (FSCV) is a technique for measuring phasic release of neurotransmitters with millisecond temporal resolution. The current data are captured by carbon fiber microelectrodes, and non-Faradaic current is subtracted from the background current to extract the Faradaic redox current through a background subtraction algorithm. FSCV is able to measure neurotransmitter concentrations in vivo down to the nanomolar scale, making it a very robust and useful technique for probing neurotransmitter release dynamics and communication across neural networks. In this study, we describe a technique that can further lower the limit of detection of FSCV. By taking advantage of a “waveform steering” technique and by amplifying only the oxidation peak of dopamine to reduce noise fluctuations, we demonstrate the ability to measure dopamine concentrations down to 0.17 nM. Waveform steering is a technique to dynamically alter the input waveform to ensure that the background current remains stable over time. Specifically, the region of the input waveform in the vicinity of the dopamine oxidation potential (∼0.6 V) is kept flat. Thus, amplification of the input waveform will amplify only the Faradaic current, lowering the existing limit of detection for dopamine from 5.48 to 0.17 nM, a 32-fold reduction, and for serotonin, it lowers the limit of detection from 57.3 to 1.46 nM, a 39-fold reduction compared to conventional FSCV. Finally, the applicability of steered FSCV to in vivo dopamine detection was also demonstrated in this study. In conclusion, steered FSCV might be used as a neurochemical monitoring tool for enhancing detection sensitivity.

Tonic extracellular neurotransmitter concentrations are important modulators of central network homeostasis. Disruptions in these tonic levels are thought to play a role in neurologic and psychiatric disease. Therefore, ways to improve their quantification are actively being investigated. Previously published voltammetric software packages have implemented FSCV, which is not capable of measuring tonic concentrations of neurotransmitters in vivo. In this paper, custom software was developed for near-real-time tracking (scans every 10 s) of neurotransmitters’ tonic concentrations with high sensitivity and spatiotemporal resolution both in vitro and in vivo using cyclic voltammetry combined with dynamic background subtraction (M-CSWV and FSCAV). This software was designed with flexibility, speed, and user-friendliness in mind. This software enables near-real-time measurement by reducing data analysis time through an optimized modeling algorithm, and efficient memory handling makes long-term measurement possible. The software permits customization of the cyclic voltammetric waveform shape, enabling experiments to detect a specific analyte of interest. Finally, flexibility considerations allow the user to alter the fitting parameters, filtering characteristics, and size and shape of the analyte kernel, based on data obtained live during the experiment to obtain accurate measurements as experimental conditions change. Herein, the design and advantages of this near-real-time voltammetric software are described, and its use is demonstrated in in vivo experiments.