For over two decades, fast-scan cyclic voltammetry (FSCV) has served as a reliable analytical method for monitoring dopamine release in near real-time in vivo. However, contemporary FSCV techniques have been limited to measure only rapid (on the order of seconds, i.e. phasic) changes in dopamine release evoked by either electrical stimulation or elicited by presentation of behaviorally salient stimuli, and not slower changes in the tonic extracellular levels of dopamine (i.e. basal concentrations). This is because FSCV is inherently a differential method that requires subtraction of prestimulation tonic levels of dopamine to measure phasic changes relative to a zeroed baseline. Here, we describe the development and application of a novel voltammetric technique, multiple cyclic square wave voltammetry (M-CSWV), for analytical quantification of tonic dopamine concentrations in vivo with relatively high temporal resolution (10 s). M-CSWV enriches the electrochemical information by generating two dimensional voltammograms which enable high sensitivity (limit of detection, 0.17 nM) and selectivity against ascorbic acid, and 3,4-dihydroxyphenylacetic acid (DOPAC), including changes in pH. Using M-CSWV, a tonic dopamine concentration of 120 ± 18 nM (n = 7 rats, ± SEM) was determined in the striatum of urethane anethetized rats. Pharmacological treatments to elevate dopamine by selectively inhibiting dopamine reuptake and to reduce DOPAC by inhibition of monoamine oxidase supported the selective detection of dopamine in vivo. Overall, M-CSWV offers a novel voltammetric technique to quantify levels and monitor changes in tonic dopamine concentrations in the brain to further our understanding of the role of dopamine in normal behavior and neuropsychiatric disorders.

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Dopamine (DA) modulates central neuronal activity through both phasic (second to second) and tonic (minutes to hours) terminal release. Conventional fast-scan cyclic voltammetry (FSCV), in combination with carbon fiber microelectrodes, has been used to measure phasic DA release in vivo by adopting a background subtraction procedure to remove background capacitive currents. However, measuring tonic changes in DA concentrations using conventional FSCV has been difficult because background capacitive currents are inherently unstable over long recording periods. To measure tonic changes in DA concentrations over several hours, we applied a novel charge-balancing multiple waveform FSCV (CBM-FSCV), combined with a dual background subtraction technique, to minimize temporal variations in background capacitive currents. Using this method, in vitro, charge variations from a reference time point were nearly zero for 48 h, whereas with conventional background subtraction, charge variations progressively increased. CBM-FSCV also demonstrated a high selectivity against 3,4-dihydroxyphenylacetic acid and ascorbic acid, two major chemical interferents in the brain, yielding a sensitivity of 85.40 ± 14.30 nA/μM and limit of detection of 5.8 ± 0.9 nM for DA while maintaining selectivity. Recorded in vivo by CBM-FSCV, pharmacological inhibition of DA reuptake (nomifensine) resulted in a 235 ± 60 nM increase in tonic extracellular DA concentrations, while inhibition of DA synthesis (α-methyl-dl-tyrosine) resulted in a 72.5 ± 4.8 nM decrease in DA concentrations over a 2 h period. This study showed that CBM-FSCV may serve as a unique voltammetric technique to monitor relatively slow changes in tonic extracellular DA concentrations in vivo over a prolonged time period.

Although fast-scan cyclic voltammetry (FSCV) has been widely used for in vivo neurochemical detection, the sensitivity and selectivity of the technique can be further improved. In this study, we develop fast cyclic square-wave voltammetry (FCSWV) as a novel voltammetric technique that combines large-amplitude cyclic square-wave voltammetry (CSWV) with background subtraction. A large-amplitude, square-shaped potential was applied to induce cycling through multiple redox reactions within a square pulse to increase sensitivity and selectivity when combined with a twodimensional voltammogram. As a result, FCSWV was significantly more sensitive than FSCV (n = 5 electrodes, two-way ANOVA, p = 0.0002). In addition, FCSWV could differentiate dopamine from other catecholamines (e.g., epinephrine and norepinephrine) and serotonin better than conventional FSCV. With the confirmation that FCSWV did not influence local neuronal activity, despite the large amplitude of the square waveform, it could monitor electrically induced phasic changes in dopamine release in rat striatum before and after injecting nomifensine, a dopamine reuptake inhibitor.

Fast scan cyclic voltammetry (FSCV) has been commonly used to measure extracellular neurotransmitter concentrations in the brain. Due to the unstable nature of the background currents inherent in FSCV measurements, analysis of FSCV data is limited to very short amounts of time using traditional background subtraction. In this paper, we propose the use of a zero-phase high pass filter (HPF) as the means to remove the background drift. Instead of the traditional method of low pass filtering across voltammograms to increase signal to noise ratio, a HPF with a low cutoff frequency was applied to temporal data set at each voltage point to remove background drift. As a result, the HPF utilizing cutoff frequencies between 0.001 Hz to 0.01 Hz could be effectively used to a set of FSCV data for removing drifting patterns while preserving the temporal kinetics of the phasic dopamine response recorded in vivo. In addition, compared to a drift removal method using principal component analysis, this was found to be significantly more effective in reducing drift (unpaired t-test p<0.0001, t=10.88) when applied to data collected from tris buffer over 24 hours although a drift removal method using principal component analysis also showed the effective background drift reduction. The HPF was also applied to 5 hours of FSCV in vivo data. Electrically evoked dopamine peaks, observed in the nucleus accumbens, were clearly visible even without background subtraction. This technique provides a new, simple, and yet robust, approach to analyse FSCV data with unstable background.