Fast-scan cyclic voltammetry (FSCV) has been used for over 20 years to study rapid neurotransmission in awake and behaving animals. These experiments were first carried out with carbon-fiber microelectrodes (CFMs) encased in borosilicate glass, which can be inserted into the brain through micromanipulators and guide cannulas. More recently, chronically implantable CFMs constructed with small diameter fused-silica have been introduced. These electrodes can be affixed in the brain with minimal tissue response, which permits longitudinal measurements of neurotransmission in single recording locations during behavior. Both electrode designs have been used to make novel discoveries in the fields of neurobiology, behavioral neuroscience, and psychopharmacology. The purpose of this Review is to address important considerations for the use of FSCV to study neurotransmitters in awake and behaving animals, with a focus on measurements of striatal dopamine. Common issues concerning experimental design, data collection, and calibration are addressed. When necessary, differences between the two methodologies (acute vs chronic recordings) are discussed. The topics raised in this Review are particularly important as the field moves beyond dopamine toward new neurochemicals and brain regions.

Principal component regression, a multivariate calibration technique, is an invaluable tool for the analysis of voltammetric data collected in vivo with acutely implanted microelectrodes. This method utilizes training sets to separate cyclic voltammograms into contributions from multiple electroactive species. The introduction of chronically implanted microelectrodes permits longitudinal measurements at the same electrode and brain location over multiple recordings. The reliability of these measurements depends on a consistent calibration methodology. One published approach has been the use of training sets built with data from separate electrodes and animals to evaluate neurochemical signals in multiple subjects. Alternatively, responses to unpredicted rewards have been used to generate calibration data. This study addresses these approaches using voltammetric data from three different experiments in freely moving rats obtained with acutely implanted microelectrodes. The findings demonstrate critical issues arising from the misuse of principal component regression that result in significant underestimates of concentrations and improper statistical model validation that, in turn, can lead to inaccurate data interpretation. Therefore, the calibration methodology for chronically implanted microelectrodes needs to be revisited and improved before measurements can be considered reliable.

The use of principal component regression, a multivariate calibration method, in the analysis of in vivo fast-scan cyclic voltammetry data allows for separation of overlapping signal contributions, permitting evaluation of the temporal dynamics of multiple neurotransmitters simultaneously. To accomplish this, the technique relies on information about current-concentration relationships across the scan-potential window gained from analysis of training sets. The ability of the constructed models to resolve analytes depends critically on the quality of these data. Recently, the use of standard training sets obtained under conditions other than those of the experimental data collection (e.g., with different electrodes, animals, or equipment) has been reported. This study evaluates the analyte resolution capabilities of models constructed using this approach from both a theoretical and experimental viewpoint. A detailed discussion of the theory of principal component regression is provided to inform this discussion. The findings demonstrate that the use of standard training sets leads to misassignment of the current-concentration relationships across the scan-potential window. This directly results in poor analyte resolution and, consequently, inaccurate quantitation, which may lead to erroneous conclusions being drawn from experimental data. Thus, it is strongly advocated that training sets be obtained under the experimental conditions to allow for accurate data analysis.

Dysregulated catecholamine signaling has long been implicated in drug abuse. Although much is known about adaptations following chronic drug administration, little work has investigated how a single drug exposure paired with withdrawal influences catecholamine signaling in vivo. We used fast-scan cyclic voltammetry in freely moving rats to measure real-time catecholamine overflow during acute morphine exposure and naloxone-precipitated withdrawal in two regions associated with the addiction cycle: the dopamine-dense nucleus accumbens (NAc) and norepinephrine-rich ventral bed nucleus of the stria terminalis (vBNST). We compared dopamine transients in the NAc with norepinephrine concentration changes in the vBNST, and correlated release with specific withdrawal-related behaviors. Morphine increased dopamine transients in the NAc, but did not elicit norepinephrine responses in the vBNST. Conversely, dopamine output was decreased during withdrawal, while norepinephrine was released in the vBNST during specific withdrawal symptoms. Both norepinephrine and withdrawal symptoms could be elicited in the absence of morphine by administering naloxone with an α 2 antagonist. The data support reciprocal roles for dopamine and norepinephrine signaling during drug exposure and withdrawal. The data also support the allostasis model and show that negative-reinforcement may begin working after a single exposure/withdrawal episode.

Fast-scan cyclic voltammetry permits robust subsecond measurements of in vivo neurotransmitter dynamics, resulting in its established use in elucidating these species' roles in the actions of behaving animals. However, the technique's limitations, namely the need for digital background subtraction for analytical signal resolution, have restricted the information obtainable largely to that about phasic neurotransmitter release on the second-to-minute time scale. The study of basal levels of neurotransmitters and their dynamics requires a means of isolating the portion of the background current arising from neurotransmitter redox reactions. Previously, we reported on the use of a convolution-based method for prediction of the resistive-capacitive portion of the carbon-fiber microelectrode background signal, to improve the information content of background-subtracted data. Here we evaluated this approach for direct analytical signal isolation. First, protocol modifications (i.e., applied waveform and carbon-fiber type) were optimized to permit simplification of the interfering background current to components that are convolution-predictable. It was found that the use of holding potentials of at least 0.0 V, as well as the use of pitch-based carbon fibers, improved the agreement between convolution predictions and the observed background. Subsequently, it was shown that measurements of basal dopamine concentrations are possible with careful control of the electrode state. Successful use of this approach for measurement of in vivo basal dopamine levels is demonstrated, suggesting the approach may serve as a useful tool in expanding the capabilities of fast-scan cyclic voltammetry.

Neurovascular coupling is understood to be the underlying mechanism of functional hyperemia, but the actions of the neurotransmitters involved are not well characterized. Here we investigate the local role of the neurotransmitter norepinephrine in the ventral bed nucleus of the stria terminalis (vBNST) of the anesthetized rat by measuring O₂, which is delivered during functional hyperemia. Extracellular changes in norepinephrine and O₂ were simultaneously monitored using fast-scan cyclic voltammetry. Introduction of norepinephrine by electrical stimulation of the ventral noradrenergic bundle or by iontophoretic ejection induced an initial increase in O₂ levels followed by a brief dip below baseline. Supporting the role of a hyperemic response, the O₂ increases were absent in a brain slice containing the vBNST. Administration of selective pharmacological agents demonstrated that both phases of this response involve β-adrenoceptor activation, where the delayed decrease in O₂ is sensitive to both α- and β-receptor subtypes. Selective lesioning of the locus coeruleus with the neurotoxin DSP-4 confirmed that these responses are caused by the noradrenergic cells originating in the nucleus of the solitary tract and A1 cell groups. Overall, these results support that non-coerulean norepinephrine release can mediate activity-induced O₂ influx in a deep brain region.

The neurotransmitter dopamine is heavily implicated in intracranial self-stimulation (ICSS). Many drugs of abuse that affect ICSS behavior target the dopaminergic system, and optogenetic activation of dopamine neurons is sufficient to support self-stimulation. However, the patterns of phasic dopamine release during ICSS remain unclear. Early ICSS studies using fast-scan cyclic voltammetry (FSCV) rarely observed phasic dopamine release, which led to the surprising conclusion that it is dissociated from ICSS. However, several advances in the sensitivity (i.e., the use of waveforms with extended anodic limits) and analysis (i.e., principal component regression) of FSCV measurements have made it possible to detect smaller, yet physiologically relevant, dopamine release events. Therefore, this study revisits phasic dopamine release during ICSS using these tools. It was found that the anodic limit of the voltammetric waveform has a substantial effect on the patterns of dopamine release observed during continuous ICSS. While data collected with low anodic limits (i.e., +1.0 V) support the disappearance of phasic dopamine release observed in previous investigation, the use of high anodic limits (+1.3 V, +1.4 V) allows for continual detection of dopamine release throughout ICSS. However, the +1.4 V waveform lacks the ability to resolve narrowly spaced events, with the best balance of temporal resolution and sensitivity provided by the +1.3 V waveform. Ultimately, it is revealed that the amplitude of phasic dopamine release decays but does not fully disappear during continuous ICSS.

The use of multivariate analysis techniques, such as principal component analysis-inverse least-squares (PCA-ILS), has become standard for signal isolation from in vivo fast-scan cyclic voltammetric (FSCV) data due to its superior noise removal and interferent-detection capabilities. However, the requirement of collecting separate training data for PCA-ILS model construction increases experimental complexity and, as such, has been the source of recent controversy. Here, we explore an alternative method, multivariate curve resolution-alternating least-squares (MCR-ALS), to circumvent this issue while retaining the advantages of multivariate analysis. As compared to PCA-ILS, which relies on explicit user definition of component number and profiles, MCR-ALS relies on the unique temporal signatures of individual chemical components for analyte-profile determination. However, due to increased model freedom, proper deployment of MCR-ALS requires careful consideration of the model parameters and the imposition of constraints on possible model solutions. As such, approaches to achieve meaningful MCR-ALS models are characterized. It is shown, through use of previously reported techniques, that MCR-ALS can produce similar results to PCA-ILS and may serve as a useful supplement or replacement to PCA-ILS for signal isolation from FSCV data.