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Michael AC, Borland LM, editors. Electrochemical Methods for Neuroscience. Boca Raton (FL): CRC Press/Taylor & Francis; 2007.

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Chapter 2Rapid Dopamine Release in Freely Moving Rats

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The Phenomenon of Dopamine Transients

Dopamine concentrations in the striatum of rats fluctuate during significant behavioral and pharmacological events, such as copulation and administration of addictive drugs. These changes have traditionally been measured in awake animals using microdialysis with offline electrochemical analysis (Westerink 1995). Microdialysis integrates the dopamine signal over space and time. Typically, the dialysis probe is 1–2 mm long and dialysate samples are collected over 5–15 min.

However, many of the behavioral or environmental events at which dopamine release is expected occur on a much faster timescale than microdialysate sampling. Fast-scan cyclic voltammetry (FSCV) measures dopamine concentrations in much smaller spatial and temporal dimensions: typically at approximately 6 μm×100 μm cylindrical electrodes and sampling every 100 ms. Thirteen years after FSCV was used to monitor electrically-evoked dopamine release in anesthetized rats (Stamford et al. 1984; Millar et al. 1985), the method was used to detect discrete, naturally occurring dopamine signals in the nucleus accumbens (NAc) shell of awake rats during behavior (Rebec et al. 1997). In parallel, detection limits for in vivo voltammetric methods have improved from 5 μM (Ewing, Bigelow, and Wightman 1983) to less than 20 nM (Cheer et al. 2004; Stuber et al. 2005). As a result, several papers have now documented dopamine concentration transients in response to a variety of behavioral and pharmacological events.

Physiology of Dopamine Transients

Dopamine transients are subsecond fluctuations in dopamine concentrations, lasting from 0.2 to several seconds and ranging from 10 nM to more than 1 μM. For example, Figure 2.1 shows a spontaneous dopamine transient preceding an electrically stimulated dopamine signal recorded in the NAc core. Transients occur at baseline frequencies in the dorsal and ventral striatum of rats (Robinson, Heien, and Wightman 2002), and increase in frequency during novelty (Rebec et al. 1997; Robinson and Wightman 2004), social interaction (Robinson et al. 2001; Robinson, Heien, and Wightman 2002), pharmacological challenge (Cheer et al. 2004; Robinson and Wightman 2004; Stuber et al. 2005) and self-administration of reinforcers (Phillips et al. 2003b; Roitman et al. 2004; Stuber, Wightman, and Carelli 2005, Stuber et al. 2005).

FIGURE 2.1. (See color insert following page 272.

FIGURE 2.1

(See color insert following page 272.) Spontaneous and electrically evoked dopamine transients from the NAc of a rat. The color plot (bottom, Michael et al. 1998) shows changes in current (color) by applied potential (y-axis) over 6 s (x-axis). The unfiltered (more...)

Dopamine transients measured in terminal regions are presumed to arise from burst firing of dopamine neurons (Garris and Rebec 2002). In awake rats, burst firing of dopamine neurons occurs spontaneously (Freeman, Meltzer, and Bunney 1985) and can be triggered by operant responding and reinforcement (Hyland et al. 2002), as well as by drug administration (Freeman and Bunney 1987). Moreover, approximately 25% of dopamine neurons in awake rats appear to fire the majority of spikes in near-coincidence with another neuron (Freeman, Meltzer, and Bunney 1985; Freeman and Bunney 1987; Hyland et al. 2002), suggesting electrical coupling or shared afferent input. Although electrical coupling does not appear to synchronize tonic firing of dopamine neurons (Vandecasteele, Glowinski, and Venance 2005), it may promote synchrony of NMDA-driven bursts (Komendantov and Canavier 2002). Together, these electrophysiological data predict that dopamine concentrations might exhibit local, subsecond fluctuations, such as the dopamine transients observed via FSCV.

Indeed, dopamine transients can be measured after electrical stimulation trains delivered to dopamine fibers or cell bodies. While the majority of FSCV studies have used stimulation trains (e.g., 24 pulses at 60 Hz) that are faster or longer than measured bursts of spikes in vivo, reproducible changes in dopamine concentrations have been observed time-locked to more physiological patterns, such as 12 pulses at 20 Hz (Figure 2.1). It is noteworthy, however, that the occurrence of detected dopamine transients in freely moving rats is much less frequent than the occurrence of firing bursts (>3 spikes) by dopamine neurons: 0.01–0.02 Hz (approximated from data in Table 2.1) versus 0.1 Hz (Hyland et al. 2002). This discrepancy suggests that only the largest bursts or simultaneous bursts from multiple neurons produce changes in dopamine concentrations detectable at the carbon-fiber electrode.

TABLE 2.1

TABLE 2.1

Basal Frequency, Amplitude, and Duration of Dopamine Transients in the Dorsal and Ventral Striatum of Freely Moving Rats

Advances in Recording Technique

The ability to observe dopamine transients in vivo has followed technical advances in FSCV. Fast dopamine measurements were first made in freely moving rats following electrical stimulation of dopamine fibers (Garris et al. 1997). These early experiments used disk electrodes with an electroactive surface of approximately 0.5 mm2. While the disk electrodes yielded low noise the spatial resolution made it difficult to find adequate dopamine release sites because they are about 4 μm apart in the dorsal striatum and approximately 6 μm apart in the ventral striatum of the rat. Switching to a cylindrical electrode with a surface area of approximately 2 mm2 allowed much easier placement and more sensitivity, as more release sites were sampled (Cahill et al. 1996).

Other methodological changes that resulted in improved signal-to-noise ratios were using a glass seal instead of epoxy at the carbon–glass interface, pretreating the electrodes with purified isopropyl alcohol (Bath et al. 2000), securing the electrodes in sturdy micromanipulators (Garris et al. 1997; Phillips et al. 2003a), upgrading the instrumentation, refining the headstage construction, optimizing the applied potential (Heien et al. 2003), and mathematically subtracting contributions of interfering species (such as pH) from the dopamine signal (Runnels et al. 1999; Heien et al. 2005). These issues will be discussed in greater detail in the following sections. These advances have allowed dopamine detection limits to drop to less than 20 nM, and research reports have progressed from measuring no spontaneous dopamine transients, to detecting only the largest transients, to observing basal frequencies of greater than 1 transient per minute.

Measurement of Dopamine Transients: Fast-Scan Cyclic Voltammetry

In voltammetric recording, a potential is applied to an electrode and the resulting current changes due to oxidation and reduction of chemicals at the electrode surface are measured. The basic difference between voltammetric techniques such as FSCV, constant-potential amperometry, chronoamperometry, or differential normal-pulse voltammetry, is the pattern of the applied potential. FSCV employs a triangular waveform that typically ramps from a negative potential to a positive potential and back, e.g., −0.4 to +1.0 V and back at 300 V/s, repeated at 10 Hz, and holding at −0.4 V between scans. Thus, current changes due to pH shifts and redox reactions are measured at the range of potentials in the triangular waveform. The charging of the double layer around the electrode results in a large, relatively stable background current that can be subtracted to reveal smaller variations in current due to oxidation of dopamine. This subtraction of the background current defines FSCV as a differential technique that measures changes in current as opposed to absolute currents.

Changes in current due to dopamine or other compounds are determined by the backgroundsubtracted cyclic voltammogram, which is a plot of the current versus applied potential. When measured versus a Ag/AgCl electrode, dopamine has an oxidation potential of approximately 0.6 V and the ortho-quinone reduces back to dopamine at approximately −0.2 V. This profile is different from other compounds that might rapidly change in vivo, including serotonin, pH, and nitric oxide (Heien et al. 2003). However, the cyclic voltammogram of norepinephrine has the same profile as dopamine, requiring that anatomical recording sites be considered when identifying a signal as “dopamine” or simply “catecholamine.”

Many of the methodological details of FSCV in freely moving rats are described elsewhere in great detail (Phillips et al. 2003a). The main points are reviewed here, along with with some updates and explanations.

Electrode Preparation

For freely-moving rat experiments, carbon-fiber microelectrodes that are encased in glass and secured in micromanipulators were used (more details in Phillips et al. 2003a; also see (Millar and Pelling 2001) for instructions on carbon-fiber electrode construction). T-650 carbon fibers (Thornel, Greenville, SC) that are formed by pyrolysis of polyacrylonitrile (PAN) were used. The fiber is loaded into a glass capillary (0.6 mm O.D., 0.4 mm I.D.; A-M Systems, Carlsborg, WA) and pulled in an electrode puller (Narishige, Tokyo, Japan). The seal can be made with epoxy or by adjusting the parameters of the electrode puller such that a glass seal of at least 100 μm is formed. The fiber extending from the seal is trimmed under the microscope to the preferred length, typically 50–100 μm (although earlier studies used electrodes up to 250 μm length). The maximum length of the electrode is generally determined by the noise limitations of the amplifiers in the associated instrumentation. The higher the noise of the amplifiers, the larger the electrode that must be used.

The electrode is then secured in a micromanipulator that allows stability and precise placement into brain tissue. While a commercial model is available for electrophysiological measurements (Biela microdrives, Crist Instrument Co., Hagerstown, MD), we have used a manipulator made in-house at the University of North Carolina Department of Chemistry instrumentation shop. This fits into the locking guide cannula from Bioanalytical Systems (West Lafayette, IN) and allows for at least 75 μm precision. A silver wire is inserted and sealed in the open glass end of the electrode to connect to the headstage. Before use, the electrodes are pretreated by soaking in purified isopropyl alcohol for at least 10 min to increase sensitivity (Bath et al. 2000).

Electrode Calibration

The electrodes are calibrated in vitro to establish the relationship between current and dopamine concentration. This can be carried out either pre- or post-experiment; the more conservative method is to calibrate post-experiment (Logman et al. 2000) as this accounts for changes at the electrode surface associated with brain tissue, such as protein adhesion. Immediately after the experiment, the electrode is removed from the rat and the tip is rinsed with a strong, focused stream of water. Next, it is placed in purified isopropyl alcohol until calibration. A minimal calibration solution is used that consists of HEPES buffer (20 mM HEPES, 150 mM NaCl, 1.2 mM CaCl2, pH = 7.4) or tris buffer (12 mM tris–HCl, 140 mM NaCl, 3.25 mM KCl, 1.2 mM CaCl2, 1.25 mM NaH2PO4, 1.2 mM MgCl2, 2 mM NaSO4, pH = 7.4); tris buffer is required for applied waveforms of greater than 1 V, as the HEPES will oxidize at those potentials. The electrode is placed in a flow cell in a constant stream of buffer, and periodically the fluid is switched to buffer with known quantities of dopamine, typically 0.5–2 μM. The voltammetric signal resulting from the pulse of dopamine at the electrode is used to estimate the current/concentration ratio for naturally occurring dopamine transients. Calibrations are typically conducted at room temperature, as diffusion is minimally different at room temperature versus 37°C (Bard and Faulkner 2001).

Surgical Preparation

The goal of the surgical procedure is to implant the guide cannula (for later electrode insertion), the reference electrode, and the stimulating electrode to electrically stimulate dopamine transients. Although only the reference and the guide cannula (with the carbon-fiber electrode) are needed to record dopamine transients, the ability to electrically evoke dopamine release via the stimulating electrode is crucial for the positive identification of dopamine transients (see “Targeting Dopamine Transients” in this chapter).

The surgery is completed in two stages (more details in Phillips et al. 2003a). The first stage consists of securing the guide cannula (locking guide with stylette, Bioanalytical Systems, West Lafayette, IN) and reference electrode (Ag/AgCl, ~5 mm length) with cranioplastic cement and screw anchors. The second stage is placing the stimulating electrode (stainless steel, 0.2 mm, bipolar; Plastics One, Roanoke, VA). To do this, a carbon-fiber electrode is inserted into the brain, typically the caudate–putamen (CP), via the guide cannula and connected to the FSCV instrumentation. Next, the stimulating electrode is placed above a dopamine fiber area, typically the medial forebrain bundle or the substantia nigra/ventral tegmental area, and incrementally lowered. Current (125 μA, 24–60 p, 60 Hz) is delivered through the stimulating electrode at each increment until a dopamine signal is observed at the carbon-fiber electrode. After the dopamine signal is optimized, the stimulating electrode is cemented in place.

Electrochemical Measurements

Instrumentation and Software

High-sensitivity dopamine measurements require instrumentation that is low-noise and capable of measuring in the subnanoampere regime with a broad frequency bandpass (Cahill et al. 1996). The requirements are similar to those in patch-clamp recordings; indeed, patch-clamp amplifiers have been used for recordings with carbon-fiber electrodes. The first commercial instrument specifically designed for FSCV with carbon-fiber electrodes was introduced by Ensman Instrumentation in the 1980s. This instrument was well suited for use with anesthetized animals or brain slices and is still available from Cypress Systems, Lawrence, Kansas. Another commercially available system for FSCV is the Millar Voltammeter, P.D. Systems, West Moseley, U.K. However, to make measurements in awake animals, a new instrument, termed the universal electrochemical instrument (UEI), has been designed by the instrument shop in the Department of Chemistry, University of North Carolina at Chapel Hill. The UEI is modular so that different components can be developed and used within a single chassis that contains the power supply. The instrument is designed to be used with computer-controlled interface boards (National Instruments, Austin, TX) controlled by locally written software (LabVIEW, National Instruments). An early version of the software has been described in detail (Michael et al. 1999), and a description of recent modifications is available (Heien et al. 2003) and is discussed below.

In its present form, the UEI consists of two modules and a headstage amplifier unit that is mounted on the head of the preparation (Figure 2.2). One of the modules serves as a signal-conditioning unit for the applied, computer-generated waveform. The module filters and attenuates the applied waveform to remove digital noise. These steps are necessary because any noise on the applied waveform is amplified in the voltammetric recordings (Howell et al. 1986). The output of the first unit goes to the second module that combines all of the lines into a single cable so that they can be routed through a commutator to the headstage. The lines in this cable consist of ±15 V and common (to drive the operational amplifier in the headstage), the applied waveform, and an output voltage that is proportional to the voltammetric current. The output voltage can be further amplified with the second module and it is then sent to the analog-to-digital converter of the computer interface board.

FIGURE 2.2. Block diagram of the components used in the instrument for cyclic voltammetric measurement of dopamine.

FIGURE 2.2

Block diagram of the components used in the instrument for cyclic voltammetric measurement of dopamine. (Figure courtesy of Colin McKinney, University of North Carolina. With permission.)

The critical unit of the instrument is the headstage. It contains surface-mount amplifiers and components so that it is lightweight. The applied waveform contained in the cable from the commutator is connected to the noninverting input of the current transducer, and the inverting input is connected directly to the carbon-fiber electrode to convert the voltammetric current to an output voltage. The headstage circuitry is designed such that the applied waveform is removed from the output voltage. The reference electrode is connected to system common. This design was adopted so that future applications could use multiple carbon-fiber electrodes or could be adapted for use with electrophysiological probes, with all voltages referenced to the reference electrode held at system common.

The software used to interface the instrumentation, TarHeel CV, is being commercialized by ESA Biosciences, Inc., Chelmsford, MA. This software controls the timing of the experiment and generates the applied triangular waveform and outputs it from a digital-to-analog converter that is a component on the interface board. The output voltages from the experiment are digitized and stored in computer memory and subsequently recorded on the computer disk. The applied potential can be customized: waveform shape, number of points, voltages, scan rate, and repetition rate are all variable. Aspects of the electrical stimulation to the dopamine fibers can be specified, such as the polarity, current, frequency, and pulses of the electrical stimulation, as well as the timing between voltammetric scans and stimulations to reduce artifacts. Electrical stimulations can also be externally triggered by events such as lever presses, or programmed as in intracranial selfstimulation play-back patterns. A digital oscilloscope is also included in TarHeel CV, showing the applied waveform and the nonbackground-subtracted cyclic voltammogram. Other data collection parameters that can be set are the gain of the signal and data collection parameters (single or sequential files). The oxidation current at a specified potential as well as the background current of the electrode are shown in real-time during data collection.

TarHeel CV has an analysis component that can be used within the data collection program or separately as a stand-alone program. One file at a time can be analyzed, with the user setting the background (typically 10 consecutive scans, or 1 s) and the cyclic voltammograms (typically 3–10 consecutive scans). The instrumentation does not filter the electrochemical signal as it is collected. Instead, the data can be smoothed or filtered as appropriate in the analysis component, including filtering the color plot or current versus time data or performing a three-dimensional smoothing of the data. Data are presented as a color plot, current versus time trace, and cyclic voltammogram. Data from these graphs can be written to text files for use in other programs. The signal to noise, based on the user-specified background and cyclic voltammograms, is automatically calculated. This screen is the link to further analyses targeting dopamine transients (see “Targeting Dopamine Transients” in this chapter).

Experimental Protocol

When designing an experimental protocol, technical aspects of electrical stimulation of dopamine neurons and the waveform parameters as well as practical aspects of timing must be considered.

Electrically Stimulated Dopamine Release

Dopamine release can be reproducibly evoked by stimulation of the dopamine cell bodies or fibers. This has been a valuable technique for analyzing drug effects on dopamine release and uptake, as the signal can be modeled to estimate the amount of dopamine release per stimulation pulse, as well as the Michaelis–Menten parameters, Vmax and Km, for dopamine uptake (Garris et al. 2003).

Electrically stimulated dopamine release is also necessary in experiments measuring spontaneous dopamine transients. First, it ensures that the electrode is positioned in a site with dopamine terminals. Second, the comparison of electrically evoked dopamine release before and after the experiment can be used to evaluate signal-to-noise and the integrity of the carbon fiber. Finally, it provides the template by which dopamine transients are identified (see “Targeting Dopamine Transients” in this chapter).

Electrical stimulations used in our studies range from 4 to 24 pulses (biphasic, 2 ms/phase), applied at 10–60 Hz, at a current of 80–200 μA. The larger stimulation trains produce larger and more reproducible signals, whereas the smaller trains are more physiological (e.g., <4 pulses at 10–20 Hz). To evoke a reproducible signal, the stimulations must be spaced in time to allow for replenishment of the readily releasable pool. Electrically stimulated dopamine release has been modeled to reveal facilitation and depression mechanisms that are dependent on previous stimulations (Montague et al. 2004). Therefore, the larger the stimulation, the more time must elapse before applying a subsequent stimulation. For example, 2 min is an adequate interval between stimulations of 24 pulses at 60 Hz and 125 μA.

Waveform Selection

Traditionally, we have used a triangle waveform with applied potentials of −0.4 to +1.0 V and back (i.e., the “traditional waveform”) to measure electrically evoked dopamine release, and some studies of dopamine transients have also reported use of these applied potentials. However, we have also used waveforms with extended anodic and/or cathodic potentials to enhance sensitivity to dopamine (i.e., “extended waveforms”). With the traditional waveform, the electrode potential is held at −0.4 V between scans; this promotes the adsorption of dopamine to the carbon surface to enhance sensitivity (Baur et al. 1988). When this holding potential is increased to more negative values, sensitivity increases; however, the response time of the electrode to changes in dopamine slows (Heien et al. 2003). Extending the anodic potential from +1.0 to +1.4 V also increases sensitivity to dopamine, but at the expense of selectivity to potential interfering compounds (Heien et al. 2003). Heien and colleagues directly compared the traditional waveform to an extended waveform of −0.6 to +1.4 V and back: although there was a ninefold increase in sensitivity to dopamine in vivo, there were slower response kinetics and less selectivity between dopamine and possible interfering species such as dihydroxyphenylacetic acid (DOPAC), serotonin, 5-hydroxy indoleacetic acid (5-HIAA), ascorbic acid, and uric acid. Some of these compounds can be excluded as interfering species by targeting fast current changes that would be observed for vesicular neurotransmitter release but not for metabolic products. Nevertheless, strict criteria are needed to separate naturally occurring dopamine transients from other sources of current change in vivo.

Because the extended waveforms exhibit decreased time resolution compared to the traditional waveform, they are less suitable for making kinetic measurements of dopamine transients such as [DA]max, half-width, and area under the peak. While extended waveforms are useful for comparing relative kinetics of dopamine transients (e.g., a percent increase in [DA]max associated with cocaine delivery), they will not yield the absolute kinetics required for modeling dopamine release and uptake parameters (Wu et al. 2001).

The FSCV scan rate determines the time required for a single voltage excursion. Typically, a scan rate of 400 V/s allows a cyclic voltammogram to be acquired in 10 ms. The scan rate also affects the amplitude of the current waveform. The current arising from charging the double-layer capacitance of the electrode, along with any currents associated with species confined to the electrode surface, increases linearly with the scan rate. Thus, doubling the scan rate from 400 to 800 V/s causes the background current to double. Because the majority of the signal amplitude from dopamine oxidation arises from dopamine that is adsorbed to the electrode (Bath et al. 2000; Heien et al. 2003), the dopamine signal will double as well. Serotonin and norepinephrine also adsorb and exhibit similar behavior. However, molecules that are not cations at physiological pH, such as DOPAC and ascorbate, do not adsorb and exhibit different behaviors. For those species, the current during the cyclic voltammogram is controlled by their diffusion to the electrode. Diffusion controlled electrochemical processes increase in amplitude with the square root of the scan rate (Bard and Faulkner 2001), i.e., the faster the scan rate, the lower the contribution from these interfering species.

The time between cyclic voltammograms (the repetition rate) also affects the amplitude of species that adsorb to the electrode. The greater the time between scans, the more the adsorption processes can approach equilibrium (Bath et al. 2000). The net result is a greater signal for dopamine. We typically use 100 ms, which is a compromise between allowing time for dopamine to accumulate on the electrode and maintaining sufficient time resolution to follow dopamine’s concentration fluctuations.

In summary, when choosing a waveform, the researcher must balance temporal precision and selectivity with sensitivity and know the limitations of each waveform. Early reports of dopamine transients used the traditional waveform (Rebec et al. 1997; Robinson, Heien, and Wightman 2002; Robinson and Wightman 2004), but an extended waveform of −0.6 to +1.4 V and back was used to monitor small changes in dopamine associated with operant responding for cocaine (Phillips et al. 2003b; Stuber, Wightman, and Carelli 2005, Stuber et al. 2005). Currently, our lab uses an extended waveform of −0.4 to +1.3 V and back, which yields almost as much sensitivity as the −0.6 to +1.4 V waveform but with better time resolution and selectivity (Cheer et al. 2004; Heien et al. 2005).

Note that the extended waveforms require longer for the electrode to stabilize after initial insertion into brain tissue. Although the reason for this is unknown, it is likely due to adaptations at the carbon surface. After the electrode is inserted into the brain, we typically apply the waveform at 60 Hz for 10–15 m and then apply it at 10 Hz (the typical frequency of data collection) for another 5 m before optimizing the electrode placement for recording.

Other Considerations

Files for behavioral experiments are typically collected in sequence and are limited to 90 s or less to minimize electrode drift during dopamine targeting (Heien et al. 2005). The length of the total experiment is kept as short as possible (usually about 60 min) to guard against electrode drift, tissue damage at the electrode site due to the rat hitting the manipulator or excessive movement, or changes in sensitivity over time. The experimenter can check these possibilities by monitoring the signal-to-noise ratios of electrically stimulated release before and after the experiment.

Data Analysis

Targeting Dopamine Transients

With thousands of electrochemical scans per experiment, dopamine transients are targeted using objective, high-throughput algorithms. One method is the “CV match” algorithm written into the Tar Heel CV software (Robinson et al. 2003). This automatically determines the correlation of each background-subtracted voltammogram with a dopamine cyclic-voltammogram template. The template voltammogram is taken from an electrically stimulated dopamine signal in the same rat, preferably at the same recording site. The background to be subtracted is specified as a certain number of scans (typically 10 scans or 1 s) and distance from the cyclic voltammogram under analysis; this is a moving background, as it is always relative to the target voltammogram. The CV match is typically run with 3–4 backgrounds (e.g., +25, +15, +10, and −5 scans from the target voltammogram). Alternatively, the program can be modified to use a single background for subtraction from the whole data file. The correlations are then displayed in graphic form, with scan number on the x-axis and r or r2 on the y-axis. The program can generate text files of the r or r2 values and the corresponding scans that exceed a threshold specified by the user. We generally use a threshold of r2 = 0.75 or r = 0.866; this cutoff was determined after calculating the correlation coefficient of dopamine with interfering species, such as DOPAC, serotonin, and 5-HIAA (Heien et al. 2003). Earlier versions of the software also calculated the inverse mean squared error, which yielded a larger number as the variation between the target and the template decreased. The CV match algorithm is a very conservative method, as only clear dopamine cyclic voltammograms with no overlapping pH signal or other interfering species will be targeted.

A second, more thorough method to identify dopamine transients is the “principal component regression” algorithm written into the Tar Heel CV software (explained in more detail in Heien, Johnson, and Wightman 2004). In brief, this algorithm uses principal component clustering to separate changes in current due to dopamine from noise or pH shifts. For this calculation, a training set is generated with cyclic voltammograms of dopamine, pH changes, and any other identifiable contributor, such as electrode drift. Typically, five voltammograms of each compound or factor are used, each illustrating different concentrations or amplitudes. The dopamine and pH signals are taken from electrically stimulated dopamine release and subsequent pH shifts, and the dopamine signals are converted to concentration using the in vitro electrode calibration. The training set is used to calculate eigenvalues that are employed to analyze experimental recordings by a principal component regression. The result is a concentration versus time plot for dopamine, as well as for pH shifts and any other factor used in the training set. To test the goodness of fit, the data can be reproduced with the regression values, then compared to the original data. The residual (Q) is calculated as the least-squared difference between the two data sets. The peaks in the dopamine concentration versus time plot that surpass the noise threshold can then be analyzed for quantity, amplitude, and duration by hand or using commercially available software such as MiniAnalysis 6.0.3. (Synaptosoft, Inc.).

The advantage of the principal component analysis is that it uses more information to identify dopamine and can separate overlapping contributions of dopamine and pH, resulting in greater signal-to-noise ratios. An additional advantage of the principal component analysis is that slower (<90 s) changes in dopamine concentrations can be resolved, as well as dopamine transients. These slower changes are often masked by pH changes (Heien et al. 2005) and would not be detected using the CV match algorithm. However, the analysis is only as good as the training set, so several low-noise, electrically stimulated dopamine signals and pH shifts of varying amplitudes are required. Another considering factor is electrical noise or the presence of a compound not in the training set. This possibility is monitored with the residual Q: when Q exceeds the 95% confidence interval of the data, the principal component analysis is invalid at those time points.

With either analysis method, it is important to remember that dopamine cannot be electrochemically differentiated from norepinephrine. Thus, transients measured in brain regions with substantial norepinephrine input, such as the prefrontal cortex, should be termed catecholamine transients, even when electrically stimulated dopamine is used in the training set.

Measuring Amplitude and Duration

Once dopamine transients are identified, the attributes of amplitude and duration can be measured. In early studies, researchers measured these aspects manually by selecting an appropriate background for each transient and then examining scan-by-scan to determine the peak amplitude ([DA]max) and duration. The background could be selected from the CV match algorithm, e.g., when multiple backgrounds were run, the background yielding the best r2 value can be used, then the amplitude of the peak current could be measured. However, as sensitivity improved and the number of transients detected in an experiment increased, this approach became very timeconsuming. When principal component analysis (PCA) is used, the resulting concentration versus time plot can be analyzed with a peak-analysis program such as MiniAnalysis to yield [DA]max, half-width, and area under the peak.

Shifts in pH

Electrically stimulated dopamine signals are often followed by an alkaline change in pH (Runnels et al. 1999). These pH shifts have been verified using ion-selective electrodes and shown to be timelocked to changes in oxygen concentration (Venton, Michael, and Wightman 2003). Moreover, they can be modulated in concert with oxygen by compounds that are vasoactive, indicating that they reflect blood-flow changes (Venton, Michael, and Wightman 2003). Indeed, pH shifts are not observed at every electrode placement, but wax and wane as the electrode is lowered, consistent with the concept that they are dependent upon proximity to a blood vessel. The shape of the pH voltammogram varies according to the applied waveform (e.g., compare Runnels et al. 1999 with Heien et al. 2003), but in each case the changes in current can overlap with those of dopamine oxidation. The pH contribution at the oxidation potential of electrically evoked dopamine can be mathematically subtracted as described by Venton, Michael, and Wightman (2003), but is much more readily handled by principal component regression.

Spontaneous changes in pH shifts have also been observed in freely moving animals following dopamine transients (Robinson D. L. and Wightman R. M., unpublished observations). A subset of recordings (n = 5, −0.4 to +1.0 V applied waveform) from a published paper (Robinson and Wightman 2004) contained robust pH shifts after electrical stimulations in the ventral striatum. Templates of the pH signals were used to find spontaneous pH fluctuations using the CV match algorithm. In these recording sites, 53% of the spontaneous dopamine transients were followed within 5 s by a basic shift in pH. Moreover, using principal component analysis, Heien et al. (2005) revealed acidic pH shifts accompanying tonic increases in dopamine following acute cocaine infusion. Inasmuch as pH changes reflect blood flow and neuronal activity (Venton, Michael, and Wightman 2003), the study of pH signals might provide insight into postsynaptic activity and functional effects of dopamine transients.

Statistical Analysis

Many aspects of spontaneous dopamine transient data are nonlinear and have inhomogeneous variance, making a traditional ANOVA inappropriate. Instead, we have often used analyses that take into account nonhomogenous population variances and nonlinear association. For example, the frequency of dopamine transients is count data (the number of transients measured per unit time) and is bounded by zero (i.e., no transients detected during a sample), so analyses for data with Poisson distributions are used. Likewise, because the amplitudes of dopamine transients are bounded by and skewed toward the detection limit of the instrumentation, we use analyses for data with gamma distributions.

We construct statistical models of the FSCV data using the generalized linear model, or “genmod” command, in SAS software (SAS Institute Inc., Cary, NC). Unlike traditional ANOVA models that use least squares regression, this analysis uses maximum likelihood. The main advantage of the SAS generalized linear model for voltammetric data is that the regression can be tailored to various distributions, by using a linear, logistic, Poisson, or gamma regression. Added advantages of the SAS generalized linear model are that it can calculate a repeated-measures analysis without having an equal n in each group and it allows you to use the complete data for subjects when a datum is missing (i.e., unit of observation is subject-time). Post-hoc comparisons following the SAS generalized linear model are made with Wald-type statistics that are analogous to t-tests and f-tests for regression comparisons.

Verification of Recording Site

As the diameter of the carbon fiber is less than 10 μm, it is impossible to detect the recording site with light microscopy. However, two approaches have been used to determine the recording site. The first is to estimate the position by examination of damage left by the guide cannula and dorsal electrode tract, where the diameter of the glass capillary was larger (Robinson, Heien, and Wightman 2002). The depth of the electrode is known by documenting the number of turns made on the micromanipulator; each turn equals 300 μm. The recording site is then estimated using the known depth at the trajectory indicated by the observable damage. A second method is to lesion the site while the rat is anesthetized (Phillips et al. 2003a). A stainless steel electrode is inserted via the guide cannula to the same depth as the carbon-fiber electrode. An electrolytic lesion is made at the electrode tip by delivering a current (100 μA, 5 s) that is visualized in the tissue section with light microscopy. Note that lesions can be made directly with the carbon fiber (Rebec et al. 1997), but doing so renders the electrode unusable for post-experiment calibration.

Naturally Occurring Dopamine Transients

Several papers in the past few years have documented naturally occurring dopamine transients in freely-moving rats in a variety of behavioral and pharmacological experiments. These data will be discussed below.

Environmental Stimuli

The first measurements of dopamine concentration transients were at the presentation of salient stimuli to the rat, using the traditional waveform of −0.4 to +1.0 V. Rebec et al. (1997) recorded dopamine transients in the NAc shell of rats at entry into a novel environment. The transients were sharp increases in current at the oxidation potential of dopamine, with amplitudes of 2.2±0.7 nA and durations of 8±1 s. The signals were not detected in the NAc core or the CP, although blunted signals (1.4±0.4 nA amplitude, 24±2 s duration) were observed in the core-shell boundary of the NAc. Moreover, no subsequent transients were observed during the exploratory phase following entry to the novel environment. The concentration of dopamine was unknown, as the electrodes could not be post-calibrated to convert the electrochemical current into concentration. Nevertheless, these data were important not only as the first report of spontaneous dopamine transients, but also as the first report of regional variation in phasic dopamine signaling.

Subsequently, dopamine transients were measured in the NAc core of male rats at the presentation of a sexually receptive female and during subsequent interaction (Robinson et al. 2001). Dopamine transients were observed at the presentation of the female, as well as during subsequent appetitive sexual behavior, such as approach and sniffing. The transients were fast (0.2–0.9 s) and reached concentrations of 200–500 nM dopamine. Nomifensine amplified the amplitude and duration of the dopamine transients and increased the frequency of detection, thereby providing pharmacological evidence that the signals were indeed dopaminergic.

Subsequent studies extended these findings in several important ways. First, transients occurred in the CP, NAc shell and OT, as well as the NAc core both at a baseline rate and at the presentation of stimuli (Robinson, Heien, and Wightman 2002; Robinson and Wightman 2004). Second, the presentation of nonreceptive female and male rats were also effective at triggering dopamine transients (Robinson, Heien, and Wightman 2002), as were novel odors, unexpected noises, and food treats (Robinson and Wightman 2004). Finally, the effectiveness of these stimuli to trigger dopamine transients waned with repeated presentation, indicating that the neurochemical response habituated (Robinson, Heien, and Wightman 2002).

Basal Conditions

While the initial studies used environmental stimuli to trigger rapid dopamine release (Rebec et al. 1997; Robinson et al. 2001), later studies included basal measurements of transients (Table 2.1). Using an applied waveform of −0.4 to +1.0 V, Robinson, Heien, and Wightman (2002) described average basal frequencies of approximately 0.2 per min in the NAc and OT, which were significantly higher than the frequency of 0.04 per min detected in the CP. In a subsequent study, Robinson and Wightman (2004) reported frequencies of 0.5 per min in the NAc and 0.7 per min in the OT, which were not statistically different. Moreover, the amplitude and duration of transients did not vary among brain regions in either study (Table 2.1). However, the detection limits of these studies were 60 nM (Robinson, Heien, and Wightman 2002) and 40 nM (Robinson and Wightman 2004); these regional measurements should be replicated now that detection limits have improved.

Later studies used waveforms that were extended in the anodic and/or cathodic direction and detected more small dopamine transients (<50 nM), resulting in higher basal frequencies. Cheer et al. (2004) measured approximately 1.5 transients per min in the NAc during basal conditions using an applied waveform of −0.4 to +1.3 V, whereas Stuber et al. (2005) measured 1 transient per min in the NAc during saline infusions using the −0.6 to +1.4 V applied waveform. The detection limits in these studies were approximately 13 nM (Cheer et al. 2004) and approximately 18 nM (Stuber et al. 2005), and the mean amplitude of dopamine transients was approximately 50 nM, suggesting that many transients were undetected in earlier studies using the −0.4 to +1.0 V waveform.

All of these studies found a large range of transient frequency—from recording sites where no transients were detected to sites where more than 5 transients per min were measured (Table 2.1). The differential propensity to measure spontaneous dopamine transients was not due simply to a lack of dopamine release sites near the carbon-fiber electrode because dopamine release was evoked at each site with electrical stimulation of the dopamine cell bodies or fibers. Rather, the phenomenon of “hot” and “cold” sites for dopamine transients may reflect terminal regions of dopamine neurons that have higher versus lower rates of firing in bursts or in synchrony with other neurons (Freeman, Meltzer, and Bunney 1985; Freeman and Bunney 1987; Hyland et al. 2002).

Pharmacology

Many studies have used FSCV to measure the presynaptic effects of drugs on dopamine transmission— specifically, effects on dopamine release and uptake after electrical stimulation of the dopamine neurons. For example, dopamine transporter antagonists increase the magnitude of electrically evoked dopamine signals by slowing dopamine uptake as measured in slices, anesthetized animals, and awake animals (Garris and Wightman 1995; Giros et al. 1996; Garris et al. 2003). To determine whether nomifensine has similar results on the kinetics of spontaneous dopamine transients, Robinson and Wightman (2004) examined the effects of nomifensine (7 mg/kg, i.p.) in the ventral striatum of awake rats. Nomifensine amplified [DA]max of dopamine transients by 10% and duration by 50%, consistent with the previous work on electrically stimulated dopamine signals. Likewise, Stuber et al. (2005) compared the amplitude of dopamine transients during saline versus cocaine infusion (4 i.v. infusions of 0.33 mg, 5 min apart) in drug-naïve rats and found approximately a twofold increase in [DA]max after cocaine infusion. Interestingly, cocaine did not affect transient amplitude in rats with a history of cocaine self-administration.

Administration of cocaine or nomifensine also increased the frequency of detected dopamine transients. Overall, dopamine transients were approximately eight times more frequent during cocaine infusions as compared to saline infusions in drug-naïve rats, and three times more frequent in rats with a history of cocaine self-administration; similar increases were seen in rats self-administering cocaine (Stuber et al. 2005). When analyzed minute-by-minute, the rate of dopamine transients in the drug-naïve rats was well correlated with theoretical brain concentrations of cocaine (Pan, Menacherry, and Justice 1991). Nomifensine also increased the frequency of detected dopamine transients, by approximately sevenfold in the NAc and approximately twofold in the OT (Robinson and Wightman 2004). It is unclear whether the increase in transients reflects an amplification of transients previously below the detection limit or a change in firing rate of the dopamine neurons. In either case, the practical effect is an increase in the number of dopamine signals reaching extrasynaptic dopamine receptors.

The effects of acute intravenous cocaine administration on dopamine release and pH changes have recently been examined in more detail by Heien et al. (2005). Using principal component analysis, they distinguished three components to the electrochemical signal in the 90 s following cocaine infusion: dopamine transients, a tonic increase in dopamine, and an acidic pH shift. Transients were evident immediately after the cocaine infusion, then increased in amplitude and duration as cocaine was distributed to the brain. However, these transients were on top of a tonic increase in dopamine concentration that reached approximately 700 nM after 3 mg/kg cocaine, reaching half-maximal concentration at approximately 30 s. The amplitude of the tonic signal was dose-dependent, but the time course was similar at all doses tested (0.3–3 mg/kg, i.v.). An acidic shift in pH followed the same time course as the tonic dopamine signal, likely due to the vasoconstrictive effects of cocaine as it was distributed to brain tissue. These data emphasize the fact that multiple compounds can contribute to the electrochemical signal, so it is crucial to use techniques that allow differentiation. Moreover, these data provide evidence that the tonic dopamine signal is not simply a composite of dopamine transients over time, but a separate component of dopamine transmission.

Cheer et al. (2004) investigated the effects of WIN55,212-2, a cannabinoid CB1 receptor agonist, on rapid dopamine signaling. After administration of WIN55,212-2 (125 μg/kg, i.v.), the rate of dopamine transients increased almost threefold, while [DA]max of the transients increased twofold, as compared to the pre-drug values. Moreover, the effect of WIN55,212-2 on transient frequency was dose-dependent and reversed by administration of the CB1 receptor antagonist SR141716A. These findings strengthen the hypothesis that transients arise from burst firing of dopamine neurons, as WIN55,212-2 increases both the number of spikes in a burst and the frequency of burst firing of dopamine neurons (Cheer et al. 2003).

Self-Administration of Reinforcers

In addition to the pharmacological effects of cocaine, dopamine transients can be triggered by the cues associated with cocaine self-administration and may contribute to the maintenance of self administration behavior. FSCV, using the −0.6 to +1.4 V extended waveform, was employed to measure phasic dopamine fluctuations in rats during operant responding for cocaine (Phillips et al. 2003b). In this behavioral paradigm, each lever-press triggered an infusion pump that delivered cocaine (0.33 mg, i.v.) and simultaneously triggered a 20 s light/tone cue that signaled the infusion. Dopamine transients occurred immediately following the lever-press at the initiation of the light/tone cue. In fact, the unexpected presentation of the light/tone cue in the absence of a lever-press also elicited a dopamine transient in trained animals, but not naïve animals, indicating that the learned association between the cue and the cocaine infusion was an important component in the dopaminergic response.

In addition, dopamine transients were observed in the seconds preceding the lever-press, although the exact timing varied from trial to trial. These dopamine signals accompanied breaks in the stereotypical movements associated with cocaine’s psychostimulant effects and approach to the lever. Indeed, inducing a dopamine transient with electrical stimulation of the dopamine fibers often triggered a break in stereotypy and a lever-press, suggesting that dopamine transients contribute to the maintenance of drug-taking behavior. Similarly, electrical stimulation of the medial forebrain bundle/lateral hypothalamus has been shown to trigger behavior dependent on the environmental context (Valenstein 1969). These data also support the hypothesis that phasic dopamine signals facilitate behavioral switching (Redgrave, Prescott, and Gurney 1999) and approach responses (Ikemoto and Panksepp 1999).

Subsequent studies used within-session extinction and reinstatement to provide further evidence that the transients preceding the lever-press were associated with lever approach, whereas the transients immediately following the lever-press were associated with learning about the cues (Stuber, Wightman, and Carelli 2005). After 30 min of responding for cocaine, saline was substituted so that the light/tone cue no longer predicted cocaine administration; rats pressed the lever approximately 20 times before the behavior was extinguished. When responding stopped for at least 30 min, cocaine replaced the saline and the rats were given a noncontingent infusion of cocaine to reinstate responding. Analysis of dopamine signals during the extinction phase revealed that the pre-response transients remained stable during extinction, but post-response dopamine transients decreased in amplitude even during the first ten presses, and were often absent during the last five presses. When cocaine pressing was reinstated, however, the post-response dopamine signals returned to pre-extinction amplitudes.

Similar dopamine fluctuations were observed during operant responding for sucrose (Roitman et al. 2004). In this study, a cue onset predicted availability of a sucrose-reinforced lever, and triggered a dopamine transient in the NAc within 1 s. The same cue elicited no dopaminergic response in another group of rats, for which the cue did not predict availability of the lever. The rats pressed the lever at the peak of the dopamine signal, with a mean of 5.5 s after the onset of the cue. In contrast, when the sucrose was delivered (0.3 M, 200 μL, intraoral administration) and consumed, no further dopamine transients were recorded. These data link phasic dopamine fluctuations with associative and appetitive aspects of sucrose reinforcement, rather than consummatory aspects.

Some of the first measurements of electrically stimulated dopamine signals in awake rats were made while the rats self-administered the electrical stimulations to the ventral tegmental area and substantia nigra, or intracranial self-stimulation (Garris et al. 1999; Kilpatrick et al. 2000). When the electrical stimulations (60 Hz, 24 p, biphasic, 80–250 μA) were delivered singly to the rats by the experimenter, reproducible dopamine transients resulted (i.e., electrically evoked release). However, when the rats self-administered trains of these stimulations during continuous reinforcement (FR1 schedule), dopamine release was measured at the beginning of the session then quickly dropped to undetectable levels. These results were replicated using chronoamperometry during ICS in mice at an FR1 schedule (Yavich and Tiihonen 2000). In contrast, when Yavich and Tiihonen (2000) increased the response requirement to FR8, which spaced the stimulations out about 5 s apart, they measured robust electrochemical signals. Indeed, previous impulse flow can determine the amount of dopamine release from subsequent stimulations; this can be modeled with facilitation and depression factors (Montague et al. 2004) and may be largely dependent on readily releasable pools of dopamine (Yavich and MacDonald 2000). It is yet unknown whether pre-response dopamine signals, such as those seen during cocaine and sucrose self-administration, would be observed during intracranial self-stimulation.

Function of Dopamine Transients

Current theory of dopamine function is that it is a neuromodulator positioned to influence afferent excitatory input onto medium spiny neurons (Nicola, Surmeier, and Malenka 2000). Tonic dopamine appears to have a gating function, enabling various behaviors. Phasic dopamine, on the other hand, may have a distinct function, such as attention and behavior switching (Ikemoto and Panksepp 1999; Redgrave, Prescott, and Gurney 1999) or reward-associated learning (Schultz 2002). Considering the high concentrations that can be achieved via dopamine transients and their precise timing, phasic dopamine signals are positioned to modulate highly specific inputs to the target cells. Furthermore, although dopamine transients in response to salient, unexpected stimuli appear to be simultaneous across the dorsal and ventral striatum (Robinson, Heien, and Wightman 2002; Robinson and Wightman 2004), the baseline rates of dopamine transients varies widely, both across the striatum and within specific nuclei (Table 2.1).

To address these functionality issues, methods are under development via collaboration between Mark Wightman and Regina Carelli to combine electrochemical and electrophysiological measurements (Williams and Millar 1990a, 1990b) in freely moving rats (Cheer et al. 2005). This combined technique will allow the simultaneous measurements of dopamine transients and cell firing at the carbon-fiber electrode, providing real-time associations between dopamine release and postsynaptic firing rates. The addition of iontophoresis of pharmacological agents will allow testing of causal relationships between the two components.

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