Mesolimbic dopamine-releasing neurons appear to be important in the brain reward system. One behavioural paradigm that supports this hypothesis is intracranial self-stimulation (ICS), during which animals repeatedly press a lever to stimulate their own dopamine-releasing neurons electrically. Here we study dopamine release from dopamine terminals in the nucleus accumbens core and shell in the brain by using rapid-responding voltammetric microsensors during electrical stimulation of dopamine cell bodies in the ventral tegmental area/substantia nigra brain regions. In rats in which stimulating electrode placement failed to elicit dopamine release in the nucleus accumbens, ICS behaviour was not learned. In contrast, ICS was acquired when stimulus trains evoked extracellular dopamine in either the core or the shell of the nucleus accumbens. In animals that could learn ICS, experimenter-delivered stimulation always elicited dopamine release. In contrast, extracellular dopamine was rarely observed during ICS itself. Thus, although activation of mesolimbic dopamine-releasing neurons seems to be a necessary condition for ICS, evoked dopamine release is actually diminished during ICS. Dopamine may therefore be a neural substrate for novelty or reward expectation rather than reward itself.
Activity changes in a large subset of midbrain dopamine neurons fulfill numerous assumptions of learning theory by encoding a prediction error between actual and predicted reward. This computational interpretation of dopaminergic spike activity invites the important question of how changes in spike rate are translated into changes in dopamine delivery at target neural structures. Using electrochemical detection of rapid dopamine release in the striatum of freely moving rats, we established that a single dynamic model can capture all the measured fluctuations in dopamine delivery. This model revealed three independent short-term adaptive processes acting to control dopamine release. These short-term components generalized well across animals and stimulation patterns and were preserved under anesthesia. The model has implications for the dynamic filtering interposed between changes in spike production and forebrain dopamine release.
Described is an improved data acquisition system for fast-scan cyclic voltammetry (FSCV). The system was designed to significantly diminish noise sources that were identified in previously recorded FSCV measurements for the detection of neurotransmitters. Minimized noise is necessary to observe the low concentrations of neurotransmitters that are physiologically important. The system was based on a high-speed, 16-bit AD/DA acquisition board that allowed high scan rates and better resolved the small faradaic currents which remained after background subtraction. Irregularities that occur when independent timing sources are used for generation of the voltage waveform and collection of the current can create large noise artifacts near the voltage limits during FSCV. These were eliminated by the use of a single acquisition board that generated the voltage waveform and collected the current. Noise from frequency drift of the power line was eliminated through the use of a phase-locked loop. To demonstrate the improved performance of the system, data were collected using carbon-fiber microelectrodes in a flow injection analysis system and in brain slices. This new data acquisition system performed significantly better than another system previously used in our laboratory without these features. The improved detection limits of the new system allowed clearly resolved current spikes featuring pre-release "feet" to be recorded adjacent to individual mast cells following chemical stimulation. When combined with false-color plots, the low-noise system facilitated identification of dopamine release in a freely moving animal.
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