34 research outputs found
Designer receptors show role for ventral pallidum input to ventral tegmental area in cocaine seeking.
The ventral pallidum is centrally positioned within mesocorticolimbic reward circuits, and its dense projection to the ventral tegmental area (VTA) regulates neuronal activity there. However, the ventral pallidum is a heterogeneous structure, and how this complexity affects its role within wider reward circuits is unclear. We found that projections to VTA from the rostral ventral pallidum (RVP), but not the caudal ventral pallidum (CVP), were robustly Fos activated during cue-induced reinstatement of cocaine seeking--a rat model of relapse in addiction. Moreover, designer receptor-mediated transient inactivation of RVP neurons, their terminals in VTA or functional connectivity between RVP and VTA dopamine neurons blocked the ability of drug-associated cues (but not a cocaine prime) to reinstate cocaine seeking. In contrast, CVP neuronal inhibition blocked cocaine-primed, but not cue-induced, reinstatement. This double dissociation in ventral pallidum subregional roles in drug seeking is likely to be important for understanding the mesocorticolimbic circuits underlying reward seeking and addiction
Deep Brain Stimulation of Nucleus Accumbens Region in Alcoholism Affects Reward Processing
The influence of bilateral deep brain stimulation (DBS) of the nucleus nucleus (NAcc) on the processing of reward in a gambling paradigm was investigated using H2[15O]-PET (positron emission tomography) in a 38-year-old man treated for severe alcohol addiction. Behavioral data analysis revealed a less risky, more careful choice behavior under active DBS compared to DBS switched off. PET showed win- and loss-related activations in the paracingulate cortex, temporal poles, precuneus and hippocampus under active DBS, brain areas that have been implicated in action monitoring and behavioral control. Except for the temporal pole these activations were not seen when DBS was deactivated. These findings suggest that DBS of the NAcc may act partially by improving behavioral control
Ventral pallidum encodes relative reward value earlier and more robustly than nucleus accumbens
A Neural Computational Model of Incentive Salience
Incentive salience is a motivational property with ‘magnet-like’ qualities. When attributed to reward-predicting stimuli (cues), incentive salience triggers a pulse of ‘wanting’ and an individual is pulled toward the cues and reward. A key computational question is how incentive salience is generated during a cue re-encounter, which combines both learning and the state of limbic brain mechanisms. Learning processes, such as temporal-difference models, provide one way for stimuli to acquire cached predictive values of rewards. However, empirical data show that subsequent incentive values are also modulated on the fly by dynamic fluctuation in physiological states, altering cached values in ways requiring additional motivation mechanisms. Dynamic modulation of incentive salience for a Pavlovian conditioned stimulus (CS or cue) occurs during certain states, without necessarily requiring (re)learning about the cue. In some cases, dynamic modulation of cue value occurs during states that are quite novel, never having been experienced before, and even prior to experience of the associated unconditioned reward in the new state. Such cases can include novel drug-induced mesolimbic activation and addictive incentive-sensitization, as well as natural appetite states such as salt appetite. Dynamic enhancement specifically raises the incentive salience of an appropriate CS, without necessarily changing that of other CSs. Here we suggest a new computational model that modulates incentive salience by integrating changing physiological states with prior learning. We support the model with behavioral and neurobiological data from empirical tests that demonstrate dynamic elevations in cue-triggered motivation (involving natural salt appetite, and drug-induced intoxication and sensitization). Our data call for a dynamic model of incentive salience, such as presented here. Computational models can adequately capture fluctuations in cue-triggered ‘wanting’ only by incorporating modulation of previously learned values by natural appetite and addiction-related states.United States. Air Force Office of Scientific ResearchUnited States. National Institutes of Health (DA015188, DA017752 and MH63649