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      RASGRF2 regulates alcohol-induced reinforcement by influencing mesolimbic dopamine neuron activity and dopamine release.

      Proceedings of the National Academy of Sciences of the United States of America
      Adolescent, Animals, Brain, metabolism, Calcium, Child, Dopamine, Dopaminergic Neurons, Electrophysiology, methods, Ethanol, pharmacology, Extracellular Signal-Regulated MAP Kinases, Genotype, Haplotypes, Humans, Male, Mice, Mice, Transgenic, Neurons, RNA, Messenger, Reinforcement (Psychology), Time Factors, Ventral Tegmental Area, ras Guanine Nucleotide Exchange Factors, genetics, physiology

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          Abstract

          The firing of mesolimbic dopamine neurons is important for drug-induced reinforcement, although underlying genetic factors remain poorly understood. In a recent genome-wide association metaanalysis of alcohol intake, we identified a suggestive association of SNP rs26907 in the ras-specific guanine-nucleotide releasing factor 2 (RASGRF2) gene, encoding a protein that mediates Ca(2+)-dependent activation of the ERK pathway. We performed functional characterization of this gene in relation to alcohol-related phenotypes and mesolimbic dopamine function in both mice and adolescent humans. Ethanol intake and preference were decreased in Rasgrf2(-/-) mice relative to WT controls. Accordingly, ethanol-induced dopamine release in the ventral striatum was blunted in Rasgrf2(-/-) mice. Recording of dopamine neurons in the ventral tegmental area revealed reduced excitability in the absence of Ras-GRF2, likely because of lack of inhibition of the I(A) potassium current by ERK. This deficit provided an explanation for the altered dopamine release, presumably linked to impaired activation of dopamine neurons firing. Functional neuroimaging analysis of a monetary incentive-delay task in 663 adolescent boys revealed significant association of ventral striatal activity during reward anticipation with a RASGRF2 haplotype containing rs26907, the SNP associated with alcohol intake in our previous metaanalysis. This finding suggests a link between the RASGRF2 haplotype and reward sensitivity, a known risk factor for alcohol and drug addiction. Indeed, follow-up of these same boys at age 16 y revealed an association between this haplotype and number of drinking episodes. Together, these combined animal and human data indicate a role for RASGRF2 in the regulation of mesolimbic dopamine neuron activity, reward response, and alcohol use and abuse.

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          FMRI visualization of brain activity during a monetary incentive delay task.

          Comparative studies have implicated striatal and mesial forebrain circuitry in the generation of autonomic, endocrine, and behavioral responses for incentives. Using blood oxygen level-dependent functional magnetic resonance imaging, we sought to visualize functional activation of these regions in 12 normal volunteers as they anticipated and responded for monetary incentives. Both individual and group analyses of time-series data revealed significant activation of striatal and mesial forebrain structures (including insula, caudate, putamen, and mesial prefrontal cortex) during trials involving both monetary rewards and punishments. In addition to these areas, during trials involving punishment, group analysis revealed activation foci in the anterior cingulate and thalamus. These results corroborate comparative studies which implicate striatal and mesial forebrain circuitry in the elaboration of incentive-driven behavior. This report also introduces a new paradigm for probing the functional integrity of this circuitry in humans.
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            Drugs abused by humans preferentially increase synaptic dopamine concentrations in the mesolimbic system of freely moving rats.

            The effect of various drugs on the extracellular concentration of dopamine in two terminal dopaminergic areas, the nucleus accumbens septi (a limbic area) and the dorsal caudate nucleus (a subcortical motor area), was studied in freely moving rats by using brain dialysis. Drugs abused by humans (e.g., opiates, ethanol, nicotine, amphetamine, and cocaine) increased extracellular dopamine concentrations in both areas, but especially in the accumbens, and elicited hypermotility at low doses. On the other hand, drugs with aversive properties (e.g., agonists of kappa opioid receptors, U-50,488, tifluadom, and bremazocine) reduced dopamine release in the accumbens and in the caudate and elicited hypomotility. Haloperidol, a neuroleptic drug, increased extracellular dopamine concentrations, but this effect was not preferential for the accumbens and was associated with hypomotility and sedation. Drugs not abused by humans [e.g., imipramine (an antidepressant), atropine (an antimuscarinic drug), and diphenhydramine (an antihistamine)] failed to modify synaptic dopamine concentrations. These results provide biochemical evidence for the hypothesis that stimulation of dopamine transmission in the limbic system might be a fundamental property of drugs that are abused.
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              Dopamine neurons report an error in the temporal prediction of reward during learning.

              Many behaviors are affected by rewards, undergoing long-term changes when rewards are different than predicted but remaining unchanged when rewards occur exactly as predicted. The discrepancy between reward occurrence and reward prediction is termed an 'error in reward prediction'. Dopamine neurons in the substantia nigra and the ventral tegmental area are believed to be involved in reward-dependent behaviors. Consistent with this role, they are activated by rewards, and because they are activated more strongly by unpredicted than by predicted rewards they may play a role in learning. The present study investigated whether monkey dopamine neurons code an error in reward prediction during the course of learning. Dopamine neuron responses reflected the changes in reward prediction during individual learning episodes; dopamine neurons were activated by rewards during early trials, when errors were frequent and rewards unpredictable, but activation was progressively reduced as performance was consolidated and rewards became more predictable. These neurons were also activated when rewards occurred at unpredicted times and were depressed when rewards were omitted at the predicted times. Thus, dopamine neurons code errors in the prediction of both the occurrence and the time of rewards. In this respect, their responses resemble the teaching signals that have been employed in particularly efficient computational learning models.
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