Dopamine neurons gate the intersection of cocaine use, decision making, and impulsivity
Bibliographic record
Abstract
Gambling and substance use disorders are highly comorbid. Both clinical populations are impulsive and exhibit risky decision-making. Drug-associated cues have long been known to facilitate habitual drug-seeking, and the salient audiovisual cues embedded within modern gambling products may likewise encourage problem gambling. The dopamine neurons of the ventral tegmental area (VTA) are exquisitely sensitive to drugs of abuse, uncertain rewards, and reward-paired cues and may therefore be the common neural substrate mediating synergistic features of both disorders. To test this hypothesis, we first gained specific inhibitory control over VTA dopamine neurons by transducing a floxed inhibitory DREADD (AAV5-hSyn-DIO-hM4D(Gi)-mCherry) in rats expressing Cre recombinase in tyrosine hydroxylase neurons. We then trained rats in our cued rat gambling task (crGT), inhibiting dopamine neurons throughout task acquisition and performance, before allowing them to self-administer cocaine in the same diurnal period as crGT sessions. The trajectories of addiction differ in women and men, and the dopamine system may differ functionally across the sexes; therefore, we used male and female rats here. We found that inhibition of VTA dopamine neurons decreased cue-induced risky choice and reduced motor impulsivity in males, but surprisingly, enhanced risky decision making in females. Inhibiting VTA dopamine neurons also prevented cocaine-induced changes in decision making in both sexes, but nevertheless drove all animals to consume more cocaine. These findings show that chronic dampening of dopamine signalling can have both protective and deleterious effects on addiction-relevant behaviours, depending on biological sex and dependent variable of interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".