Dopamine release in response to gambling: a fMRI study
Bibliographic record
Abstract
Reward-related cues can potently influence behaviour. In addicted individuals, exposure to contextual cues – e.g. drug paraphernalia – is believed to trigger cravings, drug use and relapse. In light of these powerful effects, it is important to understand the mechanisms whereby cue exposure translates into the series of actions required to attain these sometimes harmful outcomes. One candidate mechanism is the influence of cues on decision processes, which may in turn lead to maladaptive choice of the addictive behaviour. The possibility of such effects remains largely unexplored. However, our work in rats and in humans suggests that introducing sensory reward cues - similar to the lights and sounds in gambling settings - results in riskier choice. While in rats the risk-promoting effects of cues are mediated by dopamine (DA) signalling, the neural mechanisms of this effect in humans are unclear. Whether cue-induced risky choice contributes to addictive behaviour, such as “problem gambling”, is also currently unknown. Our ultimate objective is to interrogate the contribution of dopamine (DA) signalling to increased risk-proneness resulting from rewards with salient sensory features, such as casino "bells and whistles". Our earlier human and animal research shows that such sensory cues promote risk on a gambling task, and this effect is DA-dependent in rats. We have been funded by CIHR to test the contributions of DA in humans, both in healthy volunteers and in individuals with gambling problems who may be especially vulnerable to the effects of sensory cues. The aim of this study is to examine this effect of salient sensory cues paired with rewards in fMRI in healthy volunteers to develop an analysis strategy for a PET-fMRI study, and to assess novel neuromelanin-sensitive MRI measures to quantify individual differences on DA function in the context of gambling.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".