Chronic D<sub>2/3</sub> agonist ropinirole treatment increases preference for uncertainty in rats regardless of baseline choice patterns
Bibliographic record
Abstract
Abstract D2/3 receptor agonists are effective treatments for Parkinson's disease (PD), but can precipitate impulse control disorders (ICDs) including gambling disorder (GD). The neurobiological mechanisms underlying this devastating side‐effect of dopamine agonist replacement therapy (DRT), and any dependence on the dopamine depletion caused by PD, are unclear. It is also unclear whether previous biases towards risk or uncertainty are a risk factor for developing these ICDs. We investigated whether chronic D2/3 agonist administration (5 mg/kg/day ropinirole for 28 days) altered performance of a rat model of gambling‐like behaviour, the rodent betting task (rBT), and examined if baseline behaviour predicted this behavioural change. The rBT captures individual differences in subjective preference for uncertain outcomes: animals choose between guaranteed or probabilistic reinforcement of equal expected value. Chronic ropinirole dramatically increased selection of the uncertain option in two‐thirds of animals, regardless of baseline preferences. The effect on choice in the rBT was replicated in a dorsolateral striatal 6‐hydroxydopamine (6‐OHDA) rat model of early PD. These studies are the first to look at individual differences in response to chronic, rather than pulsatile, dosing of DRT in a rodent model of gambling behaviour. These findings suggest that DRT‐induced PG may stem from increases in subjective valuation of uncertainty. Such symptoms likely arise because of changes in dopaminergic striatal signalling caused by DRT rather than from an interaction between pre‐morbid behaviours or PD itself.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".