Increased striatal dopamine release in Parkinsonian patients with pathological gambling: a [11C] raclopride PET study
Bibliographic record
Abstract
Pathological gambling is an impulse control disorder reported in association with dopamine agonists used to treat Parkinson's disease. Although impulse control disorders are conceptualized as lying within the spectrum of addictions, little neurobiological evidence exists to support this belief. Functional imaging studies have consistently demonstrated abnormalities of dopaminergic function in patients with drug addictions, but to date no study has specifically evaluated dopaminergic function in Parkinson's disease patients with impulse control disorders. We describe results of a [(11)C] raclopride positron emission tomography (PET) study comparing dopaminergic function during gambling in Parkinson's disease patients, with and without pathological gambling, following dopamine agonists. Patients with pathological gambling demonstrated greater decreases in binding potential in the ventral striatum during gambling (13.9%) than control patients (8.1%), likely reflecting greater dopaminergic release. Ventral striatal bindings at baseline during control task were also lower in patients with pathological gambling. Although prior imaging studies suggest that abnormality in dopaminergic binding and dopamine release may be markers of vulnerability to addiction, this study presents the first evidence of these phenomena in pathological gambling. The emergence of pathological gambling in a number of Parkinson's disease patients may provide a model into the pathophysiology of this disorder.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".