Dopamine replacement remediates risk aversion in Parkinson’s disease in a value-independent manner
Bibliographic record
Abstract
ABSTRACT Introduction Clinical evidence suggests that Parkinson’s Disease (PD) patients are risk averse. Dopaminergic therapy has been reported to increase risk tolerance, but the underlying mechanisms are unclear. Some studies have suggested an amplification of subjective reward value, consistent with the role of dopamine in reward value coding. Others have reported value-independent risk enhancement. We evaluated the value-dependence of the effects of PD and its therapy on risk using tasks designed to sensitively measure risk over a wide range of expected values. Method 36 patients with idiopathic PD receiving levodopa monotherapy and 36 healthy matched controls performed two behavioural economic tasks aimed at quantifying 1) risk tolerance/ aversion in the gain frame and 2) valuation of potential gains relative to losses. PD patients performed the tasks on and off their usual dose of levodopa in randomized order; controls performed the same tasks twice. Results Relative to the controls, unmedicated PD patients showed significant value-independent risk aversion in the gain frame, which was normalized by levodopa. PD patients did not differ from controls in their valuation of gains relative to losses. However, across both tasks and regardless of medication, choices of the patients were more determined by expected values of the prospects than those of controls. Conclusion Dopamine deficiency in PD was associated with risk aversion, and levodopa promoted riskier choice in a value-independent manner. PD patients also showed an increased sensitivity to expected value, which was independent of levodopa and does not appear to result directly from dopamine deficiency.
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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".