Effects of levodopa on cognition in healthy volunteers: implications for Parkinson’s disease
Bibliographic record
Abstract
Background: Cognitive impairments are now recognized in Parkinson’s disease. Some of these deficits owe to disease pathology itself whereas others are due to paradoxical effects of dopaminergic medications, such as levodopa. The dopamine overdose hypothesis proposes that dissimilar effects of medication on cognition depend on baseline endogenous dopamine levels in underlying brain regions. We sought to directly test this prevalent theory. Methods: We tested healthy adults, who presumably have optimal endogenous dopamine levels, in two sessions. Participants received 100/25 mg of levodopa/carbidopa in one session and an equal volume of placebo in the other. During each session, participants completed a probabilistic reversal learning task. The number of trials to task completion was used as a behavioural proxy of learning performance. Results: A paired t-test covaried with drug-placebo order revealed that healthy adults learned more poorly on levodopa compared to placebo. Conclusions: Our findings suggest that baseline endogenous dopamine levels are a critical factor determining the effects of dopaminergic medications on cognition, independent of Parkinson’s disease pathology. Partitioning which cognitive functions are helped versus hindered by medication and improving our understanding of the underlying psychopharmacology of these effects is important for improving treatment strategies in Parkinson’s disease.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".