Learning critically drives parkinsonian motor deficits through imbalanced striatal pathway recruitment
Bibliographic record
Abstract
Dopamine (DA) loss in Parkinson's disease (PD) causes debilitating motor deficits. However, dopamine is also widely linked to reward prediction and learning, and the contribution of dopamine-dependent learning to movements that are impaired in PD-which often do not lead to explicit rewards-is unclear. Here, we used two distinct motor tasks to dissociate dopamine's acute motoric effects vs. its long-lasting, learning-mediated effects. In dopamine-depleted mice, motor task performance gradually worsened with task exposure. Task experience was critical, as mice that remained in the home cage during the same period were relatively unimpaired when subsequently probed on the task. Repeated dopamine replacement treatments acutely rescued deficits and gradually induced long-term rescue that persisted despite treatment withdrawal. Surprisingly, both long-term rescue and parkinsonian performance decline were task specific, implicating dopamine-dependent learning. D1R activation potently induced acute rescue that gradually consolidated into long-term rescue. Conversely, reduced D2R activation potently induced parkinsonian decline. In dopamine-depleted mice, either D1R activation or D2R activation prevented parkinsonian decline, and both restored balanced activation of direct vs. indirect striatal pathways. These findings suggest that reinforcement and maintenance of movements-even movements not leading to explicit rewards-are fundamental functions of dopamine and provide potential mechanisms for the hitherto unexplained "long-duration response" by dopaminergic therapies in PD.
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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 | 0.001 |
| 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".