A permissive role for dopamine in the production of vigorous movements
Bibliographic record
Abstract
Abstract Dopamine is essential for the production of vigorous movements, but how dopamine modifies the gain of motor commands remains unclear. Here, we developed a dexterous motor task in which head-restrained mice self-initiate fast and large-amplitude lever pushes with their left forelimb to earn rewards. We show that this task is goal-directed and depends on cortico-striatal circuits in the hemisphere contralateral to the limb used to push the lever. We find that unilateral loss of midbrain dopamine neurons reduces the speed and amplitude of lever pushes, and that levodopa treatment rapidly restores motor vigor, consistent with parkinsonian bradykinesia. Photometry recordings of striatal dopamine levels indicate that the therapeutic efficacy of levodopa does not require phasic dopamine release. In dopamine-intact mice, optogenetic stimulation of midbrain dopamine neurons calibrated to mimic transients evoked by rewards is also insufficient to increase the speed and amplitude of forelimb movements. Together, our data show that phasic dopamine transients are unlikely to specify the vigor of forelimb movements online as they are being executed, and suggest instead that dopamine plays a permissive role in the selection and production of vigorous movements. Our findings have important implications for our understanding of how the basal ganglia contribute to motor control under physiological conditions and 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 | 0.000 | 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 | 0.001 |
| 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".