Propranolol reduces Parkinson’s tremor and inhibits tremor-related activity in the motor cortex: a placebo-controlled crossover trial
Bibliographic record
Abstract
Abstract Objective Parkinson’s disease (PD) resting tremor is thought to be initiated in the basal ganglia and amplified in the cerebello-thalamo-cortical circuit. Since stress worsens tremor, the noradrenergic system may play a role in amplifying tremor. We tested if and how propranolol, a non-selective beta-adrenergic receptor antagonist, reduces PD tremor, and whether or not this effect is specific to stressful conditions. Methods In a cross-over, double-blind intervention study, participants with PD resting tremor received propranolol (40mg, single dose) or placebo (counter-balanced) on two different days. During both days, we assessed tremor severity (with accelerometry) and tremor-related brain activity (with functional Magnetic Resonance Imaging; fMRI), as well as heart rate and pupil diameter, while subjects performed a stressful cognitive load task that has been linked to the noradrenergic system. We tested for effects of drug (propranolol vs. placebo) and stress (cognitive load vs. rest) on tremor power and tremor-related brain activity. Results We included 27 PD patients with clear resting tremor. Tremor power significantly increased during cognitive load vs. rest ( F (1,19)=13.8; p =.001; η p 2 =0.42) and decreased by propranolol vs. placebo ( F (1,19)=6.4; p =.02; η p 2 =0.25), but there was no interaction. We observed task-related brain activity in a stress-sensitive cognitive control network, and tremor power-related activity in the cerebello-thalamo-cortical circuit. Propranolol significantly reduced tremor-related activity in the motor cortex compared to placebo ( F (1,21)=5.3; p =.03; η p 2 =0.20), irrespective of cognitive load. Interpretation Our findings indicate that the noradrenergic system has a general, context-independent role in amplifying PD tremor at the level of the primary motor cortex.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".