Back

Ipsilateral rest tremor-dopamine transporter correlation reflects a broader dopaminergic difference, not tremor-specific pathophysiology

Mendonca, M.; Alves da Silva, J.

2026-08-26 neurology
10.64898/2026.08.24.26361220 medRxiv
Show abstract

Background and Objectives: To test whether the positive correlation between rest tremor (RT) and ipsilateral striatal DAT binding reflects a tremor-specific ipsilateral mechanism or simply the globally better-preserved dopamine terminals in RT patients. Methods: We compared ipsilateral and contralateral correlations between striatal binding, rest tremor, bradykinesia and rigidity in two cross-sectional Parkinson disease cohorts from the Parkinson's Progression Markers Initiative (baseline, N=1055; follow-up, median 2.2 years, N=652). We tested whether correlations survived permutation testing that shuffled severity scores isolating severity-dependent effects from group-level effects and used out-of-sample prediction to compare how well binding predicted symptom presence versus severity. Results: Bradykinesia and rigidity showed large contralateral correlations, distinguishable from the permutation null in every comparison, and predicted both presence and severity. Rest tremor's ipsilateral correlation was not distinguishable from the null in most comparisons. Striatal binding, both ipsi or contralateral, predicted its presence (AUC 0.55-0.61) but not its severity (R2<0.002), in both cohorts. Conclusions: Our findings argue against a direct pathophysiologic link between tremor amplitude and ipsilateral dopaminergic function. More parsimoniously, the ipsilateral binding-RT correlation likely reflects a distinct degeneration pattern in patients with RT rather than a graded, dose-dependent circuit mechanism.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.