Changes in Cortical Excitability and Parkinson Tremor After Botulinum Toxin Therapy
Bibliographic record
Abstract
Background and Objectives To investigate the relationship between botulinum toxin type A (BoNT-A) administration, tremor amplitude, and modulation of intracortical excitability and sensorimotor processing using paired-pulse transcranial magnetic stimulation (pp-TMS) in patients with early, tremor-dominant Parkinson disease (PD). Methods Twelve de novo (naive to anti-PD medications) and 7 l-dopa (optimized on levodopa) participants with PD with tremor affecting one arm were recruited. All participants received 4 serial BoNT-A treatments for tremor every 12 weeks and peak effect was assessed 6 weeks posttreatment, totaling 8 visits over 42 weeks. Injection measures were based on kinematic tremor analysis. Short interval intracortical inhibition (SICI), intracortical facilitation (ICF), long interval intracortical inhibition (LICI), and measures of sensorimotor interaction (short-latency afferent [SAI] and long-latency afferent [LAI] stimulation) were assessed in both hemispheres using pp-TMS paradigms at each time point. Linear mixed models analyzed the effect of each pp-TMS measure and tremor severity within each cohort and the association between pp-TMS and tremor severity in the de novo cohort over 42 weeks. t Tests compared pp-TMS measures between hemispheres per time point. Results Baseline SICI, LICI, and SAI was reduced (higher motor evoked potential [MEP] ratio) on the tremulous/treated side compared to the nontremulous side in de novo participants. On the treated side in the de novo cohort, BoNT-A treatment significantly reduced ICF and increased LICI, SAI, and LAI (lower MEP ratio) at peak BoNT-A time points. The change in tremor severity was significantly associated with changes in SICI, LICI, and LAI. Discussion Our findings suggest that tremor severity in early PD may be related to impaired intracortical inhibition and defective sensorimotor integration.
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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 | 0.001 |
| 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) | 0.001 | 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".