GR.4 Neurophysiological and clinical effects of low-intensity transcranial ultrasound of the motor cortex in Parkinson’s disease
Bibliographic record
Abstract
Background: Low-intensity transcranial ultrasound (TUS) is a non-invasive neuromodulation technique, which in theta burst mode (tbTUS) can increase cortical excitability. Parkinson’s disease (PD) has altered cortical excitability of motor cortex (M1). We evaluated the neurophysiological and clinical effects of M1 tbTUS in PD patients. Methods: Sixteen PD patients (4F, 59.5±9.7 years) in ON and OFF dopaminergic medication states, and 15 controls (5F, 61.9±8.7 years) were evaluated. tbTUS was applied for 80 seconds at M1 with 20W/cm2. Motor evoked potential (MEP) was recorded at baseline, at 5-minutes (T5), T30, and T60 after tbTUS. Motor (m)UPDRS was evaluated in PD at baseline and T60. Results: A linear mixed model on MEP amplitudes comparing PD-ON, PD-OFF and controls showed significant effect of time (F=4.83, p=0.003). Post-hoc analysis showed significant difference between baseline and T30 timepoints (p=0.0003). The MEP increase at T30 was higher in controls (66%), followed by PD-ON (41%) and PD-OFF (21%). PD-ON showed reduced mUPDRS at T60 when compared to PD-OFF, with significant effect of time (F=6.14, p=0.017) and group (F=5.39, p=0.025). Conclusions: tbTUS induced motor cortical plasticity is reduced in PD-OFF, that is partially restored by dopaminergic medications.Repeated sessions of tbTUS can be further investigated as a novel non-invasive treatment for 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 | 0.001 | 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) | 0.002 | 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".