Brain and muscles magnetic stimulation in a drug-free case of Parkinson’s disease: Motor improvements concomitant to neuroplasticty
Bibliographic record
Abstract
Noninvasive stimulation of the nervous system is of growing interest in Parkinson's disease (PD) to slow-down motor decline and decrease medication and its side-effects. Repetitive transcranial magnetic stimulation (rTMS) used in PD to modulate the excitability of the primary motor cortex (M1) provided controversial results, in part because of interactions with medication. This warrants to administer rTMS in drug-free patients. Repetitive peripheral magnetic stimulation (rPMS of muscles) has not yet been tested in PD. Its influence on M1 plasticity (as tested by TMS, transcranial magnetic stimulation) and sensorimotor disorders in other health conditions makes it worth be explored in PD. Thus, rTMS and rPMS were tested in a drug-free woman (52 years old, PD-diagnosed 10 years ago) in four different rTMS + rPMS combinations (one week apart): sham-sham, real-real, real-sham, sham-real. rTMS was applied over M1 contralateral to the most impaired bodyside, and rPMS on muscles of the legs, trunk, and arms, bilaterally. M1 plasticity (TMS measures) and motor symptoms and function (clinical outcomes) were measured at different timepoints. The real-real session induced the largest motor improvements, with possible summation of effects between sessions, and maintenance at follow-up (80 days later). This was paralleled by changes of M1 facilitation and inhibition. This sheds a new light on the link between TMS measures of M1 plasticity and motor changes in PD and informs on the remaining potential for neuroplasticity and functional improvement after 10 years of PD with no antiparkinsonian drug. De novo patients with PD (drug-free) should be motivated to participate in future randomized clinical trials to further test the slow-down or delay of motor decline under noninvasive neurostimulation regimens, whatever the stage of the 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".