Transcranial Static Magnetic Field Stimulation of the Motor Cortex in Children
Bibliographic record
Abstract
Background: Non-invasive neuromodulation is an emerging therapy for children with early brain injury but is difficult to apply to preschoolers when windows of developmental plasticity are optimal. Transcranial static magnetic field stimulation (tSMS) decreases primary motor cortex (M1) excitability in adults but effects on the developing brain are unstudied. Objective/Hypothesis: We aimed to determine the effects of tSMS on cortical excitability and motor learning in healthy children. We hypothesized that tSMS over right M1 would reduce cortical excitability and inhibit contralateral motor learning. Methods: This randomized, sham-controlled, double-blinded, three-arm, cross-over trial enrolled 24 healthy children aged 10-18 years. Transcranial Magnetic Stimulation (TMS) assessed cortical excitability via motor-evoked potential (MEP) amplitude. Motor learning was assessed via the Purdue Pegboard Test (PPT). A tSMS magnet (S-45-30-N, Supermagnete) or sham was held over left or right M1 for 30 minutes while participants trained the left hand. A linear mixed effect model was used to examine intervention effects. Results: All 72 tSMS sessions were well tolerated without serious adverse effects. Neither cortical excitability as measured by MEPs nor paired-pulse intracortical neurophysiology was altered by tSMS. Contralateral tSMS inhibited early motor learning in the trained hand (p<0.01). Later in the motor learning process, ipsilateral tSMS facilitated motor learning (p<0.01). Conclusions: tSMS over M1 can modulate motor learning in children, with effects specific to side and stage of learning. Unlike adults, tSMS did not produce measurable changes in MEP amplitude. Our findings suggest therapeutic potential for tSMS neuromodulation in young children with cerebral palsy (CP).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".