The PSTIM Trial: Pediatric Transcranial Static Magnetic Field Stimulation to Improve Motor Learning
Bibliographic record
Abstract
Non-invasive neuromodulation is an emerging therapy for children with early brain injury but is difficult to apply to preschoolers where windows of developmental plasticity are optimal. Transcranial static magnetic field stimulation (tSMS) decreases motor cortex excitability in adults but effects on the developing brain are unstudied. We aimed to determine the effects of tSMS on primary motor cortex (M1) excitability and motor learning in healthy children. Our randomized, sham-controlled, double-blinded, 3-arm, cross-over interventional trial enrolled 24 typically developing school-aged children. We used a linear mixed effects model to examine intervention effects over time. Our results demonstrated that tSMS modulates motor learning. Contralateral (right) tSMS inhibited early motor learning in the trained hand (p<0.01) while ipsilateral (left) tSMS facilitated later stages of motor learning (p<0.01). We did not observe changes in cortical excitability as assessed by transcranial magnetic stimulation (TMS) generated motor-evoked potential (MEP) amplitudes and intracortical neurophysiology paradigms. We demonstrated the feasibility, safety, and favourable tolerability of tSMS in a pediatric population. We conclude that tSMS over motor cortex can modulate motor learning in children with effects specific to both the hemisphere of stimulation and stage of learning. 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) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".