Static magnetic stimulation of the primary motor cortex impairs online but not offline motor sequence learning
Bibliographic record
Abstract
Static magnetic fields (SMFs) are known to alter neural activity, but evidence of their ability to modify learning-related neuroplasticity is lacking. The present study tested the hypothesis that application of static magnetic stimulation (SMS), an SMF applied transcranially via a neodymium magnet, over the primary motor cortex (M1) would alter learning of a serial reaction time task (SRTT). Thirty-nine participants took part in two experimental sessions separated by 24 h where they had to learn the SRTT with their right hand. During the first session, two groups received SMS either over contralateral (i.e., left) or ipsilateral (i.e., right) M1 while a third group received sham stimulation. SMS was not applied during the second session. Results of the first session showed that application of SMS over contralateral M1 impaired online learning as compared to both ipsilateral and sham groups, which did not differ. Results further revealed that application of SMS did not impair offline learning or relearning. Overall, these results are in line with those obtained using other neuromodulatory techniques believed to reduce cortical excitability in the context of motor learning and suggest that the ability of SMS to alter learning-related neuroplasticity is temporally circumscribed to the duration of its application.
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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) | 0.000 | 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".