5 Hz repetitive transcranial magnetic stimulation over the ipsilesional sensory cortex enhances motor learning after stroke
Bibliographic record
Abstract
Sensory feedback is critical for motor learning, and thus to neurorehabilitation after stroke. Whether enhancing sensory feedback by applying excitatory repetitive transcranial magnetic stimulation (rTMS) over the ipsilesional primary sensory cortex (IL-S1) might enhance motor learning in chronic stroke has yet to be investigated. The present study investigated the effects of 5 Hz rTMS over IL-S1 paired with skilled motor practice on motor learning, hemiparetic cutaneous somatosensation, and motor function. Individuals with unilateral chronic stroke were pseudo-randomly divided into either Active or Sham 5 Hz rTMS groups (n = 11/group). Following stimulation, both groups practiced a Serial Tracking Task (STT) with the hemiparetic arm; this was repeated for 5 days. Performance on the STT was quantified by response time, peak velocity, and cumulative distance tracked at baseline, during the 5 days of practice, and at a no-rTMS retention test. Cutaneous somatosensation was measured using two-point discrimination. Standardized sensorimotor tests were performed to assess whether the effects might generalize to impact hemiparetic arm function. The active 5 Hz rTMS + training group demonstrated significantly greater improvements in STT performance {response time [F (1, 286.04) = 13.016, p < 0.0005], peak velocity [F (1, 285.95) = 4.111, p = 0.044], and cumulative distance [F (1, 285.92) = 4.076, p = 0.044]} and cutaneous somatosensation [F (1, 21.15) = 8.793, p = 0.007] across all sessions compared to the sham rTMS + training group. Measures of upper extremity motor function were not significantly different for either group. Our preliminary results suggest that, when paired with motor practice, 5 Hz rTMS over IL-S1 enhances motor learning related change in individuals with chronic stroke, potentially as a consequence of improved cutaneous somatosensation, however no improvement in general upper extremity function was observed.
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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) | 0.001 | 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".