Effect of Neck Muscle Vibration Prior to Motor Learning on Short-Latency SEP Peak Amplitudes and Motor Performance
Bibliographic record
Abstract
Background/Objectives: Neck muscle vibration alters neural processing, sensorimotor integration, and proprioception in healthy adults. Significant differential changes in the N18 and N24 somatosensory evoked potential (SEP) peak amplitudes, coupled with altered motor learning, occurred when completion of a force-matching task took place following neck muscle vibration. It is currently unknown if neck muscle vibration also impacts acquisition of skills from visuomotor tracking tasks, a gap this research addresses. Methods: A total of 25 right-handed, healthy participants were divided into vibration (age: 21.7 ± 1.89, n = 13; 8 females) (V) and no-vibration (NV) control (age: 21.2 ± 3.03, n = 12; 6 females) groups. The vibration was device applied over the right sternocleidomastoid and left cervical extensor muscles. The participants underwent right-median-nerve stimulation at 2.47 Hz and 4.98 Hz to elicit SEPs. A total of 1000 sweeps were recorded and averaged using an ANT Neuro Waveguard 64-lead EEG cap (ANT Neuro, the Netherlands, Manufactured by Eemagine, Berlin, Germany)pre- and post-completion of a novel visuomotor tracing task (MTT). Post-acquisition, the NV group had a 10 min rest, and the V group received 10 min of vibration at 60 Hz before motor task completion, followed immediately by post-acquisition and retention 24 h after. Results: N18 peak: The V group exhibited a proportional amplitude increase of 19%, while the NV group exhibited a 36% decrease. There was a trend toward decreased retention in the V group. P25 showed a significant effect of time, with increases of 11% for V and 9% for NV. Conclusions: V resulted in N18 SEP changes post-MTT-skill-acquisition. Both groups appeared to learn, with the V trending towards less retention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".