Neck Muscle Vibration Alters Cerebellar Processing Associated with Motor Skill Acquisition and Proprioception
Bibliographic record
Abstract
Abstract Long term changes in neck sensory feedback in those with neck pain impacts motor learning, proprioception, and cortical processing. However, it is unclear whether transient alterations in neck sensory input from vibration impact sensorimotor integration (SMI) and somatosensory processing following acquisition of a proprioceptive-based task. The purpose of this research was to determine the effects of neck muscle vibration on SMI and motor learning. 25 right-handed participants had electrical stimulation over the right median nerve to elicit short and middle latency somatosensory evoked potentials (SEPs) pre- and post-acquisition of a force matching tracking task. Following the pre-acquisition phase, controls (CONT) (n = 13, 6F) received 10 minutes of rest and the vibration group (VIB) (n = 12, 6F) received 10 minutes of 60Hz vibration on the right sternocleidomastoid and left cervical extensors. Task performance was measured 24 hours later to assess retention. Significant time by group interactions occurred for the N18 SEP peak (F (1, 23) = 6.475, p = 0.018, np2 = 0.220): where amplitudes increased by 58.74% in CONT and decreased by 21.77% in VIB and the N24 SEP Peak (F (1, 23) = 5.787, p = 0.025, np2 = 0.201): decreased by 14.05% in CONT and increased by 16.31% in VIB. Both groups demonstrated improvements in motor performance post-acquisition (F (1, 23) = 52.812, p < 0.001, np2 = 0.697) and at retention (F (1, 23) = 35.546, p < 0.001, np2 = 0.607). Group dependent changes in SEP peaks associated with cerebellar processing (N18 and N24) occurred post-acquisition suggesting differences in cerebellar-somatosensory pathways. This suggests that vibration altered proprioceptive inputs used to construct body schema.
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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".