Neck Muscle Vibration Alters Cerebellar Processing Associated with Motor Skill Acquisition of a Proprioceptive-Based Task
Bibliographic record
Abstract
Experimentally induced neck fatigue and neck pain have been shown to impact cortico-cerebellar processing and sensorimotor integration, assessed using a motor learning paradigm. Vibration specifically impacts muscle spindle feedback, yet it is unknown whether transient alterations in neck sensory input from vibration impact these neural processing changes following the acquisition of a proprioceptive-based task. Twenty-five 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, 6 F) received 10 min of rest and the vibration group (VIB, n = 12, 6 F) received 10 min of 60 Hz vibration on the right sternocleidomastoid and left cervical extensors. Task performance was measured 24 h later to assess retention. Significant time by group interactions occurred for the N18 SEP peak, 21.77% decrease in VIB compared to 58.74% increase in CONT (F(1,23) = 6.475, p = 0.018, np2 = 0.220), and the N24 SEP peak, 16.31% increase in VIB compared to 14.05% decrease in CONT (F(1,23) = 5.787, p = 0.025, np2 = 0.201). 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 the SEP peaks associated with cerebellar input (N18) and cerebellar processing (N24) suggests that an altered proprioceptive input from neck vibration impacts cerebellar pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".