The effects of intra-oral pain on motor cortex neuroplasticity associated with short-term novel tongue-protrusion training in humans
Bibliographic record
Abstract
To determine if short-term (15 min) training in a novel tongue-task is associated with rapid neuroplasticity of the tongue primary motor area (MI) in the human cerebral cortex, and if intra-oral tonic pain affects the tongue MI neuroplasticity and tongue-task training performance. Nine healthy volunteers (7 men, 2 women, mean age 24+/-1.1 years) participated in two cross-over training sessions in which the application to the tongue of the algesic chemical capsaicin (1%) or vehicle cream was randomized. Prior to and again immediately after 15 min of training in a tongue-protrusion task, transcranial magnetic stimulation (TMS) was applied to the MI in each session and motor evoked potentials (MEPs) were recorded in the tongue musculature and the first dorsal interosseous (FDI) muscle (as control). Neuroplasticity of the tongue MI, as reflected in a significantly enhanced TMS-MEP stimulus-response curve and reduced MEP threshold, was observed after the vehicle session but not after the capsaicin session. Subjects' overall mean performance scores were significantly higher in the vehicle session than in the capsaicin session. MI neuroplasticity may rapidly occur in association with successful performance in novel tongue-task training, but intra-oral tonic pain interferes with these effects. These findings suggest that nociceptive input modulates MI neuroplasticity associated with novel motor training and may impair the ability to learn a new motor task.
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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".