Location-specific responses to nociceptive input support the purposeful nature of motor adaptation to pain
Bibliographic record
Abstract
Movement is changed in pain, but the mechanisms remain unclear. Key questions are unresolved such as whether activation can be inhomogeneously distributed within a muscle in a manner that is specific to the location of noxious input. This study addressed this question using high-density electromyography (EMG) to study regional redistribution of muscle activation within the vasti muscles and changes in knee extension force direction in response to noxious stimulation applied to muscular and nonmuscular tissues around the knee. Fourteen participants performed a low-force knee extension contraction at baseline, during, and after pain induced in 4 locations (infrapatellar fat pad, vastus lateralis, distal vastus medialis, or proximal vastus medialis). The knee extension force direction was estimated from a 3-dimensional load cell positioned just above the ankle. Regional muscle activation was estimated from amplitude of high-density surface EMG signals from vastus medialis and lateralis. Pain-induced spatial variations of activation were identified as the position of the 5 channels that showed the largest decrease (or smallest increase) in amplitude from baseline to pain or after pain. Knee extension force was produced more medially during pain after infrapatellar pad injection only (P = 0.01). Preferential reduction of activation of the distal region of vastus medialis was observed when distal vastus medialis (P < 0.001) or vastus lateralis (P < 0.05) was injected. Both adaptations persisted after pain resolution. These results support the hypothesis that specific adaptation depends on the location of a noxious stimulus and imply that recovery of pain is not necessarily concomitant with return of the EMG to prepain patterns.
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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 | 0.001 |
| 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) | 0.001 | 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".