The Interactive Effect of Tonic Pain and Motor Learning on Corticospinal Excitability
Bibliographic record
Abstract
Prior work showed differential alterations in early somatosensory evoked potentials (SEPs) and improved motor learning while in acute tonic pain. The aim of the current study was to determine the interactive effect of acute tonic pain and early motor learning on corticospinal excitability as measured by transcranial magnetic stimulation (TMS). Two groups of twelve participants (n = 24) were randomly assigned to a control (inert lotion) or capsaicin (capsaicin cream) group. TMS input–output (IO) curves were performed at baseline, post-application, and following motor learning acquisition. Following the application of the creams, participants in both groups completed a motor tracing task (pre-test and an acquisition test) followed by a retention test (completed without capsaicin) within 24–48 h. Following an acquisition phase, there was a significant increase in the slope of the TMS IO curves for the control group (p < 0.05), and no significant change for the capsaicin group (p = 0.57). Both groups improved in accuracy following an acquisition phase (p < 0.001). The capsaicin group outperformed the control group at pre-test (p < 0.005), following an acquisition phase (p < 0.005), and following a retention test (p < 0.005). When data was normalized to the pre-test values, the learning effects were similar for both groups post-acquisition and at retention (p < 0.005), with no interactive effect of group. The acute tonic pain in this study was shown to negate the increase in IO slope observed for the control group despite the fact that motor performance improved similarly to the control group following acquisition and retention. This study highlights the need to better understand the implications of neural changes accompanying early motor learning, particularly while in pain.
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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) | 0.001 | 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 | 0.001 |
| 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".