Unravelling the effect of experimental pain on the corticomotor system using transcranial magnetic stimulation and electroencephalography
Bibliographic record
Abstract
The interaction between pain and the motor system is well-known, with past studies showing that pain can alter corticomotor excitability and have deleterious effects on motor learning. The aim of this study was to better understand the cortical mechanisms underlying the interaction between pain and the motor system. Experimental pain was induced on 19 young and healthy participants using capsaicin cream, applied on the middle volar part of the left forearm. The effect of pain on brain activity and on the corticomotor system was assessed with electroencephalography (EEG) and transcranial magnetic stimulation (TMS), respectively. Compared to baseline, resting state brain activity significantly increased after capsaicin application in the central cuneus (theta frequency), left dorsolateral prefrontal cortex (alpha frequency), and left cuneus and right insula (beta frequency). A pain-evoked increase in the right primary motor cortex (M1) activity was also observed (beta frequency), but only among participants who showed a reduction in corticospinal output (as depicted by TMS recruitment curves). These participants further showed greater beta M1-cuneus connectivity than the other participants. These findings indicate that pain-evoked increases in M1 beta power are intimately tied to changes in the corticospinal system, and provide evidence that beta M1-cuneus connectivity is related to the corticomotor alterations induced by pain. The differential pattern of response observed in our participants suggest that the effect of pain on the motor system is variable from on individual to another; an observation that could have important clinical implications for rehabilitation professionals working with pain patients.
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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".