The effect of prolonged elbow pain and rTMS on cortical inhibition: A TMS-EEG study
Bibliographic record
Abstract
Introduction: Recent studies using combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) have shown that pain leads to an increase in the N45 peak of the TMS-evoked potential (TEP), which is mediated by GABAergic inhibition. Conversely, 10Hz repetitive TMS (10Hz-rTMS), which provides pain relief, reduces the N45 peak. However, these studies used brief pain stimuli (lasting minutes), limiting their clinical relevance. The present study determined the effect of pain and 10Hz-rTMS on the N45 peak in a prolonged pain model (lasting several days) induced by nerve growth factor (NGF) injection to the elbow muscle. Materials and Methods: Experiment 1 : TEPs were measured in 22 healthy participants on Day 0 (pre-NGF), Day 2 (peak pain), and Day 7 (pain resolution). Experiment 2 : We examined the effect of 5 days of active (n=16) or sham (n=16) rTMS to the left primary motor cortex (M1) on the N45 peak during prolonged NGF-induced pain, with TEPs measured on Day 0 and Day 4 (post-rTMS). Results: Experiment 1: While no overall change in the N45 peak was seen, a correlation emerged between higher pain severity on Day 2 and a larger increase in the N45 peak. Experiment 2 : Active rTMS reduced the N45 peak on Day 4 vs. Day 0, with no effect in the sham group. Conclusion: Our findings suggest that (i) higher pain severity correlates with an increase in the N45 peak, and (ii) rTMS decreases cortical inhibition in a model of prolonged experimental pain. This study extends previous research by demonstrating a link between pain perception and cortical inhibition within a prolonged pain context.
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 | 0.000 |
| 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 | 0.000 |
| 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".