Posterior-superior insula repetitive transcranial magnetic stimulation reduces experimental tonic pain and pain-related cortical inhibition in humans
Bibliographic record
Abstract
ABSTRACT: High frequency repetitive transcranial magnetic stimulation (rTMS) to the posterior-superior insula (PSI) may produce analgesic effects. However, the alterations in cortical activity during PSI-rTMS analgesia remain poorly understood. The present study aimed to determine whether tonic capsaicin-induced pain and cortical inhibition (indexed using TMS-electroencephalography) are modulated by PSI-rTMS. Twenty healthy volunteers (10 females) attended 2 sessions randomized to active or sham rTMS. Experimental pain was induced by capsaicin administered to the forearm for 90 minutes, with pain ratings collected every 5 minutes. Left PSI-rTMS was delivered (10 Hz, 100 pulses per train, 15 trains) ∼50 minutes postcapsaicin administration. Transcranial magnetic stimulation-evoked potentials (TEPs) and thermal sensitivity were assessed at baseline, during capsaicin pain prior to rTMS and after rTMS. Bayesian evidence of reduced pain scores and increased heat pain thresholds were found after active rTMS, with no changes occurring after sham rTMS. Pain (prior to active rTMS) led to an increase in the frontal negative peak ∼45 ms (N45) TEP relative to baseline. After active rTMS, there was a decrease in the N45 peak back to baseline levels. In contrast, after sham rTMS, the N45 peak was increased relative to baseline. We also found that the reduction in pain numerical rating scale scores after active vs sham rTMS was correlated with and partially mediated by decreases in the N45 peak. These findings provide evidence of the analgesic effects of PSI-rTMS and suggest that the TEP N45 peak is a potential marker and mediator of both pain and analgesia. This study demonstrates that high-frequency rTMS targeting the posterior-superior insula reduces capsaicin-induced pain and alters cortical activity, with changes in the N45 TMS-evoked potential peak mediating the analgesic effects.
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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) | 0.003 | 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".