Peak Alpha Frequency Is Not Significantly Altered by Five Days of Experimental Pain and Repetitive Transcranial Stimulation of the Left Dorsolateral Prefrontal Cortex
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) holds promise as a noninvasive pain treatment. Given the link between individual peak alpha frequency (PAF) of resting-state electroencephalographic recordings and pain sensitivity, and the potential for rTMS to modulate PAF, we investigated these relationships through a secondary analysis of established rTMS-induced analgesia in an experimental model of sustained muscle pain. In a randomized, single-blind, sham-controlled experiment, 30 healthy adults underwent either active (n = 15) or sham (n = 15) high-frequency rTMS (20 min) to the left dorsolateral prefrontal cortex for five consecutive days following the induction of sustained experimental pain by nerve growth factor (NGF) injected into the right extensor carpi radialis brevis muscle. The pain intensity was assessed daily for 14 days on a numerical rating scale (NRS). PAF of the resting-state electroencephalography (5 min) was assessed before and 1 day after the five rTMS treatment days. The preregistered analysis revealed no significant changes in PAF following five consecutive days of active (from 9.90 ± 0.39 Hz to 9.95 ± 0.38 Hz) or sham (from 9.86 ± 0.44 Hz to 9.81 ± 0.35 Hz) rTMS, suggesting that the impact of rTMS on NGF-induced pain is independent of PAF modulation. However, exploratory analysis indicated an association between a larger absolute difference in baseline PAF to 10 Hz (i.e., the rTMS frequency) and higher NRS pain ratings at Day 5 in participants receiving active rTMS. This suggests rTMS is more efficient when delivered close to individual PAF or for those with PAF around 10 Hz, necessitating further exploration of PAF's role in rTMS-induced pain relief.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".