It’s the Sound, not the Pulse: Peripheral Magnetic Stimulation Reduces Central Sensitization through Auditory Modulatory Effects
Bibliographic record
Abstract
Abstract Repetitive peripheral magnetic stimulation (rPMS) is a non-pharmacological, non-invasive analgesic modality with limited side effects. However, there is a paucity of controlled studies demonstrating its efficacy compared to existing pain management tools. Here, in an initial sample of 100 healthy participants (age 18-40), we compared the analgesic efficacy of two rPMS stimulation protocols—continuous theta burst stimulation (ctbPMS) and intermittent TBS (itbPMS)—against transcutaneous electric nerve stimulation (TENS), a peripheral stimulation technique that is commonly used for pain management. We also included a sham rPMS protocol where participants heard the sound of rPMS stimulation while the coil was placed over their arm, but received no peripheral stimulation. We hypothesized that itbPMS and ctbPMS—but not sham—would reduce pain intensity, pain unpleasantness, and secondary hyperalgesia evoked by a phasic heat pain (PHP) paradigm on the volar forearm with similar efficacy to TENS. Neither rPMS nor TENS reduced reported pain intensity or unpleasantness (p>0.25). However, ctbPMS and itbPMS significantly reduced the area of secondary hyperalgesia, whereas TENS did not (F3,96= 4.828, p= 0.004). Unexpectedly, sham rPMS, which involved auditory but no peripheral nerve stimulation, also significantly reduced secondary hyperalgesia compared to TENS. We performed a second study (n=32) to investigate auditory contributions to rPMS analgesia. Masking the rPMS stimulation sound with pink noise eliminated its analgesic effect on secondary hyperalgesia (p=0.5). This is the first study to show that the analgesic properties of rPMS in acute experimental pain may be largely attributed to its auditory component rather than peripheral nerve stimulation.
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) | 0.000 | 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) | 0.006 | 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".