The heating rate matters! contact heat evoked potentials in musicians and non-musicians
Bibliographic record
Abstract
Classical musical training requires extreme levels of fine motor control, resulting in adaptive neuroplastic alterations in professional musicians. Additionally, musicians have a high prevalence of pain syndromes, which makes them an interesting group to research the influence of neuroplasticity on nociception. This report consists of two parts. Firstly, we present the results of a preliminary study comparing musicians and non-musicians with respect to their cortical responses to noxious heat stimuli at their hands and feet, using contact heat evoked potentials (CHEPs). Secondly, we quantitatively discuss the influence of the heating rates of two different stimulation devices on CHEPs when applying the exact same settings. For this, we measured the temperature curves of the devices’ stimuli and connected their respective heating rates to the resulting CHEPs. Musicians showed a significantly larger N2 latency difference between hands and feet (20.86 ms, p=0.0045 ), compared to non-musicians. Additionally, we found that, despite the exact same settings, different stimulation devices produced considerably different temperature curves. The resulting time difference between the stimulation devices of 104.78 ms explains the latency difference of the CHEPs produced by the respective device of 104.09 ms extremely well. This study underlines that musicians are an interesting model for neuroplasticity regarding nociception, as they respond differently to nociceptive stimuli. Moreover, it contributes to the understanding of the connection between a stimulation device’s heating rate and the resulting CHEPs, an important finding that has never been quantified before but has considerable consequences on the comparability of results.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".