Music to My Senses: Functional Magnetic Resonance Imaging Evidence of Music Analgesia Across Connectivity Networks Spanning the Brain and Brainstem
Bibliographic record
Abstract
Pain is often viewed and studied as an isolated perception. However, cognition, emotion, salience effects, and autonomic and sensory input are all integrated to create a comprehensive experience. Music-induced analgesia has been used for thousands of years, with moderate behavioural effects on pain perception, yet the neural mechanisms remain ambiguous. The purpose of this study was to investigate the effects of music analgesia through individual ratings of pain, and changes in connectivity across a network of regions spanning the brain and brainstem that are involved in limbic, paralimbic, autonomic, cognitive, and sensory domains. This is the first study of its kind to assess the effects of music analgesia using complex network analyses in the human brain and brainstem. Functional MRI data were collected from 20 healthy men and women with concurrent presentation of noxious stimulation and music, in addition to control runs without music. Ratings of peak pain intensity and unpleasantness were collected for each run and were analysed in relation to the functional data. We found that music alters connectivity across these neural networks between regions such as the insula, thalamus, hypothalamus, amygdala and hippocampus (among others), and is impacted by individual pain sensitivity. While these differences are important for how we understand pain and analgesia, it is essential to note that these effects are variable across participants and provide moderate pain relief at best. Therefore, a therapeutic strategy involving music should use it as an adjunct to pain management in combination with healthy lifestyle changes and/or pharmaceutical intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 teacher head, 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".