Acute Stress Contributes to Individual Differences in Pain and Pain-Related Brain Activity in Healthy and Chronic Pain Patients
Bibliographic record
Abstract
Individual differences in pain sensitivity and reactivity are well recognized but the underlying mechanisms are likely to be diverse. The phenomenon of stress-induced analgesia is well documented in animal research and individual variability in the stress response in humans may produce corresponding changes in pain. We assessed the magnitude of the acute stress response of 16 chronic back pain (CBP) patients and 18 healthy individuals exposed to noxious thermal stimulations administered in a functional magnetic resonance imaging experiment and tested its possible contribution to individual differences in pain perception. The temperature of the noxious stimulations was determined individually to control for differences in pain sensitivity. The two groups showed similar significant increases in reactive cortisol across the scanning session when compared with their basal levels collected over 7 consecutive days, suggesting normal hypothalamic-pituitary-adrenal axis reactivity to painful stressors in CBP patients. Critically, after controlling for any effect of group and stimulus temperature, individuals with stronger cortisol responses reported less pain unpleasantness and showed reduced blood oxygenation level-dependent activation in nucleus accumbens at the stimulus onset and in the anterior mid-cingulate cortex (aMCC), the primary somatosensory cortex, and the posterior insula. Mediation analyses indicated that pain-related activity in the aMCC mediated the relationship between the reactive cortisol response and the pain unpleasantness. Psychophysiological interaction analysis further revealed that higher stress reactivity was associated with reduced functional connectivity between the aMCC and the brainstem. These findings suggest that acute stress modulates pain in humans and contributes to individual variability in pain affect and pain-related brain activity.
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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 | 0.001 |
| 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) | 0.001 | 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".