Multiple faces of pain: effects of chronic pain on the brain regulation of facial expression
Bibliographic record
Abstract
Pain behaviors are shaped by social demands and learning processes, and chronic pain has been previously suggested to affect their meaning. In this study, we combined functional magnetic resonance imaging with in-scanner video recording during thermal pain stimulations and use multilevel mediation analyses to study the brain mediators of pain facial expressions and the perception of pain intensity (self-reports) in healthy individuals and patients with chronic back pain (CBP). Behavioral data showed that the relation between pain expression and pain report was disrupted in CBP. In both patients with CBP and healthy controls, brain activity varying on a trial-by-trial basis with pain facial expressions was mainly located in the primary motor cortex and completely dissociated from the pattern of brain activity varying with pain intensity ratings. Stronger activity was observed in CBP specifically during pain facial expressions in several nonmotor brain regions such as the medial prefrontal cortex, the precuneus, and the medial temporal lobe. In sharp contrast, no moderating effect of chronic pain was observed on brain activity associated with pain intensity ratings. Our results demonstrate that pain facial expressions and pain intensity ratings reflect different aspects of pain processing and support psychosocial models of pain suggesting that distinctive mechanisms are involved in the regulation of pain behaviors in chronic pain.
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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 | 0.002 |
| 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) | 0.002 | 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".