Keeping an eye on pain expression in primary somatosensory cortex
Bibliographic record
Abstract
Facial expressions of pain are composed of a subset of pain-indicative muscle movements. Amongst this subset, contracting the muscles surrounding the eyes (orbicularis oculi muscle) is the most frequent response and has been linked specifically to pain intensity, a fundamental aspect of the sensory dimension of pain. To further explore this link, the present study used functional magnetic resonance imaging (fMRI) to test the hypothesis that orbicularis oculi activation during pain reflects the magnitude of brain responses in areas being involved in processing the sensory dimension of pain. Facial and brain (BOLD) responses to experimentally-induced heat pain applied to the left lower leg were assessed in twenty-two healthy participants after verbal suggestions were given to specifically increase perceived pain intensity and in control conditions involving no suggestion. Increases in pain intensity produced the expected changes in facial responses characterized by a stronger contraction of the orbicularis oculi muscle. A regression model further demonstrated that stronger increases in orbicularis oculi activity reflected a larger increase in the BOLD response to the noxious stimulus in the leg area of the primary somatosensory cortex (S1) and a larger decrease in medial prefrontal activity consistent with previous finding suggesting disinhibition. Importantly, the positive coupling of orbicularis oculi with S1 activity was not accounted for by changes in other facial muscles. These results are consistent with the notion that facial expressions of pain differentially encode the multi-dimensional pain experience and reflect, at least partly, the activity of the spino-thalamo-cortical pathway targeting the primary somatosensory cortex.
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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) | 0.002 | 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".