Cortical responses to pain in healthy individuals depends on pain catastrophizing
Bibliographic record
Abstract
The personal experience of pain is complex and depends on physiological and psychological factors. From this latter category, pain catastrophizing plays an important role in pain behavior and response. We aimed to determine the effect of pain catastrophizing on central nociceptive processing in healthy individuals. Functional MRI was performed during two pain intensity levels evoked by electrical median nerve stimulation in 22 healthy individuals. Pain catastrophizing scores were determined for all subjects. Pain catastrophizing was not related to activity in regions associated with sensory-discriminative aspects of pain, such as the primary or secondary somatosensory cortex. Instead, during mild pain, there was a relationship between catastrophizing and activity in cortical regions associated with affective, attention, and motor aspects of pain, including dorsolateral prefrontal, insula, rostral anterior cingulate, premotor, and parietal cortices. During more intense pain, prefrontal cortical regions implicated in the top-down modulation of pain were negatively correlated with catastrophizing. These findings can be viewed from the framework of an attention model of pain catastrophizing, whereby a cortical vigilance network is engaged during mild pain, but diminished prefrontal cortical modulation impedes disengaging from and suppressing pain during more intense pain. These findings may also implicate catastrophizing in the progression to or persistence of 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 | 0.001 |
| 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) | 0.003 | 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".