Imaging Attentional Modulation of Pain in the Periaqueductal Gray in Humans
Bibliographic record
Abstract
Pain is an unpleasant sensory and emotional experience usually triggered by stimulation of peripheral nerves and often associated with actual or potential tissue damage. It is well known that pain perception for patients and normal subjects can be modulated by psychological factors, such as attention, stress, and arousal. Our understanding of how this modulation occurs at a neuroanatomical level is poor. Here we neuroanatomically defined a key area in the network of brain regions active in response to pain that is modulated by attention to the painful stimulus. High-resolution functional magnetic resonance imaging was used to define brain activation to painful heat stimulation applied to the hand of nine normal subjects within the periaqueductal gray region. Subjects were asked to either focus on or distract themselves from the painful stimuli, which were cued using colored lights. During the distraction condition, subjects rated the pain intensity as significantly lower compared with when they attended to the stimulus. Activation in the periaqueductal gray was significantly increased during the distraction condition, and the total increase in activation was predictive of changes in perceived intensity. This provides direct evidence supporting the notion that the periaqueductal gray is a site for higher cortical control of pain modulation in humans.
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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 | 0.000 |
| 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".