Unique brain regions involved in positive versus negative emotional modulation of pain
Bibliographic record
Abstract
Background and aims Research has shown that negative emotions increase perceived pain whereas positive emotions reduce pain. Here we aim to investigate the neural mechanisms underlying this phenomenon. Methods While undergoing functional magnetic resonance imaging of the brain, 20 healthy adult females were presented with negative, neutral, and positive emotion-evoking visual stimuli in combination with the presentation of a noxious thermal stimulus to the hand. Participants rated the intensity and unpleasantness of the noxious thermal stimulus during each of the valence conditions. General linear model analyses were performed on the imaging data for each valence condition and specific contrasts were run. Results Significant differences were detected for the emotional modulation of pain (EMP) between the positive and negative conditions. Unique to the positive condition, there was increased activity in the inferior parietal, parahippocampal/perirhinal, precuneus/superior parietal, and the prefrontal cortices. Unique to the negative condition, there was increased activity in anterior and posterior cingulate and angular gyrus. Conclusions Positive and negative EMP appear to involve different brain regions. Implications Although there is some overlap in the brain regions involved in the positive and negative EMP, brain regions unique to each condition are identified and, moreover, the regions identified are involved in internal and external focus, respectively, pointing to a potential mechanism underlying this phenomenon.
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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".