Mood Influences Supraspinal Pain Processing Separately from Attention
Bibliographic record
Abstract
Studies show that inducing a positive mood or diverting attention from pain decreases pain perception. Nevertheless, induction manipulations, such as viewing interesting movies or performing mathematical tasks, often influence both emotional and attentional states. Imaging studies have examined the neural basis of psychological pain modulation, but none has explicitly separated the effects of emotion and attention. Using odors to modulate mood and shift attention from pain, we previously showed that the perceptual consequences of changing mood differed from those of altering attention, with mood primarily altering pain unpleasantness and attention preferentially altering pain intensity. These findings suggest that brain circuits involved in pain modulation provoked by mood or attention are partially separable. Here we used functional magnetic resonance imaging to directly compare the neurocircuitry involved in mood- and attention-related pain modulation. We manipulated independently mood state and attention direction, using tasks involving heat pain and pleasant and unpleasant odors. Pleasant odors, independent of attentional focus, induced positive mood changes and decreased pain unpleasantness and pain-related activity within the anterior cingulate (ACC), medial thalamus, and primary and secondary somatosensory cortices. The effects of attentional state were less robust, with only the activity in anterior insular cortex (aIC) showing possible attentional modulation. Lateral inferior frontal cortex [LinfF; Brodmann's area (BA) 45/47] activity correlated with mood-related modulation, whereas superior posterior parietal (SPP; BA7) and entorhinal activity correlated with attention-related modulation. ACC activity covaried with LinfF and periacqueductal gray activity, whereas aIC activity covaried with SPP activity. These findings suggest that separate neuromodulatory circuits underlie emotional and attentional modulation of 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 | 0.000 |
| 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) | 0.002 | 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".