Cognitive modulation of pain-related brain responses depends on behavioral strategy
Bibliographic record
Abstract
Interactions of pain and cognition have been studied in humans and animals previously, but the relationship between such behavioral interactions and brain activity is unknown. We aimed to show using functional MRI (fMRI) how a cognitively demanding task (Stroop) modulates pain-related brain activations and conversely, how pain modulates attention-related activity. Reaction time data indicated two types of pain responders: subjects in the A group had a faster Stroop reaction time when pain was concomitant to the attention task, while those in the P group had a slower Stroop performance during painful stimulation. fMRI data obtained during Stroop performance with and without noxious stimulation were subjected to region of interest analyses. We first tested whether brain activity during painful median nerve stimulation was modulated by cognitive load. We next tested whether brain activity during the high conflict cognitive task was modulated by pain. Pain-related activity in three regions, primary (S1), and secondary (S2) somatosensory cortices, and anterior insula, was attenuated by cognitive engagement, but this effect was specific to the A group. Pain-related activations in the caudal and rostral anterior cingulate cortex (ACC) and ventroposterior thalamus were not modulated by cognitive load. None of the areas showing attention-related responses, including bilateral dorsolateral prefrontal and posterior parietal cortices, were modulated by pain. These findings suggest that cortical regions associated with pain can be modulated by cognitive strategies. Furthermore, the distinction of behavioral subgroups may relate to cognitive coping strategies taken by patients with 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) | 0.002 | 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".