Neural and behavioral correlates of human pain avoidance in participants with and without episodic migraine
Bibliographic record
Abstract
ABSTRACT: The innate motivation to avoid pain can be disrupted when individuals experience uncontrollable stress, such as pain. This can lead to maladaptive behaviors, including passivity, and negative affect. Despite its importance, motivational aspects of pain avoidance are understudied in humans and their neural mechanisms vastly unknown. Rodent models suggest an important role of the periaqueductal gray, but it is unknown whether it subserves a similar role in humans. Furthermore, it is unclear whether pain avoidance is associated with individual differences in pain coping. Using functional magnetic resonance imaging, networks underlying pain avoidance behavior were examined in 32 participants with and without episodic migraine. Pain avoidance behavior was assessed using an adaptation of the incentive delay task. In each trial of the task, participants tried to avoid a painful stimulus and receive a nonpainful one instead while the difficulty to succeed varied across trials (3 difficulty levels: safe, easy, and difficult). After unsuccessful pain avoidance on the preceding trial, participants showed reduced pain avoidance behavior, especially in the difficult condition. This reduction in behavior was associated with higher helplessness scores only in participants with migraine. Higher helplessness in participants with migraine was further correlated with a stronger decrease in activation of cortical areas associated with motor behavior, attention, and memory after unsuccessful pain avoidance. Of these areas, specifically posterior parietal cortex activation predicted individual's pain avoidance behavior on the next trial. The results link individual pain coping capacity to patterns of neural activation associated with altered pain avoidance in patients with migraine.
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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".