Resilience is associated with cortical gray matter of the antinociceptive pathway in people with chronic pain
Bibliographic record
Abstract
Resilience is an important personal characteristic that influences health and recovery. Previous studies of chronic pain suggest that highly resilient people may be more effective at modulating their pain. Since brain gray matter in the antinociceptive pathway has also been shown to be abnormal in people with chronic pain, we examined whether resilience is related to gray matter in regions of interest (ROIs) of the antinociceptive pathway (rostral and subgenual anterior cingulate cortex (rACC, sgACC), anterior insula (aINS), dorsolateral prefrontal cortex (dlPFC)) normally and in people who are experiencing chronic pain. We extracted gray matter volume (GMV) and cortical thickness (CT) from 3T MRIs of 88 people with chronic pain (half males/females) and 86 healthy controls (HCs), who completed The Resilience Scale and Brief Pain Inventory. We found that resilience scores were significantly lower in people with chronic pain compared to HCs, whereas ROI GMV and CT were not different between groups. Resilience negatively correlated with average pain scores and positively correlated with GMV in the bilateral rACC, sgACC, and left dlPFC of people with chronic pain. Mediation analyses revealed that GMV in the right rACC and left sgACC partially co-mediated the relationship between resilience and average pain in people with chronic pain. The resilience-pain and some resilience-GMV relationships were sex-dependent. These findings suggest that the antinociceptive pathway may play a role in the impact of resilience on one's ability to modulate chronic pain. A better understanding of the brain-resilience relationship may help advance evidence-based approaches to pain management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".