Patients with chronic pain exhibit a complex relationship triad between pain, resilience, and within- and cross-network functional connectivity of the default mode network
Bibliographic record
Abstract
Resilience is a psychological trait that strongly predicts chronic pain-related health outcomes. The neural correlates of both pain and trait resilience are critical to understand the brain-behaviour relationship in chronic pain; yet, neural correlates of resilience in chronic pain states are unknown. However, measures of pain perception and a wide range of psychological health measures have been linked to function of the default mode network (DMN). Thus, we aimed to determine the relationships between resilience, pain perception, and functional connectivity (FC) within the DMN and between the DMN and other brain networks. Resting-state functional magnetic resonance imaging data were acquired from 51 chronic pain patients with a form of spondylarthritis (ankylosing spondylitis) and 51 healthy control participants. Participants completed a questionnaire on their individual trait resilience (the Resilience Scale), and patients reported their clinical pain. In healthy controls, we found within-DMN FC to be stronger in less resilient individuals. In patients with chronic pain, individual resilience was negatively correlated with pain and disease activity. Cross-network FC between the DMN and the sensorimotor network was abnormally high in patients with high clinical pain scores on the day of the study. Finally, there was an interaction between within-DMN FC and clinical pain report in patients: In patients reporting greater pain, the relationship between within-DMN connectivity and resilience was atypical. Thus, our findings reveal different neural representations of resilience and pain. The way in which these behavioural measures interact provides insight into understanding the neural correlates of 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".