Conditioned pain modulation is associated with heightened connectivity between the periaqueductal grey and cortical regions
Bibliographic record
Abstract
Introduction: Conditioned pain modulation (CPM) is a psychophysical assessment used to estimate the efficiency of an individual's endogenous modulatory mechanisms. Conditioned pain modulation has been used as a predictive assessment for the development of chronic pain and responses to pain interventions. Although much is known about the spinal cord mechanisms associated with descending pain modulation, less is known about the contribution of supraspinal and especially cortical regions. Objectives: We aimed to explore how whole-brain connectivity of a core modulatory region, the periaqueductal grey (PAG), is associated with conditioned pain modulation, and endogenous pain modulation more broadly. Methods: We measured CPM and resting-state connectivity of 35 healthy volunteers, absent of chronic pain diagnoses. As a region of interest, we targeted the PAG, which is directly involved in endogenous modulation of input to the spinal cord and is a key node within the descending pain modulation network. Results: We found that CPM was associated with heightened connectivity between the PAG and key regions associated with pain processing and inhibition, such as the primary and secondary somatosensory cortices, as well as the motor, premotor, and dorsolateral prefrontal cortices. These findings are consistent with connectivity findings in other resting-state and event-related fMRI studies. Conclusion: These findings indicate that individuals who are efficient modulators have greater functional connectivity between the PAG and regions involved in processing pain. The heightened connectivity of these regions may contribute to the beneficial outcomes in clinical pain management, as quantified by CPM. These results may function as brain-based biomarkers for vulnerability or resilience to 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) | 0.004 | 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".