Descending pain modulatory efficiency in healthy subjects is related to structure and resting connectivity of brain regions
Bibliographic record
Abstract
The descending pain modulatory system in humans is commonly investigated using conditioned pain modulation (CPM). Whilst variability in CPM efficiency, i.e., inhibition and facilitation, is normal in healthy subjects, exploring the inter-relationship between brain structure, resting-state functional connectivity (rsFC) and CPM readouts will provide greater insight into the underlying CPM efficiency seen in healthy individuals. Thus, this study combined CPM testing, voxel-based morphometry (VBM) and rsFC to identify the neural correlates of CPM in a cohort of healthy subjects (n =40), displaying pain inhibition (n = 29), facilitation (n = 10) and no CPM effect (n = 1). Clusters identified in the VBM analysis were implemented in the rsFC analysis alongside key constituents of the endogenous pain modulatory system. Greater pain inhibition was related to higher volume of left frontal cortices and stronger rsFC between the motor cortex and periaqueductal grey. Conversely, weaker pain inhibition was related to higher volume of the right frontal cortex - coupled with stronger rsFC to the primary somatosensory cortex, and rsFC between the amygdala and posterior insula. Overall, healthy subjects showed higher volume and stronger rsFC of brain regions involved with descending modulation, while the lateral and medial pain systems were related to greater pain inhibition and facilitation during CPM, respectively. These findings reveal structural alignments and functional interactions between supraspinal areas involved in CPM efficiency. Ultimately understanding these underlying variations and how they may become affected in chronic pain conditions, will advance a more targeted subgrouping in pain patients for future cross-sectional studies investigating endogenous pain modulation.
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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".