Convergent structural brain alterations in chronic pain: A multi-metric individual participant data meta-analysis
Bibliographic record
Abstract
Chronic pain is a leading contributor to all-cause morbidity and disability, encompassing numerous biopsychosocial dimensions that persistently engage complex networks of brain regions. Meta-analyses have advanced our understanding of structural brain differences in chronic pain but rely exclusively on summary statistics which may introduce heterogeneity related to completeness of reporting and differences in methodological approaches. To address these limitations, we conducted the first individual participant data (IPD) meta-analysis of brain structure alterations in chronic pain. Using traditional morphometric measures (i.e. volume, cortical thickness, and surface area) and differential-geometric shape metrics (i.e. intrinsic and extrinsic curvature), we aimed to reveal alterations in brain structure convergent across chronic pain conditions. We hypothesized that chronic pain would be associated with region-specific grey matter reductions in regions previously implicated in chronic pain (e.g. parahippocampal gyrus and insula) and explored whether curvature metrics would reveal additional structural changes. Anatomical MRI images from eight publicly available datasets spanning five conditions and 401 individuals with chronic pain (and 245 age- and sex- matched healthy controls) were analysed: (i) knee osteoarthritis, (ii) chronic low back pain, (iii) fibromyalgia, (iv) migraine, and (v) primary trigeminal neuralgia. FreeSurfer was used to parcellate T1-weighted anatomical images, and metrics for cortical and subcortical regions were extracted. Meta-analysis revealed a range of structural changes in the brain associated with chronic pain. Cortical thinning and volume loss were small and localized to the temporo-occipital regions, including bilateral volumetric reductions in the entorhinal cortex in individuals with chronic pain. Increases in intrinsic curvature were widespread, involving 49 out of 68 cortical regions. No significant alterations were detected in subcortical volumes. Intrinsic curvature and subcortical volumetric estimates had higher levels of inter-study heterogeneity compared to other metrics, reflecting potential condition and sample-specific variability. Leveraging harmonized processing across a large sample size, our novel IPD meta-analysis highlights both widespread and region-specific structural remodelling of chronic pain-related neuroanatomy.
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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 | 0.033 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.039 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".