Brain network properties in chronic pain—a systematic review and meta-analysis of graph-based connectivity metrics
Bibliographic record
Abstract
Introduction Identifying brain topology alterations in chronic pain is a crucial step in understanding its pathophysiology. The primary objective of this systematic review and meta-analysis was to assess alterations in resting-state functional and structural global network properties in patients with chronic pain. Methods Following the preregistration (PROSPERO CRD42024542390), databases were searched for studies comparing connectivity-based whole-brain global network properties between patients with chronic pain and healthy controls. Risk of bias was assessed using an adapted Newcastle-Ottawa scale. Random-effect meta-analyses were conducted for each global network property separately. Results A total of 32 functional topology studies and 17 structural topology studies were included in the qualitative review, with 27 functional topology studies and 17 structural topology studies eligible for meta-analysis across nine unique structural and functional global network properties. The number of participants per meta-analysis ranged from 178 to 1,592. There was low-certainty evidence that chronic pain patients showed impairments in local efficiency of resting-state functional whole-brain topology (SMD: −0.50, 95%-CI: −0.81 to −0.19, 95%-PI: −1.38 to 0.38), and low to very low-certainty evidence that structural whole-brain topology was not altered in chronic pain across nine global network properties. The heterogeneity was high in the majority of functional (I 2 : 1–76%) and structural (I 2 : 68–97%) topology studies. Most functional (50%) and structural (65%) topology studies showed some concern regarding the risk of bias. Discussion The meta-analyses indicate that functional but not structural whole-brain topological reorganisation is involved in the pathophysiology 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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.027 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".