Connectomic Mapping of Chronic Musculoskeletal Pain: Neural Circuitries Identified Through a Systematic Review and ALE Meta-Analysis
Bibliographic record
Abstract
Functional neuroimaging of the encephalon of humans with chronic musculoskeletal pain (CMP) has demonstrated functional alterations in the neurophysiological properties of cortical and subcortical circuits. Nevertheless, the current knowledge on specific neural circuitries that may occur in different CMP subgroups is limited, which in turn limits the understanding of the encephalon mechanisms associated with persistent pain and clinical heterogeneity. This PROSPERO-registered (CRD42022382309) systematic review and activation likelihood estimation meta-analysis of observational fMRI studies on human encephalon aimed to characterize specific patterns of connectomic reorganization in different CMP subgroups. PubMed, Web of Science, and Scopus databases were searched. Two independent reviewers read titles and abstracts, full texts, assessed methodological quality using the Newcastle–Ottawa scale, and extracted X, Y, and Z coordinates. The data analyses were conducted using the GingerALE 3.0.2 software and complemented by frequency analyses. A 95% confidence interval for the Family-wise error rate was applied using an initial uncorrected voxel-level threshold of p<0.001, together with 1,000 permutation tests and a minimum cluster volume of ≥200 mm³. In total, 43 studies out of 5,543 records met the inclusion criteria and none presented a very high risk of bias. The seven encephalon regions comprising the basic neural circuitry of CMP (somatosensory cortex, motor cortex, anterior cingulate cortex, insula, prefrontal cortex, thalamus, and cerebellum) correspond to a connectomic hub that coordinates sensory-motor discriminative, affective-emotional, and cognitive-motivational processing. Additionally, 13 other regions are specifically recruited in CMP subgroups. This dynamic reorganization indicates a pattern of compensatory neuroplasticity with maladaptive mechanisms.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".