Multi-network dynamical structure of the human brain in the setting of chronic pain: a coordinate-based meta-analysis
Bibliographic record
Abstract
The treatment of chronic pain represents a widespread clinical challenge. Current approaches to network-based mapping of the cerebral cortex have the potential to localize chronic pain in the brain. In an effort to further characterize the dynamical brain networks, or the 'dynome' in the setting of chronic pain, we performed a Coordinate-Based Meta-Analysis of resting-state functional Magnetic Resonance Imaging studies on chronic pain to create a multinetwork dynome of chronic pain. A cluster-level analysis generated seven statistically significant activation likelihood estimates (ALEs): one for chronic pain as a whole dynome, three for chronic pain conditions, and three for chronic pain mechanisms. Chronic pain is a complex disease process involving tripartite network dysfunction encompassing the Default Mode Network, Central Executive Network and Salience Network. Chronic visceral pain was distinct from chronic headache and chronic musculoskeletal pain, and chronic pain mechanisms have the potential to share common cortical network rearrangements with their respective chronic pain conditions. Collectively, this work represents the first anatomically specific network-based cortical map of chronic pain, with representation of disease-specific and mechanism-specific disruptions in cortical function.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.017 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".