Dynamic pain connectome functional connectivity and oscillations reflect multiple sclerosis pain
Bibliographic record
Abstract
Pain is a prevalent and debilitating symptom of multiple sclerosis (MS); yet, the mechanisms underlying this pain are unknown. Previous studies have found that the functional relationships between the salience network (SN), specifically the right temporoparietal junction a SN node, and other components of the dynamic pain connectome (default mode network [DMN], ascending and descending pathways) are abnormal in many chronic pain conditions. Here, we use resting-state functional magnetic resonance imaging and measures of static and dynamic functional connectivity (sFC and dFC), and regional BOLD variability to test the hypothesis that patients with MS have abnormal DMN-SN cross-network sFC, dFC abnormalities in SN-ascending and SN-descending pathways, and disrupted BOLD variability in the dynamic pain connectome that relates to pain inference and neuropathic pain (NP). Thirty-one patients with MS and 31 controls completed questionnaires to characterize pain and pain interference, and underwent a resting-state functional magnetic resonance imaging scan from which measures of sFC, dFC, and BOLD variability were compared. We found that (1) ∼50% of our patients had NP features, (2) abnormalities in SN-DMN sFC were driven by the mixed-neuropathic subgroup, (3) in patients with mixed NP, dFC measures showed that there was a striking change in how the SN was engaged with the ascending nociceptive pathway and descending modulation pathway, (4) BOLD variability was increased in the DMN, and (5) the degrees of sFC and BOLD variability abnormalities were related to pain interference. We propose that abnormal SN-DMN cross-network FC and temporal dynamics within and between regions of the dynamic pain connectome reflect MS pain features.
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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) | 0.001 | 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".