Cross-network coupling of neural oscillations in the dynamic pain connectome reflects chronic neuropathic pain in multiple sclerosis
Bibliographic record
Abstract
• Abnormal broadband cross-network brain communication in MS-related chronic pain. • Abnormal communication found between the salience network and other networks. • Neuropathic and non-neuropathic pain showed different patterns of abnormality. • Abnormal brain communication was linked to pain intensity and pain interference. • Alpha, beta and gamma bands may play a significant role in chronic pain. Sensory perceptions are coded by complex neural dynamics of regional communication in the brain. Thus, sensory abnormalities such as chronic pain may occur when neural dynamics go awry. Previous studies of cross-network dynamic functional connectivity in chronic pain identified abnormalities but were based on functional MRI which only captures slow temporal features. Here we conducted a magnetoencephalography (MEG) study to investigate fine temporal dynamics of aberrant cross-regional and cross-network communication of the dynamic pain connectome in patients with chronic pain. We also introduced a novel measure, dynamic functional coupling, to quantify the variability of brain communication. The study was performed in 33 people who had chronic pain associated with multiple sclerosis and 30 healthy controls. We found that patients with chronic pain exhibited abnormalities in cross-network functional coupling across multiple frequency bands (theta, alpha, beta, gamma), between the salience network and 3 other networks: the ascending nociceptive pathway, descending anti-nociceptive pathway, and the default mode network. However, these cross-network abnormalities involved different frequency bands in patients with neuropathic versus non-neuropathic chronic pain. Furthermore, cross-network abnormalities were linked to pain severity and pain interference. Our findings implicate broadband cross-network abnormalities as hallmark features of chronic pain in multiple sclerosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.245 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".