Neuropathic pain and pain interference are linked to alpha-band slowing and reduced beta-band magnetoencephalography activity within the dynamic pain connectome in patients with multiple sclerosis
Bibliographic record
Abstract
Chronic pain is a common occurrence in multiple sclerosis (MS) that severely affects quality of life, but the underlying brain mechanisms related to these symptoms are unknown. Previous electroencephalography studies have demonstrated a role of alpha-band and beta-band power in pain processing. However, how and where these brain signals change in MS-related chronic pain is unknown. Here, we used resting state magnetoencephalography to examine regional spectral power in the dynamic pain connectome-including areas of the ascending nociceptive pathway, default mode network (DMN), and the salience network (SN)-in patients with chronic MS pain and in healthy controls. Each patient was assessed for pain, neuropathic pain (NP), and pain interference with activities of daily living. We found that patients with MS exhibited an increase of alpha-band power and a decrease of beta-band power, most prominently in the thalamus and the posterior insula of the ascending nociceptive pathway and in the right temporoparietal junction of the SN. In addition, patients with mixed-NP exhibited slowing of alpha peak power within the thalamus and the posterior insula, and in the posterior cingulate cortex of the DMN. Finally, pain interference scores in patients with mixed-NP were strongly correlated with alpha and beta peak power in the thalamus and posterior insula. These novel findings reveal brain mechanisms of MS-related pain in the ascending nociceptive pathway, SN, and DMN, and that these spectral abnormalities reflect the impact of pain on quality of life measures.
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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 | 0.001 | 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".