Neural synchrony reflects pain and co-occurring psychological symptoms: a transdiagnostic magnetoencephalography study using multivariate modeling
Bibliographic record
Abstract
Abstract Objective Pain commonly co-occurs with psychological symptoms, yet the neural synchrony patterns associated with this shared symptom burden remain incompletely understood. We examined whether frequency-specific neural synchrony is associated with pain severity and co-occurring psychological symptoms in a transdiagnostic military cohort with heterogeneous symptom presentations. Methods Resting-state magnetoencephalography data were acquired from military personnel and veterans exhibiting varying levels of pain, anxiety-, depression-, and PTSD-related symptoms. Frequency-specific neural synchrony was estimated between regions within literature-derived pain-relevant brain networks. Multivariate partial least squares regression was used to model associations between neural synchrony patterns and pain severity, as well as composite measures reflecting joint pain and psychological symptom burden. Results Pain severity was associated with distributed synchrony patterns, with the strongest associations observed in the beta and high-gamma frequency bands. Joint pain-anxiety symptom burden was primarily associated with theta- and gamma-band synchrony, whereas joint pain-depressive and pain-PTSD symptom burden showed predominant associations within the gamma band, with fewer beta-band effects. Across symptom domains, overlapping but frequency-specific synchrony patterns were identified across distributed brain networks. Conclusions Pain severity and co-occurring psychological symptoms are associated with partially overlapping, frequency-specific neural synchrony patterns involving distributed brain networks. Significance These findings support a network-level neurophysiological framework for understanding pain and psychological symptom co-occurrence and highlight frequency-specific MEG synchrony patterns as potential markers of multidimensional symptom burden. Highlights – MEG synchrony patterns associate with pain severity across beta and high-gamma bands – Pain-anxiety symptom burden links to theta- and gamma-band synchrony – Pain-depression and pain-PTSD burden show predominant gamma-band synchrony
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".