Enhanced Medial Prefrontal-Default Mode Network Functional Connectivity in Chronic Pain and Its Association with Pain Rumination
Bibliographic record
Abstract
Rumination is a form of thought characterized by repetitive focus on discomforting emotions or stimuli. In chronic pain disorders, rumination can impede treatment efficacy. The brain mechanisms underlying rumination about chronic pain are not understood. Interestingly, a link between rumination and functional connectivity (FC) of the brain's default mode network (DMN) has been identified within the context of mood disorders. We, and others, have also found DMN dysfunction in chronic pain populations. The medial prefrontal cortex (mPFC) is a key node of the DMN that is anatomically connected with the descending pain modulatory system. Therefore, we tested the hypothesis that in patients with chronic pain, the mPFC exhibits abnormal FC related to the patient's degree of rumination about their pain. Seventeen patients with idiopathic temporomandibular disorder (TMD) and 17 age- and sex-matched healthy controls underwent resting state functional MRI, and rumination about pain was assessed through the rumination subscale of the Pain Catastrophizing Scale. Compared with healthy controls, we found that TMD patients exhibited enhanced mPFC FC with other DMN regions, including the posterior cingulate cortex (PCC)/precuneus (PCu) and retrosplenial cortex. We also found that individual differences in pain rumination in the chronic pain patients (but not in healthy controls) were positively correlated to mPFC FC with the PCC/PCu, retrosplenial cortex, medial thalamus, and periaqueductal/periventricular gray. These data implicate communication within the DMN and of the DMN with the descending modulatory system as a mechanism underlying the degree to which patients ruminate about their chronic pain.
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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".