Suppressed prefrontal cortex oscillations associate with clinical pain in fibrodysplasia ossificans progressiva
Bibliographic record
Abstract
BACKGROUND: Pain is a highly prevalent symptom experienced by patients across numerous rare musculoskeletal conditions. Much remains unknown regarding the central, neurobiological processes associated with clinical pain in musculoskeletal disease states. Fibrodysplasia ossificans progressiva (FOP) is an inherited condition characterized by substantial physical disability and pain. FOP arises from mutations of the bone morphogenetic protein (BMP) receptor Activin A receptor type 1 (ACVR1) causing patients to undergo painful flare-ups as well as heterotopic ossification (HO) of skeletal muscles, tendons, ligaments, and fascia. To date, the neurobiological processes that underlie pain in FOP have rarely been investigated. We examined pain and central pain mechanism in FOP as a model primary musculoskeletal condition. Central nervous system (CNS) functional properties were investigated in FOP patients (N = 17) stratified into low (0-3; 0-10 Scale) and high (≥ 4) pain cohorts using functional near-infrared spectroscopy (fNIRS). Associations among clinical pain, mental health, and physical health were also quantified using responses derived from a battery of clinical questionnaires. RESULTS: Resting-state fNIRS revealed suppressed power of hemodynamic activity within the slow-5 frequency sub-band (0.01-0.027 Hz) in the prefrontal cortex in high pain FOP patients, where reduced power of slow-5, prefrontal cortex oscillations exhibited robust negative correlations with pain levels. Higher clinical pain intensities were also associated with higher magnitudes of depressive symptoms. CONCLUSIONS: Our findings not only demonstrate a robust coupling among prefrontal cortex functionality and clinical pain in FOP but lays the groundwork for utilizing fNIRS to objectively monitor and central pain mechanisms in FOP and other musculoskeletal disorders.
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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".