Distinct neural signaling characteristics between fibromyalgia and provoked vestibulodynia revealed by means of functional magnetic resonance imaging in the brainstem and spinal cord
Bibliographic record
Abstract
Introduction: Fibromyalgia and provoked vestibulodynia are two chronic pain conditions that disproportionately affect women. The mechanisms underlying the pain in these conditions are still poorly understood, but there is speculation that both may be linked to altered central sensitization and autonomic regulation. Neuroimaging studies of these conditions focusing on the brainstem and spinal cord to explore changes in pain regulation and autonomic regulation are emerging, but none to date have directly compared pain and autonomic regulation in these conditions. This study compares groups of women with fibromyalgia and provoked vestibulodynia to healthy controls using a threat/safety paradigm with a predictable noxious heat stimulus. Methods: Functional magnetic resonance imaging data were acquired at 3 tesla in the cervical spinal cord and brainstem with previously established methods. Imaging data were analyzed with structural equation modeling and ANCOVA methods during: a period of noxious stimulation, and a period before the stimulation when participants were expecting the upcoming pain. Results: The results demonstrate several similarities and differences between brainstem/spinal cord connectivity related to autonomic and pain regulatory networks across the three groups in both time periods. Discussion: Based on the regions and connections involved in the differences, the altered pain processing in fibromyalgia appears to be related to changes in how autonomic and pain regulation networks are integrated, whereas altered pain processing in provoked vestibulodynia is linked in part to changes in arousal or salience networks as well as changes in affective components of pain regulation.
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 | 0.000 | 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) | 0.002 | 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".