aceGraph theory-based machine learning for automated fibromyalgia diagnosis using fMRI during an emotional task
Background Fibromyalgia (FM) is a multifaceted chronic pain disorder presenting with pain throughout the musculoskeletal system, alongside chronic fatigue,sleep disturbances, and cognitive impairments. Current diagnostic criteria primarily rely on symptom questionnaires, with no specific laboratory markers or definitive tests, making diagnosis subjective and often delayed. Moreover, the overlap with other central pain syndromes further complicates accurate diagnosis. Neuroimaging studies, particularly functional…
A mixture-kernel support vector machine trained on graph-theory features from emotion-regulation brain regions best distinguished 32 fibromyalgia patients from 30 controls on sensitivity and AUC, suggesting a route to less subjective diagnosis.
The source states that small sample sizes make translating fMRI findings into reliable diagnostic tools challenging, and this study itself uses only 62 participants.
Evidence
- Peer-reviewedPLOS One2026-10-01
How should this claim be treated?
Truvace Impact Record TRV-2026-1463, v1: “Graph theory-based machine learning for automated fibromyalgia diagnosis using fMRI during an emotional task.” Truvace, 2026-10-15. /record/TRV-2026-1463 (accessed at citation time). sha256 7c29ae694a0311c3…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1463 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.