Multivariate Pattern Analysis Identifies Potential IntertrialResting-State EEG Biomarkers in Fibromyalgia

Abstract

Fibromyalgia involves widespread musculoskeletal pain and hypersensitivity, often accompanied by neurological, cognitive, and affective disturbances. Resting-state electroencephalography studies have revealed abnormal brain activity in chronic pain conditions, with anxiety and symptom duration potentially exacerbating these alterations. This study applied multivariate pattern analysis to differentiate intertrial resting-state electroencephalography signals between fibromyalgia patients and healthy controls across frequency bands associated with pain processing, incorporating state and trait anxiety scores. It also examined differences between patients with short- and long-duration symptoms and identified the most relevant scalp regions contributing to the models. Fifty-one female participants (25 fibromyalgia patients, 26 controls; aged 35–65) were included. Patients were classified into short-term (12) and long-term (13) groups. Normalized power spectral density values were extracted from electroencephalography data and used to train machine learning classifiers, with Haufe-transformed weights computed to determine key scalp contributions. The models distinguished patients from controls with area under the curve values exceeding 0.75 across all frequency bands, reaching 0.99 in beta and gamma bands when anxiety was included. Symptom duration was also a relevant factor, as the model differentiated short- from long-term fibromyalgia patients with area under the curve values up to 0.96 in beta and gamma bands. Alterations in theta power within frontal and parietal regions, along with frequency-specific contributions, highlight disrupted pain processing in fibromyalgia and suggest cumulative effects of prolonged symptom duration. Future resting-state studies leveraging multivariate pattern analysis may support the development of potential biomarkers to improve diagnosis and guide treatment strategies in clinical settings.