Multivariate pattern analysis reveals resting-state EEG biomarkers in fibromyalgia

Fibromyalgia (FM) involves widespread musculoskeletal pain and hypersensitivity, often accompanied by neurological, cognitive, and affective disturbances. Resting-state (RS) electroencephalography (EEG) 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 RS-EEG signals between FM 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 FM 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 EEG 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 accuracies exceeding 0.70 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 FM patients with accuracies 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 FM and suggest cumulative effects of prolonged symptom duration. RS-EEG combined with multivariate pattern analysis may support the development of biomarkers to improve diagnosis and guide treatment strategies in clinical settings.