High resolution EEG microstate framework in fibromyalgia: An integrated multivariate and source localization approach to pain-related dynamics

Fibromyalgia (FM) is a nociplastic pain syndrome associated with altered central nervous system processing consistent with central sensitization, leading to enhanced pain perception. Despite its prevalence, the underlying neurophysiological mechanisms remain unclear. In this study, we characterized resting-state EEG (RS-EEG) dynamics in FM using a high resolution microstate (MS) framework combined with multivariate pattern analysis (MVPA) and source localization. 5-min eyes-closed RS-EEG was recorded from 57 female participants (29 FM patients and 28 healthy controls), and an 11-MS template was determined using the KL-GEV criterion. To assess the discriminative value of MS spatiotemporal features, we applied linear discriminant analysis with leave-one-out cross-validation. MS features were correlated with clinical measures, and source-level group differences were estimated using exact low-resolution electromagnetic tomography (eLORETA). MVPA identified MS G as the most discriminative state (AUC = 0.63), although it remained below commonly used thresholds for clinical interpretability (AUC > 0.75). Source-level analyses revealed significant differences in the insula (MS B and G), and in the middle frontal and postcentral gyri (MS E). No clinical associations survived correction for multiple comparisons, although exploratory trends suggested relationships between pain- and fatigue-related symptoms and MS B and E features. Overall, MVPA and eLORETA findings indicated altered spatiotemporal MS dynamics within somatosensory, salience, and executive networks in FM, consistent with central sensitization and dysfunction across large-scale pain networks. These findings highlight the potential of RS-EEG MS as candidate markers of pain-related neurophysiological heterogeneity within established FM, while their diagnostic utility remains to be established.