Alpha oscillations are dysrhythmic in Fragile X syndrome

Background: Alpha oscillations are dominant rhythms in the human brain, supporting inhibitory control and coordination of neural activity. Altered alpha dynamics are observed across many neuropsychiatric and neurodevelopmental disorders, including Fragile X syndrome (FXS), the most common monogenic cause of autism and intellectual disability. FXS exhibits paradoxical alpha power: elevated absolute but reduced relative power. To resolve this incongruity, we considered that conventional power metrics, relying on averaging, may obscure the underlying critical temporal dynamics of such neural rhythms. Methods: Here, we investigate alpha oscillations in FXS as a model to decompose nonspecific alpha abnormalities into underlying temporal features. We used cycle-by-cycle (bycycle) alpha burst analysis from source-localized resting-state EEG of 70 individuals with FXS and 71 age- and sex-matched typically developing controls. Statistical modeling examined group, sex, and regional differences in alpha burst features using generalized linear mixed-effects models. Results: We reveal that alpha bursts in FXS show reduced count only in males, prolonged periods across sexes, and elevated amplitudes, particularly in males. Spatial mapping identified differential circuit vulnerability: timing-associated dysregulation in cognitive-control regions and amplitude elevations in sensory cortices. Within the FXS group, global alpha burst amplitude correlated with hyperactivity symptoms and inversely with general intelligence scores, and burst count correlated with age. Limitations: This study is limited by its resting-state design and cross-sectional nature. Future studies should explore task-based modulation of alpha burst features and longitudinal trajectories in FXS. Additionally, fragile X messenger ribonucleoprotein (FMRP) was not quantified for participants, limiting potential stratification by molecular severity. Conclusions: These findings resolve paradoxical alpha power in FXS into features consistent with interneuron dysfunction, demonstrating the potential for burst-level decomposition in mechanistic hypothesis generation and biomarker development across neurodevelopmental and neuropsychiatric disorders.