Magnetoencephalography oscillation-based functional connectivity identifies clinically relevant depression phenotypes

Heterogeneity in clinical presentation and mechanisms of major depressive disorder (MDD) probably contributes to limited responses to current treatments in many patients. Identifying biologically meaningful phenotypes would constitute a major step towards the development of personalized treatment approaches. Brain-activity-based phenotyping offers a promising route toward this goal. In particular, brain oscillations—rhythmic patterns of neural activity that support information processing—have been implicated in depression but have not previously been used to define biological phenotypes of the disorder. Yet, no studies have used brain oscillations to identify biological depression phenotypes. Here we report data-driven identification of oscillation phenotypes for MDD. We conducted a cross-sectional study and collected resting-state magnetoencephalography (MEG), structural magnetic resonance imaging and clinical symptom data from 263 patients with MDD and 75 healthy controls. We assessed oscillation-based functional connectivity from source-reconstructed MEG data with two coupling-mode measures and computed their low-dimensional brain–symptom associations to obtain latent components. Using clustering methods on these components, we identified five depression phenotypes that were characterized by distinct spectral and spatial patterns and differentiated clinically unique symptom profiles. These findings suggest that MEG-based oscillatory connectivity captures clinically relevant heterogeneity in MDD and provides candidate mechanistic phenotypes for future validation and treatment-stratification studies.