Dynamical Exploration of Resting-State Attractors Altered in Major Depressive Disorder

Major depressive disorder (MDD) represents a heterogeneous condition lacking reliable neurobiological biomarkers and mechanistic understanding. Time-resolved characterisation of brain dynamics reveals that mental health is associated with a characteristic dynamical regime, exhibiting spontaneous switching between a repertoire of ghost attractor states forming resting-state networks. Analysing resting-state fMRI data from 848 MDD patients and 794 healthy controls across 17 sites in China (REST-meta-MDD) using Leading Eigenvector Dynamics Analysis (LEiDA), we found MDD patients exhibit significantly reduced default mode network (DMN) occupancy (p < 0.001; Hedges' g = −0.51) and increased occipito-parieto-temporal state occupancy (p < 0.001; Hedges' g = 0.42), suggesting compensatory dynamical rebalancing. These findings extend prior observations of disrupted DMN in MDD, aligning with the emerging dynamical systems framework for mental health to advance mechanistic understanding of MDD pathophysiology.