Synergistic Coactivation Probabilities of Large-Scale Resting-State Networks in Major Depressive Disorder

Major depressive disorder (MDD) involves subtle, distributed alterations across multiple large-scale resting-state brain networks (RSNs), highlighting the need for integrative approaches to uncover synergistic network patterns driving clinical symptoms.

In this study, we used a dynamic systems approach to investigate patterns of simultaneous RSN activation-i.e., coactivation-in 867 participants, including 487 healthy control participants (HCs), 175 patients with current MDD (cMDD), and 205 patients with remitted MDD (rMDD) from the Marburg-Münster Affective Disorders Cohort Study. Using a pairwise maximum entropy model, we estimated RSN coactivation probabilities based on resting-state fMRI data of 7 RSNs-default mode network (DMN), frontoparietal network (FPN), sensorimotor network (SMN), visual network (VIS), salience network, dorsal attention network (DAN), and language network (LAN)-capturing 128 possible states of coactivation.

General linear models revealed elevated coactivation probabilities in cMDD, particularly for states involving the DMN, FPN, and VIS, with the coactivation state involving the DMN, VIS, DAN, FPN, and LAN showing the strongest association with MDD diagnosis, clinical status, and symptom severity. Furthermore, canonical correlation analysis (CCA) on the total sample identified 2 distinct network-symptom profiles: canonical variate (CV) 1 linked high DMN and DAN coactivation probabilities to cognitive, insomnia, and mood/anhedonia symptoms, while CV2 tied the SMN and VIS to cognitive and somatic symptom domains.

These results demonstrate that MDD, especially during acute episodes, is marked by a dominance of DMN, FPN, and VIS coactivation, pointing to altered dynamic network organization. Further, the results highlight how changes in brain state dynamics are linked to MDD symptoms.