Segregation of dynamic resting-state reward, default mode and attentional networks after remitted patients transition into a recurrent depressive episode

IntroductionRecurrence in major depression disorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most approaches focus on stable (vs. dynamic) network characteristics. We investigated how the brains dynamical repertoire changes after patients transition from remission to recurrence of a new depressive episode. MethodsSixty drug-free, MDD-patients with [≥]2 episodes underwent a baseline resting-state fMRI scan when in remission. Over 30-months follow-up, 11 patients with a recurrence and 17 matched-remitted MDD-patients without a recurrence underwent a second fMRI scan. Recurrent patterns of functional connectivity were characterized by applying leading eigenvector dynamics analysis (LEiDA). Differences between baseline and follow-up were identified for the 11 non-remitted patients, while data from the 17 matched-remitted patients was used as a validation dataset. ResultsAfter the transition into a depressive state, the reward and a visuo-attentional networks were detected significantly more often, whereas default mode network activity was found to have a longer duration. Additionally, the fMRI signal in the areas underlying the reward network were significantly less synchronized with the rest of the brain after recurrence (compared to a state of remission). These changes were not observed in the matched-remitted patients who were scanned twice while in remission. ConclusionThese findings characterize the changes that are specifically associated with the transition from remission to recurrence and provide first evidence of increased segregation in the brains dynamical repertoire when a recurrent depressive episode occurs.