Abstract
BACKGROUND: Rumination is a maladaptive cognitive style and a risk factor for relapse of depression. However, the clinically relevant pattern of dynamic network reconfiguration during rumination in remitted depression and its implication in relapse remained unclear. METHODS: We employed a rumination induction neuroimaging paradigm in which subjects would be guided into an active rumination state and a distraction state. Forty-two patients with remitted depression were involved. Participants underwent assessments of rumination behavior and imaging tasks, and were then monitored for two year to assess the potential relapse of depression. A time-resolved community detection approach was applied to investigate the temporal dynamics of brain networks, and the dynamic network properties including flexibility and integration were analyzed. RESULTS: Logistic regression revealed that higher rumination levels during remission increased the risk of depression relapse. Neuroimaging analyses demonstrated significantly reduced integration between the fronto-parietal network and dorsal attention network during rumination compared to distraction state (t(40) = -3.05, P(FDR) = 0.036). Moreover, elastic net regression indicated that dynamic network features could predict two-year relapse outcomes with moderate accuracy (AUC = 0.70). CONCLUSIONS: Our findings reveal a potential mechanistic link between the brain network dynamics during rumination and relapse of depression, shedding light on the intricate relationship between cognitive-affective processes, neural dynamics, and the potential vulnerability to depression recurrence.