Neural correlates of rumination in remitted depressive episodes: Brain network connectivity and topology analyses.

Abstract

Background: Rumination is a critical psychological factor contributing to the relapse of major depressive episodes (MDEs) and a core residual symptom in remitted MDEs. Investigating its neural correlations is essential for developing strategies to prevent MDE relapse. Despite its clinical importance, the brain network mechanisms underlying rumination in remitted MDE patients have yet to be fully elucidated.

Aim: To investigate the brain network mechanism underlying rumination in patients with remitted MDEs using functional magnetic resonance imaging (fMRI).

Methods: We conducted an fMRI-based rumination-distraction task to induce rumination and distraction states in 51 patients with remitted MDEs. Functional connectivity (FC) was analyzed using the network-based statistic (NBS) approach, and eight topological metrics were calculated to compare the network topological properties between the two states. Correlation analyses were further performed to identify the relationships between individual rumination levels and the significantly altered brain network metrics.

Results: The NBS analysis revealed that the altered FCs between the rumination and distraction states were located primarily in the frontoparietal, default mode, and cerebellar networks. No significant correlation was detected between these altered FCs and individual rumination levels. Among the eight topological metrics, the clustering coefficient, shortest path length, and local efficiency were significantly lower during rumination and positively correlated with individual rumination levels. In contrast, global efficiency was greater in the rumination state than in the distraction state and was negatively correlated with individual rumination levels.

Conclusion: Our work revealed the altered FC and topological properties during rumination in remitted MDE patients, offering valuable insights into the neural mechanisms of rumination from a brain network perspective.