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Updated: Jun 9, 2025

Design and Implementation of an fMRI Study Examining Thought Suppression in Young Women with, and At-risk, for Depression
Published on: May 19, 2015
Structural alterations in a rumination-related network in patients with major depressive disorder
Paul Z Cheng1, Hsin-Chien Lee2, Timothy J Lane3
1Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.
Abstract:
Rumination is a common symptom in major depressive disorder (MDD). Previous work has connected individual differences in rumination to structural properties in various brain regions. Some of these, such as the dorsolateral prefrontal cortex (dlPFC), have also been highlighted as being altered in MDD, suggesting a connection between structural changes and ruminative symptoms. Although informative, such localised relations have limitations in the context of a network view of the brain. To further investigate rumination-related structural changes in depression, and to situate these within potential functional networks, we acquired T1-weighted structural MRI data from patients with MDD (n = 32) and controls (n = 69). Rumination was measured with the Rumination Response Scale. Surface-based, whole-brain analysis of cortical grey-matter identified group differences in the dlPFC that were, however, not related to rumination. Instead, rumination was correlated with grey-matter properties in the right precuneus. Using normative functional connectivity analysis on an independent sample (n = 100), we show these two regions to be interconnected. Further developing a network-based perspective, it was shown that the rumination-related precuneus region is connected with networks associated with processes such as executive function, autobiographical memory, and visual perception. Notably, these processes have been connected to rumination. These results suggest that rumination in depression may be linked to focal structural changes. The effects of these focal changes on rumination may then be connected to their influence on distributed functional networks.
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