Journal
JOURNAL OF PSYCHIATRIC RESEARCH
Volume 165, Issue -, Pages 305-314Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jpsychires.2023.07.034
Keywords
Resting -state fMRI; Default mode network; Rumination; Remitted depression; Relapse prediction; Machine learning
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This study investigated the persistence of abnormalities in self-referential cognitions and functioning of associated brain networks in remitted recurrent MDD patients and their predictive value for relapse. The results showed no significant differences between remitted patients and controls in self-associations and resting-state functional connectivity. However, relapse was related to baseline functional connectivity, implicit self-associations, and uncontrollability of ruminative thinking. These findings suggest that variations in self-related processing play a role in the vulnerability to developing recurrent depressive episodes.
Background: The recurrent nature of Major Depressive Disorder (MDD) asks for a better understanding of mechanisms underlying relapse. Previously, self-referential processing abnormalities have been linked to vulnerability for relapse. We investigated whether abnormalities in self-referential cognitions and functioning of associated brain-networks persist upon remission and predict relapse. Methods: Remitted recurrent MDD patients (n = 48) and never-depressed controls (n = 23) underwent restingstate fMRI scanning at baseline and were additionally assessed for their implicit depressed self-associations and ruminative behaviour. A template-based dual regression approach was used to investigate between-group differences in default mode, cingulo-opercular and frontoparietal network resting-state functional connectivity (RSFC). Additional prediction of relapse status at 18-month follow-up was investigated within patients using both regression analyses and machine learning classifiers.Results: Remitted patients showed higher rumination, but no implicit depressed self-associations or RSFC abnormalities were observed between patients and controls. Nevertheless, relapse was related to i) baseline RSFC between the ventral default mode network and the precuneus, dorsomedial frontal gyrus, and inferior occipital lobe, ii) implicit self-associations, and iii) uncontrollability of ruminative thinking, when controlled for depressive symptomatology. Moreover, preliminary machine learning classifiers demonstrated that RSFC within the investigated networks predicted relapse on an individual basis. Conclusions: Remitted MDD patients seem to be commonly characterized by abnormal rumination, but not by implicit self-associations or abnormalities in relevant brain networks. Nevertheless, relapse was predicted by selfrelated cognitions and default mode RSFC during remission, suggesting that variations in self-relevant processing play a role in the complex dynamics associated with the vulnerability to developing recurrent depressive episodes.Clinical trial registration: Netherlands Trial Register, August 18, 2015, trial number NL53205.042.15.
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