Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing

Rozemarijn S. van Kleef*, Pallavi Kaushik, Marlijn Besten, Jan Bernard C. Marsman, Claudi L.H. Bockting, Marieke van Vugt, André Aleman, Marie José van Tol

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

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 resting-state 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 self-related 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.

Original languageEnglish
Pages (from-to)305-314
Number of pages10
JournalJournal of Psychiatric Research
Volume165
Early online date7-Aug-2023
DOIs
Publication statusPublished - Sept-2023

Keywords

  • Default mode network
  • Machine learning
  • Relapse prediction
  • Remitted depression
  • Resting-state fMRI
  • Rumination

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