TY - JOUR
T1 - Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing
AU - van Kleef, Rozemarijn S.
AU - Kaushik, Pallavi
AU - Besten, Marlijn
AU - Marsman, Jan Bernard C.
AU - Bockting, Claudi L.H.
AU - van Vugt, Marieke
AU - Aleman, André
AU - van Tol, Marie José
N1 - Funding Information:
The NEWPRIDE study is supported by a Dutch Research Council (NWO/ZonMW) grant (VENI grant number 016.156.077 ) and a Dutch Brain Foundation (Hersenstichting) grant (Fellowship grant number F2014(1)-21) in name of Marie-José van Tol. The funding sources had no involvement in conducting the research.
Publisher Copyright:
© 2023
PY - 2023/9
Y1 - 2023/9
N2 - 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.
AB - 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.
KW - Default mode network
KW - Machine learning
KW - Relapse prediction
KW - Remitted depression
KW - Resting-state fMRI
KW - Rumination
UR - http://www.scopus.com/inward/record.url?scp=85166951803&partnerID=8YFLogxK
U2 - 10.1016/j.jpsychires.2023.07.034
DO - 10.1016/j.jpsychires.2023.07.034
M3 - Article
AN - SCOPUS:85166951803
SN - 0022-3956
VL - 165
SP - 305
EP - 314
JO - Journal of Psychiatric Research
JF - Journal of Psychiatric Research
ER -