Weakened effective connectivity between salience network and default mode network during resting state in adolescent depression

Front Psychiatry. 2024 Apr 4:15:1386984. doi: 10.3389/fpsyt.2024.1386984. eCollection 2024.

Abstract

Adolescent major depressive disorder (MDD) is associated with altered resting-state connectivity between the default mode network (DMN) and the salience network (SN), which are involved in self-referential processing and detecting and filtering salient stimuli, respectively. Using spectral dynamical causal modelling, we investigated the effective connectivity and input sensitivity between key nodes of these networks in 30 adolescents with MDD and 32 healthy controls while undergoing resting-state fMRI. We found that the DMN received weaker inhibition from the SN and that the medial prefrontal cortex and the anterior cingulate cortex showed reduced self-inhibition in MDD, making them more prone to external influences. Moreover, we found that selective serotonin reuptake inhibitor (SSRI) intake was associated with decreased and increased self-inhibition of the SN and DMN, respectively, in patients. Our findings suggest that adolescent MDD is characterized by a hierarchical imbalance between the DMN and the SN, which could affect the integration of emotional and self-related information. We propose that SSRIs may help restore network function by modulating excitatory/inhibitory balance in the DMN and the SN. Our study highlights the potential of prefrontal-amygdala interactions as a biomarker and a therapeutic target for adolescent depression.

Keywords: SSRI; adolescence; affective disorders; brain connectivity; resting-state fMRI.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. The study was funded by the Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, the Olga Mayenfisch Foundation and the Swiss National Science Foundation (SNSF 33IC30_166826).