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

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

Affiliations

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

David Willinger et al. Front Psychiatry. .

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.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Spectral DCM analysis during resting state. (A) The common effect reflects the average connectivity and model structure across all participants. (B) Connectivity differences between controls and patients were found between amygdala and the core nodes of the DMN, as well as the self-inhibition parameters of MPFC and ACC. The latter regions were disinhibited (i.e. more sensitive to input) compared to healthy controls. (C) Patients taking SSRI showed decreased self-inhibition in the ACC and increased self-inhibition in the MPFC and PCC compared to patients not taking SSRIs. Detailled results are reported in Table 2 . ACC, anterior cingulate cortex; DMN, default mode network; HC, healthy controls; lAMY, left amygdala; MDD, major depressive disorder; MPFC, medial prefrontal cortex; PCC, posterior cingulate cortex; Pp, posterior probability; SSRI, selective serotonin reuptake inhibitor; rAMY, right amygdala; SN, salience network.
Figure 2
Figure 2
Predicting the diagnostic status using the spDCM connectivity parameters. The receiver operating characteristic (ROC) curve depicted here represents the outcome of a leave-one-out cross-validation procedure applied to the DCM analysis. The curve illustrates the trade-off between sensitivity and specificity for the predictive model across different thresholds. The area under the curve (AUC) serves as a statistical measure of the model’s ability to correctly classify a new participant as having MDD or not. An AUC of 1 indicates perfect predictive accuracy, whereas an AUC of 0.5 suggests no discriminative power, equivalent to random chance.

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