Antidepressant Treatment-Induced State-Dependent Reconfiguration of Emotion Regulation Networks in Major Depressive Disorder

Abstract

Deficits in emotion regulation are the main clinical features, common risk factors, and treatment-related targets for major depressive disorder (MDD). The neural bases of emotion regulation are moving beyond specific functions and emphasizing instead the integrative functions of spatially distributed brain areas that work together as large-scale brain networks, but it is still unclear whether the dynamic interactions among these emotion networks would be the target of clinical intervention for MDD. Data were collected from 70 MDD patients and 43 sex- and age-matched healthy controls. The dynamic functional connectivity (dFC) between emotion regions was estimated via a sliding-window method based on resting-state functional magnetic resonance imaging (R-fMRI). A k-means clustering method was applied to classify all time windows across all participants into several dFC states reflecting recurring functional interaction patterns among emotion regions over time. The results showed that four dFC states were identified in the emotion networks. Their alterations of state-related occurrence proportion were found in MDD and subsequently normalized following 12-week antidepressant treatment. Baseline strong dFC could predict the reduction rate of Hamilton Depression Rating Scale (HAMD) scores. These findings highlighted the state-dependent reconfiguration of emotion regulation networks in MDD patients owing to antidepressant treatment.

Prediction of remission among patients with a major depressive disorder based on the resting-state functional connectivity of emotion regulation networks

“…They demonstrated that the predicted reduction rate of HAMD scores based on the strength of baseline dFC was significantly correlated with the actual reduction rate of HAMD scores. This finding also illustrated the role of ER networks in predicting the efficacy of antidepressants [ 46 ]. The above finding and our study suggest a direction in which future studies could focus, namely on the features with potential prediction efficacy found by previous studies, rather than screening features in the whole brain range by univariate statistics.…”

“…First, despite the high accuracy achieved in the current study, whether the model can still achieve high accuracy on a dataset with a larger sample size or from multiple sites remains to be verified. However, combined with previous studies, we have reason to believe that this large-scale brain network contains rich efficacy prediction information [ 46 ]. Second, the patients in this study used only escitalopram as antidepressants, which ensure the homogeneity of our sample, but in the real world, the patients used far more kinds of drugs, which makes it more difficult for the model to accurately predict.…”

Sex differences of brain cortical structure in major depressive disorder

“…In MDD, antidepressant therapy was significantly associated with DLPFC abnormalities (Nestor et al ., 2022 ; Zhukovsky et al ., 2021 ). As networks implicated in the cognitive regulation of emotion, the frontoparietal network and subcortical regions, including the bilateral fusiform gyrus, are related to the state-dependent reconstruction of emotion regulation networks in individuals with MDD due to antidepressant treatment (Zhao et al ., 2021 ). Males with MDD showed enhanced neural responses to acute psychosocial stress in the DLPFC and right frontoparietal network compared with females (Dong et al ., 2022 ).…”