Although most knowledge regarding antidepressant effects is at the receptor level, the neurophysiological correlates of these neurochemical changes remain poorly understood. Such an understanding could benefit from elucidation of antidepressant effects at the level of neural circuits, which would be crucial in identifying biomarkers for monitoring treatment efficacy of antidepressants. In this study, we recruited 20 first-episode drug-naive major depressive disorder (MDD) patients and performed resting-state functional magnetic resonance imaging (MRI) scans before and after 8 weeks of treatment with a selective serotonin reuptake inhibitor-escitalopram. Twenty healthy controls (HCs) were also scanned twice with an 8-week interval. Whole-brain connectivity was analyzed using a graph-theory approach-functional connectivity strength (FCS). The analysis of covariance of FCS was used to determine treatment-related changes. We observed significant group-by-time interaction on FCS in the bilateral dorsomedial prefrontal cortex and bilateral hippocampi. Post hoc analyses revealed that the FCS values in the bilateral dorsomedial prefrontal cortex were significantly higher in the MDD patients compared to HCs at baseline and were significantly reduced after treatment; conversely, the FCS values in the bilateral hippocampi were significantly lower in the patients at baseline and were significantly increased after treatment. Importantly, FCS reduction in the dorsomedial prefrontal cortex was significantly correlated with symptomatic improvement. Together, these findings provided evidence that this commonly used antidepressant can selectively modulate the intrinsic network connectivity associated with the medial prefrontal-limbic system, thus significantly adding to our understanding of antidepressant effects at a circuit level and suggesting potential imaging-based biomarkers for treatment evaluation in MDD.
Keywords: antidepressant; connectomics; depression; graph theory; resting-state fMRI.
© 2014 Wiley Periodicals, Inc.