Frontiers in Neuroscience (Feb 2025)
Dynamic functional connectivity patterns predict early antidepressant treatment response in drug-naïve, first-episode adolescent MDD
Abstract
ObjectiveAdolescents with major depressive disorder (MDD) exhibit abnormal dynamic functional connectivity (dFC) patterns, but it remains unclear whether these aberrant dFC patterns are linked to antidepressant treatment. The aim of this study is to investigate whether dFC patterns will be changed by antidepressant treatment, as well as whether baseline dFC pattern could predict treatment response in adolescent MDD patients.MethodWe included 35 drug-naïve, first-episode MDD adolescents (age 14.40 ± 1.24; 8 males and 27 females) and 24 healthy controls (HCs, age 14.21 ± 1.41; 11 males and 13 females). All MDD adolescents received 6 weeks of antidepressant treatment. Resting state and T1 MRI data were collected in MDD adolescents before and after treatment and in HCs. Independent component analysis (ICA) was used to compare the different dFC pattern between MDD adolescents and HCs at baseline, as well as which between before and after treatment in MDD adolescents. Finally, Pearson correlation and multivariate linear regression analyses were used to explore the associations between dFC pattern and changed score of BDI in MDD adolescents.ResultsThe mean dFC value between right inferior frontal gyrus (IFG) and bilateral insular cortex (IC; right, r = −0.461, p-FDR = 0.012; left, r = −0.518, p-FDR = 0.007) at baseline were negatively correlated with BDI score reduction. The mean dFC value between left frontal pole (FP) and right superior parietal lobule (SPL) after treatment was positively correlated with BDI score reduction (r = 0.442, p-FDR = 0.014). And the mean dFC values between right IFG and bilateral IC (right, β = −1.563, p-FDR = 0.021; left, β = −1.868, p-FDR = 0.012) at baseline could predict antidepressant treatment response.ConclusionThese findings demonstrate that dFC patterns between some brain areas could be a prospective factor for predicting antidepressant treatment response.
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