Baseline Default Mode Network Functional Connectivity as a Predictor of SSRI Treatment Response in Adolescent Major Depressive Disorder v1

This protocol describes the preprocessing and analytic steps for examining whether baseline resting-state functional connectivity (rsFC) within the default mode network (DMN) predicts treatment response to selective serotonin reuptake inhibitors (SSRIs) in medication-naïve adolescents with major depressive disorder (MDD). Resting-state fMRI data are preprocessed using FreeSurfer 6.0 and AFNI 21.3.09, including removal of initial volumes, motion correction, normalization to MNI152_T1_2009c space, and 4 mm FWHM spatial smoothing. Noise reduction employs ANATICOR with white-matter and ventricular regressors and motion censoring (> 0.2 mm threshold). Functional connectivity maps are computed for three DMN seed regions (vMPFC, dMPFC, PCC). Voxel-wise regression analyses test the association between baseline DMN rsFC and 8-week changes in Children’s Depression Rating Scale–Revised scores following escitalopram treatment, controlling for age and sex. Cluster-level correction uses AFNI 3dClustSim (10 000 Monte Carlo iterations; voxel p < 0.005, FWE α < 0.017). This step-by-step protocol provides a transparent, reproducible workflow for identifying neural predictors of SSRI response in adolescent MDD.