Resting-State Functional Connectivity in Children and Adolescents with Major Depressive Disorder: A Deep Learning Approach Using High-density EEG

This research focuses on utilizing convolutional neural networks (CNNs) to identify biomarkers for major depressive disorder (MDD) in children and adolescents, compared to age-matched healthy individuals. We analyzed resting-state, eyes-closed electroencephalography (EEG) data, pre-processed and segmented by frequency bands and regions of interest (ROI). Several resting-state functional connectivity (rsFC) measurements were computed using a multi-variate auto-regressive (MVAR) model. The best-performing CNN model was further analyzed to understand its decision-making process and to identify relevant biomarkers. Our approach achieved an F1-Score of 0.790 and a Matthews correlation coefficient (MCC) of 0.745 using the full-frequency partial directed coherence (ffPDC) rsFC measurement. Among the connectivity metrics, partial directed coherence (PDC) outperformed coherence, partial coherence, and the directed transfer function (DTF). Additionally, the full-frequency versions of PDC and DTF demonstrated better performance compared to their standard and variant forms. These results highlight the potential of CNN models and EEG-derived biomarkers in advancing the understanding and diagnosis of MDD in children and adolescents.