An electroencephalography connectome predictive model of major depressive disorder severity.

ABSTRACT: Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10^-18) using i

SUBMITTER: Kabbara A 

PROVIDER: S-EPMC9042869 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature