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Emerging evidence showed that major depressive disorder (MDD) is associatedwith disruptions of brain structural and functional networks, rather thanimpairment of isolated brain region. Thus, connectome-based models capable ofpredicting the depression severity at the individual level can be clinicallyuseful. Here, we applied a machine-learning approach to predict the severity ofdepression using resting-state networks derived from source-reconstructedElectroencephalography (EEG) signals. Using regression models and threeindependent EEG datasets (N=328), we tested whether resting state functionalconnectivity could predict individual depression score. On the first dataset,results showed that individuals scores could be reasonably predicted (r=0.61,p=4 x 10-18) using intrinsic functional connectivity in the EEG alpha band(8-13 Hz). In particular, the brain regions which contributed the most to thepredictive network belong to the default mode network. We further tested thepredictive potential of the established model by conducting two externalvalidations on (N1=53, N2=154). Results showed high significant correlationsbetween the predicted and the measured depression scale scores (r1= 0.49,r2=0.37, p<0.001). These findings lay the foundation for developing ageneralizable and scientifically interpretable EEG network-based markers thatcan ultimately support clinicians in a biologically-based characterization ofMDD.

An Electroencephalography connectome predictive model of major depressive disorder severity