  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=0.61,
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 the
predictive network belong to the default mode network. We further tested the
predictive potential of the established model by conducting two external
validations on (N1=53, N2=154). Results showed high significant correlations
between the predicted and the measured depression scale scores (r1= 0.49,
r2=0.37, p<0.001). These findings lay the foundation for developing a
generalizable and scientifically interpretable EEG network-based markers that
can ultimately support clinicians in a biologically-based characterization of
MDD.
