OBJECTIVE: We aimed to integrate neural data and an advanced machine learning 
technique to predict individual major depressive disorder (MDD) patient 
severity.
METHODS: MEG data was acquired from 22 MDD patients and 22 healthy controls (HC) 
resting awake with eyes closed. Individual power spectra were calculated by a 
Fourier transform. Sources were reconstructed via beamforming technique. 
Bayesian linear regression was applied to predict depression severity based on 
the spatial distribution of oscillatory power.
RESULTS: In MDD patients, decreased theta (4-8 Hz) and alpha (8-14 Hz) power was 
observed in fronto-central and posterior areas respectively, whereas increased 
beta (14-30 Hz) power was observed in fronto-central regions. In particular, 
posterior alpha power was negatively related to depression severity. The 
Bayesian linear regression model showed significant depression severity 
prediction performance based on the spatial distribution of both alpha (r=0.68, 
p=0.0005) and beta power (r=0.56, p=0.007) respectively.
CONCLUSIONS: Our findings point to a specific alteration of oscillatory brain 
activity in MDD patients during rest as characterized from MEG data in terms of 
spectral and spatial distribution.
SIGNIFICANCE: The proposed model yielded a quantitative and objective estimation 
for the depression severity, which in turn has a potential for diagnosis and 
monitoring of the recovery process.