Quantitative abnormalities of brain structure in patients with major depressive 
disorder have been reported at a group level for decades. However, these 
structural differences appear subtle in comparison with conventional 
radiologically defined abnormalities, with considerable inter-subject 
variability. Consequently, it has not been possible to readily identify scans 
from patients with major depressive disorder at an individual level. Recently, 
machine learning techniques such as relevance vector machines and support vector 
machines have been applied to predictive classification of individual scans with 
variable success. Here we describe a novel hybrid method, which combines machine 
learning with feature selection and characterization, with the latter aimed at 
maximizing the accuracy of machine learning prediction. The method was tested 
using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 
patients with major depressive disorder and matched controls were recruited from 
referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The 
generalization ability and predictive accuracy of the classifiers was tested 
using data left out of the training process. High prediction accuracy was 
achieved (~90%). While feature selection was important for maximizing high 
predictive accuracy with machine learning, feature characterization contributed 
only a modest improvement to relevance vector machine-based prediction (~5%). 
Notably, while the only information provided for training the classifiers was 
T(1)-weighted scans plus a categorical label (major depressive disorder versus 
controls), both relevance vector machine and support vector machine 'weighting 
factors' (used for making predictions) correlated strongly with subjective 
ratings of illness severity. These results indicate that machine learning 
techniques have the potential to inform clinical practice and research, as they 
can make accurate predictions about brain scan data from individual subjects. 
Furthermore, machine learning weighting factors may reflect an objective 
biomarker of major depressive disorder illness severity, based on abnormalities 
of brain structure.