  Machine learning (ML) techniques have gained popularity in the neuroimaging
field due to their potential for classifying neuropsychiatric disorders.
However, the diagnostic predictive power of the existing algorithms has been
limited by small sample sizes, lack of representativeness, data leakage, and/or
overfitting. Here, we overcome these limitations with the largest multi-site
sample size to date (n=5,356) to provide a generalizable ML classification
benchmark of major depressive disorder (MDD). Using brain measures from
standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify
MDD vs healthy controls (HC) with around 62% balanced accuracy, but when
harmonizing the data using ComBat balanced accuracy dropped to approximately
52%. Similar results were observed in stratified groups according to age of
onset, antidepressant use, number of episodes and sex. Future studies
incorporating higher dimensional brain imaging/phenotype features, and/or using
more advanced machine and deep learning methods may achieve more encouraging
prospects.
