  Schizophrenia is a chronic neuropsychiatric disorder that causes distinct
structural alterations within the brain. We hypothesize that deep learning
applied to a structural neuroimaging dataset could detect disease-related
alteration and improve classification and diagnostic accuracy. We tested this
hypothesis using a single, widely available, and conventional T1-weighted MRI
scan, from which we extracted the 3D whole-brain structure using standard
post-processing methods. A deep learning model was then developed, optimized,
and evaluated on three open datasets with T1-weighted MRI scans of patients
with schizophrenia. Our proposed model outperformed the benchmark model, which
was also trained with structural MR images using a 3D CNN architecture. Our
model is capable of almost perfectly (area under the ROC curve = 0.987)
distinguishing schizophrenia patients from healthy controls on unseen
structural MRI scans. Regional analysis localized subcortical regions and
ventricles as the most predictive brain regions. Subcortical structures serve a
pivotal role in cognitive, affective, and social functions in humans, and
structural abnormalities of these regions have been associated with
schizophrenia. Our finding corroborates that schizophrenia is associated with
widespread alterations in subcortical brain structure and the subcortical
structural information provides prominent features in diagnostic
classification. Together, these results further demonstrate the potential of
deep learning to improve schizophrenia diagnosis and identify its structural
neuroimaging signatures from a single, standard T1-weighted brain MRI.
