  We propose a novel deep neural network architecture to integrate imaging and
genetics data, as guided by diagnosis, that provides interpretable biomarkers.
Our model consists of an encoder, a decoder and a classifier. The encoder
learns a non-linear subspace shared between the input data modalities. The
classifier and the decoder act as regularizers to ensure that the
low-dimensional encoding captures predictive differences between patients and
controls. We use a learnable dropout layer to extract interpretable biomarkers
from the data, and our unique training strategy can easily accommodate missing
data modalities across subjects. We have evaluated our model on a population
study of schizophrenia that includes two functional MRI (fMRI) paradigms and
Single Nucleotide Polymorphism (SNP) data. Using 10-fold cross validation, we
demonstrate that our model achieves better classification accuracy than
baseline methods, and that this performance generalizes to a second dataset
collected at a different site. In an exploratory analysis we further show that
the biomarkers identified by our model are closely associated with the
well-documented deficits in schizophrenia.
