Deep Learning From Imaging Genetics for Schizophrenia Classification

Abstract

Schizophrenia (SZ) is a serious psychiatric disorder, causing substantial socioeconomic burden. Since an SZ patients’ brain may have structural changes including reduced hippocampal and thalamic volume [1], brain MRI is becoming a popular imaging method studying SZ. In addition, as a genetic disorder, genetic information such as single nucleotide polymorphisms (SNP) plays an important role in distinguishing SZ. However, the structural and genetic changes in SZ patients are too subtle to be identified by human vision, so it is necessary to develop an automated method to find the nonlinear patterns associated with disease progression. Toward this, we propose a novel multi-modal deep learning approach where we combine both features from structural MRI (sMRI) and single-nucleotide polymorphisms (SNPs) for SZ classification. For sMRI, we extract convolutional features from a pre-trained deep neural network to capture morphological characteristics. For SNPs, we apply a layer-wise relevance propagation (LRP) method on a pre-trained 1-D convolutional network to identify SZ-linked SNPs. We then feed the combined features to a tree-based classifier for SZ diagnosis. Experimental results on clinical dataset showed classification accuracy was increased by 5.3% compared to the state of the art DenseNet using only sMRI data.