T9. EXPLAINABLE DEEP LEARNING OF NEUROIMAGING REVEALS KEY STRUCTURAL DEFICITS IN SCHIZOPHRENIA - Yang, Albert; Tsai, Shih-Jen (Schizophrenia bulletin, 2019)
Abstract Background Deep neural network (DNN) has facilitated the record-breaking of classification accuracy in fields such as speech or visual object recognition. However, limited studies have investigated the applicability of DNN to three-dimensional neuroimage data, and the interpretation of deep learning model remains like a black box. Here, we present an explainable DNN framework to identify key structural deficits in schizophrenia. Methods Structural brain magnetic resonance images (MRI) were obtained from 200 schizophrenic patients and 200 age- and sex-matched healthy control subjects. The brain MRI images were normalized and segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) space. We introduced an original KL-L1 regularization method as a feature selection layer in the DNN to reduce dimensionality of neuroimage data and automatically identify key brain voxels without prior knowledge of brain pathology. Results The DNN classifier with KL-L1 regularization achieved an average test accuracy of 91.7% in WM, an average of 87.5% in GM, and 75.5% in CSF. The key GM voxels identified by the DNN were within brain regions including insula, precuneus, and superior temporal pole; WM voxels were associated with neural tracts, such as cingulum/hippocampus, splenium of corpus callosum, and posterior corona radiata. Discussion The present study shows that the DNN with KL-L1 regularization can identify key structural deficits that are effectively related to the known structural pathology of schizophrenia. We anticipate that this explainable deep learning approach may provide a useful framework for the search of objective biomarkers of mental illness in future studies.