Journal Article

Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia

Noto, Cristiano, Zugman, André, Bressan, Rodrigo A., Cordeiro, Quirino, Jackowski, Andrea P., Sato, João R., Pinaya, Walter H. L., Gadelha, Ary, Doyle, Orla M.

2016

Overview

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.

• Publisher: Nature Publishing Group UK,Nature Publishing Group
• Subject: 639/705/117 / 692/699/476/1799 / Adult / Area Under Curve / Belief networks / Brain - pathology / Cerebellum / Corpus callosum / Deep learning / Female / Heterogeneity / Humanities and Social Sciences / Humans / Learning algorithms / Machine learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Male / Medical imaging / Mental disorders / Middle Aged / Models, Neurological / Morphometry / multidisciplinary / Neural Networks (Computer) / Neuroimaging / Psychosis / Psychotic Disorders - diagnosis / Putamen / ROC Curve / Schizophrenia / Schizophrenia - diagnosis / Schizophrenia - pathology / Schizophrenia - physiopathology / Science / Support Vector Machine