title: Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia.
abstract: 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.
authors.affiliation: Center of Mathematics, Computation, and Cognition. Universidade Federal do ABC, Santo André, Brazil.
authors.forename: Walter H L
authors.initials: WH
authors.lastname: Pinaya
authors.affiliation: Department of Psychiatry. Universidade Federal de São Paulo, São Paulo, Brazil.
authors.forename: Ary
authors.initials: A
authors.lastname: Gadelha
authors.affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
authors.forename: Orla M
authors.initials: OM
authors.lastname: Doyle
authors.affiliation: Department of Psychiatry. Universidade Federal de São Paulo, São Paulo, Brazil.
authors.forename: Cristiano
authors.initials: C
authors.lastname: Noto
authors.affiliation: Interdisciplinary Lab for Clinical Neurosciences (LiNC), Universidade Federal de Sao Paulo, Sao Paulo, Brazil.
authors.forename: André
authors.initials: A
authors.lastname: Zugman
authors.affiliation: Department of Psychiatry. Universidade Federal de São Paulo, São Paulo, Brazil.|Department of Psychiatry, Faculdade de Ciências Médicas da Santa Casa de São Paulo, São Paulo, Brazil.
authors.forename: Quirino
authors.initials: Q
authors.lastname: Cordeiro
authors.affiliation: Department of Psychiatry. Universidade Federal de São Paulo, São Paulo, Brazil.
authors.forename: Andrea P
authors.initials: AP
authors.lastname: Jackowski
authors.affiliation: Department of Psychiatry. Universidade Federal de São Paulo, São Paulo, Brazil.
authors.forename: Rodrigo A
authors.initials: RA
authors.lastname: Bressan
authors.affiliation: Center of Mathematics, Computation, and Cognition. Universidade Federal do ABC, Santo André, Brazil.|Department of Psychiatry. Universidade Federal de São Paulo, São Paulo, Brazil.
authors.forename: João R
authors.initials: JR
authors.lastname: Sato
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001921:Brain / Q000473:pathology*; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D016571:Neural Networks, Computer; D059906:Neuroimaging; D011618:Psychotic Disorders / Q000175:diagnosis; D012372:ROC Curve; D012559:Schizophrenia / Q000175:diagnosis / Q000473:pathology* / Q000503:physiopathology; D060388:Support Vector Machine