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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Pinaya, Walter H. L. ; Gadelha, Ary ; Doyle, Orla M. ; Noto, Cristiano ; Zugman, Andre ; Cordeiro, Quirino ; Jackowski, Andrea P. ; Bressan, Rodrigo A. ; Sato, Joao R.
Total Authors: 9
Document type: Journal article
Source: SCIENTIFIC REPORTS; v. 6, DEC 12 2016.
Web of Science Citations: 25
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. (AU)

FAPESP's process: 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders
Grantee:João Ricardo Sato
Support type: Regular Research Grants
FAPESP's process: 13/05168-7 - Development of Deep Belief Networks to support the diagnosis of psychiatric disorders
Grantee:Walter Hugo Lopez Pinaya
Support type: Scholarships in Brazil - Doctorate