Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study

Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain‐based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for...

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Bibliographic Details
Published in:Human brain mapping Vol. 40; no. 3; pp. 944 - 954
Main Authors: Pinaya, Walter H. L., Mechelli, Andrea, Sato, João R.
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 15.02.2019
Subjects:
Adult
Anatomy
Artificial intelligence
Artificial neural networks
Autism
autism spectrum disorder
Autism Spectrum Disorder - diagnostic imaging
Black boxes
Brain
Brain - diagnostic imaging
Brain architecture
computational psychiatry
deep autoencoder
Deep Learning
Deviation
Disease control
Disorders
Female
Humans
Image Interpretation, Computer-Assisted - methods
Investigations
Learning algorithms
Machine learning
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Medical imaging
Mental disorders
Neural networks
Neuroimaging
Neuroimaging - methods
Neurology
Regional analysis
Schizophrenia
Schizophrenia - diagnostic imaging
structural MRI
computational psychiatry
deep learning
structural MRI
deep autoencoder
schizophrenia
autism spectrum disorder
ISSN:1065-9471, 1097-0193, 1097-0193
Online Access:Get full text
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Summary:Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain‐based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a “black box” that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain‐based disorders which aim to overcome these limitations. We used an artificial neural network known as “deep autoencoder” to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.
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Funding information Wellcome Trust, Grant/Award: 208519/Z/17/Z; Fundação de Amparo à Pesquisa do Estado de São Paulo, Grant/Award Number: 2013/05168‐7; Fundação de Amparo à Pesquisa do Estado de São Paulo, Grant/Award Number: 2013/10498‐6; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
ISSN:1065-9471
1097-0193
1097-0193
DOI:10.1002/hbm.24423