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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Vydané v:	Human brain mapping Ročník 40; číslo 3; s. 944 - 954
Hlavní autori:	Pinaya, Walter H. L., Mechelli, Andrea, Sato, João R.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken, USA John Wiley & Sons, Inc 15.02.2019
Predmet:	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
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Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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