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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Veröffentlicht in:	Human brain mapping Jg. 40; H. 3; S. 944 - 954
Hauptverfasser:	Pinaya, Walter H. L., Mechelli, Andrea, Sato, João R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 15.02.2019
Schlagworte:	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-Zugang:	Volltext
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