Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications

NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS

卷:74

Review

Vieira, Sandra¹  Pinaya, Walter H. L.²  Mechelli, Andrea¹ 

[1] Kings Coll London, Dept Psychosis Studies, Inst Psychiat Psychol & Neurosci, 16 De Crespigny Pk, London SE5 8AF, England

[2] Univ Fed ABC, Ctr Math Computat & Cognit, Rua Arcturus, BR-09606070 Sao Bernardo Do Campo, SP, Brazil

关键词: Deep learning;    Machine learning;    Neuroimaging;    Pattern recognition;    Multilayer perceptron;    Autoencoders;    Convolutional neural networks;    Deep belief networks;    Psychiatric disorders;    Neurologic disorders;   

DOI  :  10.1016/j.neubiorev.2017.01.002

来源: Elsevier

【 摘 要 】

Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition. DL differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecutive nonlinear transformations, achieving increasingly higher levels of abstraction and complexity. Given its ability to detect abstract and complex patterns, DL has been applied in neuroimaging studies of psychiatric and neurological disorders, which are characterised by subtle and diffuse alterations. Here we introduce the underlying concepts of DL and review studies that have used this approach to classify brain-based disorders. The results of these studies indicate that DL could be a powerful tool in the current search for biomarkers of psychiatric and neurologic disease. We conclude our review by discussing the main promises and challenges of using DL to elucidate brain-based disorders, as well as possible directions for future research. (C) 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.orgilicenses/by/4.0/).

【 授权许可】

Free