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

•Overview of deep learning basic concepts: architecture, learning and testing.•Literature review of deep learning in neuroimaging studies of brain-based disorders.•Discussion about future research and challenges of deep learning in neuroimaging. Deep learning (DL) is a family of machine learning met...

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Vydáno v:Neuroscience and biobehavioral reviews Ročník 74; číslo Pt A; s. 58 - 75
Hlavní autoři: Vieira, Sandra, Pinaya, Walter H.L., Mechelli, Andrea
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Elsevier Ltd 01.03.2017
Témata:
Autoencoders
Convolutional neural networks
Deep belief networks
Deep learning
Humans
Machine Learning
Mental Disorders
Multilayer perceptron
Nervous System Diseases
Neural Networks, Computer
Neuroimaging
Neurologic disorders
Pattern recognition
Psychiatric disorders
Speech
Deep learning
Neuroimaging
Autoencoders
Machine learning
Deep belief networks
Multilayer perceptron
Pattern recognition
Convolutional neural networks
Neurologic disorders
Psychiatric disorders
ISSN:0149-7634, 1873-7528
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Shrnutí:•Overview of deep learning basic concepts: architecture, learning and testing.•Literature review of deep learning in neuroimaging studies of brain-based disorders.•Discussion about future research and challenges of deep learning in neuroimaging. 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.
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ISSN:0149-7634
1873-7528
DOI:10.1016/j.neubiorev.2017.01.002