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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Published in:	Neuroscience and biobehavioral reviews Vol. 74; no. Pt A; pp. 58 - 75
Main Authors:	Vieira, Sandra, Pinaya, Walter H.L., Mechelli, Andrea
Format:	Journal Article
Language:	English
Published:	United States Elsevier Ltd 01.03.2017
Subjects:	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
Online Access:	Get full text
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Abstract	•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.
AbstractList	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. •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.
Author	Vieira, Sandra Pinaya, Walter H.L. Mechelli, Andrea
Author_xml	– sequence: 1 givenname: Sandra surname: Vieira fullname: Vieira, Sandra email: sandra.vieira@kcl.ac.uk organization: Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, SE5 8AF, United Kingdom – sequence: 2 givenname: Walter H.L. surname: Pinaya fullname: Pinaya, Walter H.L. organization: Centre of Mathematics, Computation, and Cognition, Universidade Federal do ABC, Rua Arcturus, Jardim Antares, São Bernardo do Campo, SP CEP 09.606-070, Brazil – sequence: 3 givenname: Andrea surname: Mechelli fullname: Mechelli, Andrea organization: Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28087243$$D View this record in MEDLINE/PubMed
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Keywords	Deep learning Neuroimaging Autoencoders Machine learning Deep belief networks Multilayer perceptron Pattern recognition Convolutional neural networks Neurologic disorders Psychiatric disorders
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Snippet	•Overview of deep learning basic concepts: architecture, learning and testing.•Literature review of deep learning in neuroimaging studies of brain-based... Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in...
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SubjectTerms	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
Title	Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications
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