Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Niu, K; Guo, J; Pan, Y; Gao, X; Peng, X; Li, N; Li, H

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Niu, K

Guo, J

Pan, Y Gao, X Peng, X

Li, N

Li, H

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Publisher:: Hindawi Limited
Publication Type:: Journal Article
Citation:: Complexity, 2020, 2020 pp. 1 - 9
Issue Date:: 2020-01-31

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Field	Value	Language
dc.contributor.author	Niu, K https://orcid.org/0000-0003-1004-3613	en_US
dc.contributor.author	Guo, J https://orcid.org/0000-0002-5821-4277	en_US
dc.contributor.author	Pan, Y	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Peng, X https://orcid.org/0000-0002-8901-1472	en_US
dc.contributor.author	Li, N https://orcid.org/0000-0003-4533-1535	en_US
dc.contributor.author	Li, H https://orcid.org/0000-0002-5267-2875	en_US
dc.date.issued	2020-01-31	en_US
dc.identifier.citation	Complexity, 2020, 2020 pp. 1 - 9	en_US
dc.identifier.issn	1076-2787	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138316
dc.description.abstract	<jats:p>Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a <jats:italic>k</jats:italic>-fold cross validation experiment. Additional <jats:italic>k</jats:italic>-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.</jats:p>	en_US
dc.language	en	en_US
dc.publisher	Hindawi Limited	en_US
dc.relation.ispartof	Complexity	en_US
dc.relation.isbasedon	10.1155/2020/1357853	en_US
dc.subject.classification	Fluids & Plasmas	en_US
dc.title	Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data	en_US
dc.type	Journal Article
utslib.citation.volume	2020	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2020	en_US

Abstract:

Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

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http://hdl.handle.net/10453/138316