Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data Article Swipe
Ke Niu
,
Jiayang Guo
,
Yijie Pan
,
Xin Gao
,
Xueping Peng
,
Ning Li
,
Hailong Li
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/1357853
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1155/2020/1357853
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2020/1357853
- https://downloads.hindawi.com/journals/complexity/2020/1357853.pdf
- OA Status
- gold
- Cited By
- 100
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3003675036
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W3003675036Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2020/1357853Digital Object Identifier
- Title
-
Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-01-31Full publication date if available
- Authors
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Ke Niu, Jiayang Guo, Yijie Pan, Xin Gao, Xueping Peng, Ning Li, Hailong LiList of authors in order
- Landing page
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https://doi.org/10.1155/2020/1357853Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/complexity/2020/1357853.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/complexity/2020/1357853.pdfDirect OA link when available
- Concepts
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Generalizability theory, Autism spectrum disorder, Autism, Neuroimaging, Artificial intelligence, Computer science, Artificial neural network, Machine learning, Deep learning, Connectome, Psychology, Functional connectivity, Developmental psychology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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100Total citation count in OpenAlex
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2025: 16, 2024: 18, 2023: 22, 2022: 20, 2021: 14Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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