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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\u2019 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>","DOI":"10.1155\/2020\/1357853","type":"journal-article","created":{"date-parts":[[2020,1,31]],"date-time":"2020-01-31T18:30:49Z","timestamp":1580495449000},"page":"1-9","source":"Crossref","is-referenced-by-count":82,"title":["Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1004-3613","authenticated-orcid":true,"given":"Ke","family":"Niu","sequence":"first","affiliation":[{"name":"Computer School, Beijing Information Science and Technology University, Beijing 100101, China"},{"name":"CAI, School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5821-4277","authenticated-orcid":true,"given":"Jiayang","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH 45221, USA"}]},{"given":"Yijie","family":"Pan","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Information Technology Application, CAS, Beijing, China"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[{"name":"Computational Bioscience Research Center (CBRC), Computer Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8901-1472","authenticated-orcid":true,"given":"Xueping","family":"Peng","sequence":"additional","affiliation":[{"name":"CAI, School of Computer Science, Faculty of Engineering and Information Technology, 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