title: Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study.
abstract: Autism spectrum disorder (ASD) is a neurodevelopment disease characterized by impairment of social interaction, language, behavior, and cognitive functions. Up to now, many imaging-based methods for ASD diagnosis have been developed. For example, one may extract abundant features from multi-modality images and then derive a discriminant function to map the selected features toward the disease label. A lot of recent works, however, are limited to single imaging centers. To this end, we propose a novel multi-modality multi-center classification (M3CC) method for ASD diagnosis. We treat the classification of each imaging center as one task. By introducing the task-task and modality-modality regularizations, we solve the classification for all imaging centers simultaneously. Meanwhile, the optimal feature selection and the modeling of the discriminant functions can be jointly conducted for highly accurate diagnosis. Besides, we also present an efficient iterative optimization solution to our formulated problem and further investigate its convergence. Our comprehensive experiments on the ABIDE database show that our proposed method can significantly improve the performance of ASD diagnosis, compared to the existing methods. Hum Brain Mapp 38:3081-3097, 2017. © 2017 Wiley Periodicals, Inc.
authors.affiliation: School of Digital Media, Jiangnan University, Wuxi, Jiangsu, 214122, China.|Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.
authors.forename: Jun
authors.identifier: 0000-0001-9548-0411
authors.initials: J
authors.lastname: Wang
authors.affiliation: Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.
authors.forename: Jialin
authors.initials: J
authors.lastname: Peng
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.
authors.forename: Dong
authors.initials: D
authors.lastname: Nie
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.
authors.forename: Feng
authors.initials: F
authors.lastname: Zhao
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.
authors.forename: Minjeong
authors.initials: M
authors.lastname: Kim
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.
authors.forename: Han
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, 119077.
authors.forename: Chong-Yaw
authors.initials: CY
authors.lastname: Wee
authors.affiliation: School of Digital Media, Jiangnan University, Wuxi, Jiangsu, 214122, China.
authors.forename: Shitong
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599.|Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000293:Adolescent; D000465:Algorithms; D000067877:Autism Spectrum Disorder / Q000145:classification* / Q000000981:diagnostic imaging*; D002648:Child; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results
keywords: autism spectrum disorders; feature selection; modality-modality relation; multi-modality data; multitask learning; task-task relation