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Brain imaging-based machine learning in autism spectrum disorder: methods and applications

文献类型：期刊论文

作者Xu, Ming1,2,3; Calhoun, Vince4,5; Jiang, Rongtao1,2; Yan, Weizheng4,5; Sui, Jing6
刊名JOURNAL OF NEUROSCIENCE METHODS
出版日期2021-09-01
卷号361页码:14
ISSN号0165-0270
关键词Autism spectrum disorder (ASD) Machine learning Classification Prediction fMRI sMRI dMRI
DOI10.1016/j.jneumeth.2021.109271
通讯作者Sui, Jing(jsui@bnu.edu.cn)
英文摘要Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood onset and high het-erogeneity. As the pathogenesis is still elusive, ASD diagnosis is comprised of a constellation of behavioral symptoms. Non-invasive brain imaging techniques, such as magnetic resonance imaging (MRI), provide a valuable objective measurement of the brain. Many efforts have been devoted to developing imaging-based diagnostic tools for ASD based on machine learning (ML) technologies. In this survey, we review recent ad-vances that utilize machine learning approaches to classify individuals with and without ASD. First, we provide a brief overview of neuroimaging-based ASD classification studies, including the analysis of publications and general classification pipeline. Next, representative studies are highlighted and discussed in detail regarding different imaging modalities, methods and sample sizes. Finally, we highlight several common challenges and provide recommendations on future directions. In summary, identifying discriminative biomarkers for ASD diagnosis is challenging, and further establishing more comprehensive datasets and dissecting the individual and group heterogeneity will be critical to achieve better ADS diagnosis performance. Machine learning methods will continue to be developed and are poised to help advance the field in this regard.
WOS关键词DEEP NEURAL-NETWORK ; FUNCTIONAL CONNECTIVITY ; DEVELOPMENTAL TRAJECTORIES ; DIAGNOSTIC PREDICTION ; ACCURATE DETECTION ; FEATURE-SELECTION ; SYMPTOM SEVERITY ; CLASSIFICATION ; CHILDREN ; MRI
资助项目National Natural Science Foundation of China[82022035] ; National Natural Science Foundation of China[61773380] ; Beijing Municipal Science and Technology Commission, China[Z181100001518005] ; National Institute of Health, USA[1R01MH117107] ; National Institute of Health, USA[R01EB005846] ; National Institute of Health, USA[P20GM103472]
WOS研究方向Biochemistry & Molecular Biology ; Neurosciences & Neurology
语种英语
出版者ELSEVIER
WOS记录号WOS:000684103700004
资助机构National Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission, China ; National Institute of Health, USA
源URL[http://ir.ia.ac.cn/handle/173211/45691]  
专题自动化研究所_脑网络组研究中心
通讯作者Sui, Jing
作者单位1.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Georgia State Univ, Georgia Inst Technol, Triinst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA 30303 USA
5.Emory Univ, Atlanta, GA 30303 USA
6.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing 100088, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Ming,Calhoun, Vince,Jiang, Rongtao,et al. Brain imaging-based machine learning in autism spectrum disorder: methods and applications[J]. JOURNAL OF NEUROSCIENCE METHODS,2021,361:14.
APA Xu, Ming,Calhoun, Vince,Jiang, Rongtao,Yan, Weizheng,&Sui, Jing.(2021).Brain imaging-based machine learning in autism spectrum disorder: methods and applications.JOURNAL OF NEUROSCIENCE METHODS,361,14.
MLA Xu, Ming,et al."Brain imaging-based machine learning in autism spectrum disorder: methods and applications".JOURNAL OF NEUROSCIENCE METHODS 361(2021):14.

入库方式： OAI收割

来源：自动化研究所

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