CASIA OpenIR  > 脑网络组研究中心
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, Mohammad R.1,2; Plis, Sergey1; Sui, Jing1,3,4; Calhoun, Vince D.1,5
Source PublicationNEUROIMAGE
2017-01-15
Volume145Pages:137-165
SubtypeArticle
AbstractNeuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead. (C) 2016 Elsevier Inc. All rights reserved.
KeywordNeuroimaging Machine Learning Classification Brain Disorders Prediction
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.neuroimage.2016.02.079
WOS KeywordMILD COGNITIVE IMPAIRMENT ; SUPPORT VECTOR MACHINE ; RESTING-STATE FMRI ; MAJOR DEPRESSIVE DISORDER ; ATTENTION-DEFICIT/HYPERACTIVITY DISORDER ; DEFICIT HYPERACTIVITY DISORDER ; ALZHEIMERS-DISEASE DIAGNOSIS ; FUNCTIONAL NETWORK CONNECTIVITY ; AUTISM SPECTRUM DISORDER ; DIFFUSION TENSOR IMAGES
Indexed BySCI ; SSCI
Language英语
Funding OrganizationNational Institutes of Health(P20GM103472 ; NSF EPSCoR grant(1539067) ; "100 Talents Plan" of Chinese Academy of Sciences ; Chinese National Science Foundation(81471367) ; State High-Tech Development Plan (863)(2015AA020513) ; R01EB005846 ; R01EB020407 ; 1R01DA040487)
WOS Research AreaNeurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectNeurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000390976200002
Citation statistics
Cited Times:175[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13384
Collection脑网络组研究中心
Affiliation1.Mind Res Network, Albuquerque, NM 87106 USA
2.Geisinger Hlth Syst, Danville, PA 17822 USA
3.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
5.Univ New Mexico, Dept ECE, Albuquerque, NM 87131 USA
Recommended Citation
GB/T 7714
Arbabshirani, Mohammad R.,Plis, Sergey,Sui, Jing,et al. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls[J]. NEUROIMAGE,2017,145:137-165.
APA Arbabshirani, Mohammad R.,Plis, Sergey,Sui, Jing,&Calhoun, Vince D..(2017).Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.NEUROIMAGE,145,137-165.
MLA Arbabshirani, Mohammad R.,et al."Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls".NEUROIMAGE 145(2017):137-165.
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