Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

doi:10.1016/j.neuroimage.2016.02.079

	Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
	Arbabshirani, Mohammad R.1,2; Plis, Sergey 1; Sui, Jing1,3,4 ; Calhoun, Vince D.1,5
发表期刊	NEUROIMAGE
	2017-01-15
卷号	145 页码:137-165
文章类型	Article
摘要	Neuroimaging-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.
关键词	Neuroimaging Machine Learning Classification Brain Disorders Prediction
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
DOI	10.1016/j.neuroimage.2016.02.079
关键词[WOS]	MILD 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
收录类别	SCI ; SSCI
语种	英语
项目资助者	National 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研究方向	Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000390976200002
引用统计	被引频次：530[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13384
专题	脑图谱与类脑智能实验室_脑网络组研究
作者单位	1.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
推荐引用方式 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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