Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data

doi:10.1093/schbul/sby026

	Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data
	Madsen, Kristoffer H.1,2; Krohne, Laerke G.1,2; Cai, Xin-lu 3,5; Wang, Yi3 ; Chan, Raymond C. K.3,4,5
摘要	Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.
关键词	functional magnetic resonance imaging feature extraction neuroimaging schizotypy schi zophrenia spectrum disorder
	2018-11-01
语种	英语
DOI	10.1093/schbul/sby026
发表期刊	SCHIZOPHRENIA BULLETIN
ISSN	0586-7614
卷号	44 页码:S480-S490
资助项目	Beijing Municipal Science & Technology Commission[Z161100000216138] ; National Key Research and Development Programme[2016YFC0906402] ; Beijing Training Project for Leading Talents in ST[Z151100000315020] ; CAS Key Laboratory of Mental Health, Institute of Psychology
出版者	OXFORD UNIV PRESS
WOS关键词	RESTING-STATE FMRI ; SCHIZOPHRENIA-SPECTRUM DISORDERS ; FUNCTIONAL BRAIN IMAGES ; PARTIAL LEAST-SQUARES ; DEEP NEURAL-NETWORK ; PSYCHOSIS-PRONENESS ; HIGH-RISK ; NEURODEVELOPMENTAL DISORDER ; PSYCHOMETRIC SCHIZOTYPY ; PATTERN-CLASSIFICATION
WOS研究方向	Psychiatry
WOS类目	Psychiatry
WOS记录号	WOS:000448172600004
资助机构	Beijing Municipal Science & Technology Commission ; National Key Research and Development Programme ; Beijing Training Project for Leading Talents in ST ; CAS Key Laboratory of Mental Health, Institute of Psychology
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.psych.ac.cn/handle/311026/25687
专题	中国科学院心理健康重点实验室健康与遗传心理学研究室
通讯作者	Madsen, Kristoffer H.
作者单位	1.Univ Copenhagen, Danish Res Ctr Magnet Resonance, Ctr Funct & Diagnost Imaging & Res, Hosp Hvidovre, Hvidovre, Denmark 2.Tech Univ Denmark, Dept Appl Math & Comp Sci, Lyngby, Denmark 3.Chinese Acad Sci, CAS Key Lab Mental Hlth, Neuropsychol & Appl Cognit Neurosci Lab, Inst Psychol, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sinodanish Coll, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Madsen, Kristoffer H.,Krohne, Laerke G.,Cai, Xin-lu,et al. Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data[J]. SCHIZOPHRENIA BULLETIN,2018,44:S480-S490.
APA	Madsen, Kristoffer H.,Krohne, Laerke G.,Cai, Xin-lu,Wang, Yi,&Chan, Raymond C. K..(2018).Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.SCHIZOPHRENIA BULLETIN,44,S480-S490.
MLA	Madsen, Kristoffer H.,et al."Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data".SCHIZOPHRENIA BULLETIN 44(2018):S480-S490.

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Perspectives on Mach（479KB）	期刊论文	出版稿	限制开放	CC BY-NC-SA	请求全文