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Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data
Madsen, Kristoffer H.1,2; Krohne, Laerke G.1,2; Cai, Xin-lu3,5; Wang, Yi3; Chan, Raymond C. K.3,4,5
2018-11-01
发表期刊SCHIZOPHRENIA BULLETIN
ISSN0586-7614
卷号44页码:S480-S490
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
DOI10.1093/schbul/sby026
语种英语
项目资助者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
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
WOS研究方向Psychiatry
WOS类目Psychiatry
WOS记录号WOS:000448172600004
出版者OXFORD UNIV PRESS
关键词[WOS]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
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被引频次:1[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
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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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