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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
Source PublicationSCHIZOPHRENIA BULLETIN
ISSN0586-7614
Volume44Pages:S480-S490
AbstractFunctional 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.
Keywordfunctional magnetic resonance imaging feature extraction neuroimaging schizotypy schi zophrenia spectrum disorder
DOI10.1093/schbul/sby026
Language英语
Funding OrganizationBeijing 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
Funding ProjectBeijing 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 Research AreaPsychiatry
WOS SubjectPsychiatry
WOS IDWOS:000448172600004
PublisherOXFORD UNIV PRESS
WOS KeywordRESTING-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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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/25687
Collection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
Corresponding AuthorMadsen, Kristoffer H.
Affiliation1.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
Recommended Citation
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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