Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data
文献类型：期刊论文
| 作者 | Madsen, Kristoffer H.1,2; Krohne, Laerke G.1,2; Cai, Xin-lu3,5; Wang, Yi3 ; Chan, Raymond C. K.3,4,5
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| 刊名 | SCHIZOPHRENIA BULLETIN
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| 出版日期 | 2018-11-01 |
| 卷号 | 44页码:S480-S490 |
| 关键词 | functional magnetic resonance imaging feature extraction neuroimaging schizotypy schi zophrenia spectrum disorder |
| ISSN号 | 0586-7614 |
| DOI | 10.1093/schbul/sby026 |
| 英文摘要 | 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. |
| 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 |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000448172600004 |
| 出版者 | OXFORD UNIV PRESS |
| 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 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/25687]  |
| 专题 | 心理研究所_中国科学院心理健康重点实验室 心理研究所_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. |
入库方式： OAI收割
来源：心理研究所
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