Institutional Repository of Key Laboratory of Mental Health, CAS
| 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
| |
| Abstract | 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. |
| Keyword | functional magnetic resonance imaging feature extraction neuroimaging schizotypy schi zophrenia spectrum disorder |
| 2018-11-01 | |
| Language | 英语 |
| DOI | 10.1093/schbul/sby026 |
| Source Publication | SCHIZOPHRENIA BULLETIN
 |
| ISSN | 0586-7614 |
| Volume | 44Pages:S480-S490 |
| Funding Project | 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 |
| Publisher | OXFORD UNIV PRESS |
| WOS Keyword | 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 Research Area | Psychiatry |
| WOS Subject | Psychiatry |
| WOS ID | WOS:000448172600004 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://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 Author | Madsen, Kristoffer H. |
| Affiliation | 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 |
| 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Perspectives on Mach（479KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | Application Full Text | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment