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Perspectives on machine learning for classification of schizotypy using fMRI data
Madsen, H. K.1,2; Krohne, G. L.1,2; Cai, X. L.3,4,5; Wang, Y.3; Chan, R. C. K.3,4,5; Kristoffer H. Madsen
第一作者Kristoffer H. Madsen
2018
发表期刊Schizophrenia Bulletin
通讯作者邮箱kristofferm@drcmr.dk
期号sby026页码:1-11
Q分类Q1
产权排序3
摘要

Functional magnetic resonance imaging is capable of estimat- ing 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 identi cation 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 gen- eralization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe com- mon data processing steps while commenting on best prac- tices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classi-  cation, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical para- metric mapping, parcellation, complex network analysis, and decomposition methods, as well as classi cation with a spe- cial focus on support vector classi cation and deep learning. We provide more detailed descriptions and software as sup- plementary material. Finally, we present current challenges in machine learning for classi cation of schizotypy and com- ment on future trends and perspectives.

关键词functional magnetic resonance imaging feature extraction neuroimaging schizotypy schizophrenia spectrum disorder
收录类别SCI ; SSCI
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Kristoffer H. Madsen
作者单位1.Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
2.Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
3.Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
4.Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
5.Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Madsen, H. K.,Krohne, G. L.,Cai, X. L.,et al. Perspectives on machine learning for classification of schizotypy using fMRI data[J]. Schizophrenia Bulletin,2018(sby026):1-11.
APA Madsen, H. K.,Krohne, G. L.,Cai, X. L.,Wang, Y.,Chan, R. C. K.,&Kristoffer H. Madsen.(2018).Perspectives on machine learning for classification of schizotypy using fMRI data.Schizophrenia Bulletin(sby026),1-11.
MLA Madsen, H. K.,et al."Perspectives on machine learning for classification of schizotypy using fMRI data".Schizophrenia Bulletin .sby026(2018):1-11.
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