Ranking and averaging independent component analysis by reproducibility (RAICAR)
Yang, Zhi1,2,3; LaConte, Stephen1; Weng, Xuchu2; Hu, Xiaoping1; X. P. Hu
2008-06-01
发表期刊HUMAN BRAIN MAPPING
ISSN1065-9471
文章类型Article
卷号29期号:6页码:711-725
摘要Independent component analysis (ICA) is a data-driven approach that has exhibited great utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations, however, do not provide the number and relative importance of the resulting components. In addition, ICA algorithms utilizing gradient-based optimization give decompositions that are dependent on initialization values, which can lead to dramatically different results. In this work, a new method, RAICAR (Ranking and Averaging Independent Component Analysis by Reproducibility), is introduced to address these issues for spatial ICA applied to fMRI. RAICAR utilizes repeated ICA realizations and relies on the reproducibility between them to rank and select components. Different realizations are aligned based on correlations, leading to aligned components. Each component is ranked and thresholded based on between-realization correlations. Furthermore, different realizations of each aligned component are selectively averaged to generate the final estimate of the given component. Reliability and accuracy of this method are demonstrated with both simulated and experimental fMRI data.; Independent component analysis (ICA) is a data-driven approach that has exhibited great utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations, however, do not provide the number and relative importance of the resulting components. In addition, ICA algorithms utilizing gradient-based optimization give decompositions that are dependent on initialization values, which can lead to dramatically different results. In this work, a new method, RAICAR (Ranking and Averaging Independent Component Analysis by Reproducibility), is introduced to address these issues for spatial ICA applied to fMRI. RAICAR utilizes repeated ICA realizations and relies on the reproducibility between them to rank and select components. Different realizations are aligned based on correlations, leading to aligned components. Each component is ranked and thresholded based on between-realization correlations. Furthermore, different realizations of each aligned component are selectively averaged to generate the final estimate of the given component. Reliability and accuracy of this method are demonstrated with both simulated and experimental fMRI data.
关键词fMRI independent component analysis data analysis
学科领域认知神经科学
收录类别SCI
语种英语
WOS记录号WOS:000256609500008
引用统计
被引频次:54[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/311026/5653
专题中国科学院心理研究所回溯数据库(1956-2010)
通讯作者X. P. Hu
作者单位1.Emory Univ, Wallace H Coulter Dept Biomed Engn, Biomed Imaging Technol Ctr, Atlanta, GA 30322 USA
2.Chinese Acad Sci, Inst Psychol, Lab Higher Brain Funct, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Grad Univ, Coll Humanities & Social Sci, Beijing 100101, Peoples R China
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Yang, Zhi,LaConte, Stephen,Weng, Xuchu,et al. Ranking and averaging independent component analysis by reproducibility (RAICAR)[J]. HUMAN BRAIN MAPPING,2008,29(6):711-725.
APA Yang, Zhi,LaConte, Stephen,Weng, Xuchu,Hu, Xiaoping,&X. P. Hu.(2008).Ranking and averaging independent component analysis by reproducibility (RAICAR).HUMAN BRAIN MAPPING,29(6),711-725.
MLA Yang, Zhi,et al."Ranking and averaging independent component analysis by reproducibility (RAICAR)".HUMAN BRAIN MAPPING 29.6(2008):711-725.
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