Ranking and averaging independent component analysis by reproducibility (RAICAR)

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	Ranking and averaging independent component analysis by reproducibility (RAICAR)
	Yang, Zhi1,2,3 ; LaConte, Stephen 1; Weng, Xuchu2 ; Hu, Xiaoping 1; X. P. Hu
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
	2008-06-01
语种	英语
发表期刊	HUMAN BRAIN MAPPING
ISSN	1065-9471
卷号	29 期号:6 页码:711-725
期刊论文类型	Article
收录类别	SCI
WOS记录号	WOS:000256609500008
引用统计	被引频次：75[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
第一作者单位	中国科学院心理研究所
推荐引用方式 GB/T 7714	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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