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	Ranking and averaging independent component analysis by reproducibility (RAICAR)
	Yang, Zhi1,2,3; LaConte, Stephen1; Weng, Xuchu2; Hu, Xiaoping1; X. P. Hu
刊名	HUMAN BRAIN MAPPING
	2008-06-01
卷号	29期号:6页码:711-725
关键词	fMRI independent component analysis data analysis
ISSN号	1065-9471
文献子类	Article
英文摘要	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.
学科主题	认知神经科学
语种	英语
WOS记录号	WOS:000256609500008
公开日期	2011-08-22
内容类型	期刊论文
源URL	[http://ir.psych.ac.cn/handle/311026/5654]
专题	心理研究所_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
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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