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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
2008-06-01
Source PublicationHUMAN BRAIN MAPPING
ISSN1065-9471
SubtypeArticle
Volume29Issue:6Pages:711-725
AbstractIndependent 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.
KeywordfMRI independent component analysis data analysis
Subject Area认知神经科学
Indexed BySCI
Language英语
WOS IDWOS:000256609500008
Citation statistics
Cited Times:56[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/5653
Collection中国科学院心理研究所回溯数据库(1956-2010)
Corresponding AuthorX. P. Hu
Affiliation1.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
Recommended Citation
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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