PSYCH OpenIR  > 中国科学院行为科学重点实验室
Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks
Yang, Zhi1,2,3; Zuo, Xi-Nian1,2; Wang, Peipei4; Li, Zhihao5; LaConte, Stephen M.; Bandettini, Peter A.3; Hu, Xiaoping P.5; Yang, Z (reprint author), 4A Datun Rd, Beijing 100101, Peoples R China.
2012-10-15
Source PublicationNEUROIMAGE
ISSN1053-8119
SubtypeArticle
Volume63Issue:1Pages:403-414
Contribution Rank1
AbstractExisting spatial independent component analysis (ICA) methods for multi-subject fMRI datasets have mainly focused on detecting common components across subjects, under the assumption that all the subjects in a group share the same (identical) components. However, as a data-driven approach, ICA could potentially serve as an exploratory tool at multi-subject level, and help us uncover inter-subject differences in patterns of connectivity (e.g., find subtypes in patient populations). In this work, we propose a methodology named gRAICAR that exploits the data-driven nature of ICA to allow discovery of sub-groupings of subjects based on reproducibility of their ICA components. This technique allows us not only to find highly reproducible common components across subjects but also to explore (without a priori subject groupings) components that could classify all subjects into sub-groups. gRAICAR generalizes the reproducibility framework previously developed for single subjects (Ranking and averaging independent component analysis by reproducibility-RAICAR-Yang et al., Hum Brain Mapp, 2008) to multiple-subject analysis. For each group-level component, gRAICAR generates its reproducibility matrix and further computes two metrics, inter-subject consistency and intra-subject reliability, to characterize inter-subject variability and reflect contributions from individual subjects. Nonparametric tests are employed to examine the significance of both the inter-subject consistency and the separation of subject groups reflected in the component. Our validations based on simulated and experimental resting-state fMRI datasets demonstrated the advantage of gRAICAR in extracting features reflecting potential subject groupings. It may facilitate discovery of the underlying brain functional networks with substantial potential to inform our understandings of development, neurodegenerative conditions, and psychiatric disorders. (C) 2012 Elsevier Inc. All rights reserved.
KeywordIndependent component analysis Reproducibility Group discovery Sample homogeneity Exploratory analysis Resting state
Subject AreaCognitive Neuroscience
URL查看原文
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China [30900366, 81171409, 30973164, 30670674] ; National Basic Research Program of China (973 Program) [2007CB512300] ; National Institutes of Health of the United States [RO1B002009] ; Youth Foundation [O9CX012001] ; Institute of Psychology, Chinese Academy of Sciences [Y0CX492S03]
Project Intro.We acknowledge Dr. Anna B. Moore for sharing the resting-state fMRI dataset and Dr. R. Cameron Craddock for his efforts in improving the paper. This work was supported by the National Natural Science Foundation of China (Nos. 30900366, 81171409, 30973164, and 30670674), the National Basic Research Program of China (973 Program, 2007CB512300), the National Institutes of Health of the United States (RO1B002009), the Youth Foundation (O9CX012001, ZY) and the Startup Foundation for Distinguished Research Professor (Y0CX492S03, XNZ) of the Institute of Psychology, Chinese Academy of Sciences.
WOS IDWOS:000308770300042
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/12830
Collection中国科学院行为科学重点实验室
Corresponding AuthorYang, Z (reprint author), 4A Datun Rd, Beijing 100101, Peoples R China.
Affiliation1.Chinese Acad Sci, Inst Psychol, Lab Funct Connectome & Dev, Key Lab Behav Sci, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, Beijing 100101, Peoples R China
3.NIMH, Sect Funct Imaging Methods, Lab Brain & Cognit, NIH, Bethesda, MD 20892 USA
4.Capital Med Univ, Ctr Higher Brain Funct Res, Sch Basic Med Sci, Beijing, Peoples R China
5.Emory Univ, Dept Biomed Engn, Biomed Imaging Technol Ctr, Atlanta, GA 30322 USA
Recommended Citation
GB/T 7714
Yang, Zhi,Zuo, Xi-Nian,Wang, Peipei,et al. Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks[J]. NEUROIMAGE,2012,63(1):403-414.
APA Yang, Zhi.,Zuo, Xi-Nian.,Wang, Peipei.,Li, Zhihao.,LaConte, Stephen M..,...&Yang, Z .(2012).Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks.NEUROIMAGE,63(1),403-414.
MLA Yang, Zhi,et al."Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks".NEUROIMAGE 63.1(2012):403-414.
Files in This Item:
File Name/Size DocType Version Access License
WOS_000308770300042.(1732KB)期刊论文出版稿限制开放CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yang, Zhi]'s Articles
[Zuo, Xi-Nian]'s Articles
[Wang, Peipei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yang, Zhi]'s Articles
[Zuo, Xi-Nian]'s Articles
[Wang, Peipei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yang, Zhi]'s Articles
[Zuo, Xi-Nian]'s Articles
[Wang, Peipei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: WOS_000308770300042.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.