Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks

	Generalized RAICAR: Discover homogeneous subject (sub)groups by reproducibility of their intrinsic connectivity networks
	Yang, Zhi1,2,3 ; Zuo, Xi-Nian1,2 ; Wang, Peipei 4; Li, Zhihao 5; LaConte, Stephen M.; Bandettini, Peter A.3; Hu, Xiaoping P.5; Yang, Z (reprint author), 4A Datun Rd, Beijing 100101, Peoples R China.
心理所单位排序	1
摘要	Existing 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.
关键词	Independent component analysis Reproducibility Group discovery Sample homogeneity Exploratory analysis Resting state
学科领域	Cognitive Neuroscience
	2012-10-15
语种	英语
发表期刊	NEUROIMAGE
ISSN	1053-8119
卷号	63 期号:1 页码:403-414
期刊论文类型	Article
URL	查看原文
收录类别	SCI
项目简介	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记录号	WOS:000308770300042
资助机构	National 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]
引用统计	被引频次：42[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/311026/12830
专题	中国科学院行为科学重点实验室
通讯作者	Yang, Z (reprint author), 4A Datun Rd, Beijing 100101, Peoples R China.
作者单位	1.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
第一作者单位	认知与发展心理学研究室; 管理支撑系统
推荐引用方式 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.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
WOS_000308770300042.（1732KB）	期刊论文	出版稿	限制开放	CC BY-NC-SA	请求全文