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
发表期刊NEUROIMAGE
ISSN1053-8119
文章类型Article
卷号63期号:1页码:403-414
产权排序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
URL查看原文
收录类别SCI
语种英语
项目资助者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]
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
引用统计
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
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浏览 请求全文
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Yang, Zhi]的文章
[Zuo, Xi-Nian]的文章
[Wang, Peipei]的文章
百度学术
百度学术中相似的文章
[Yang, Zhi]的文章
[Zuo, Xi-Nian]的文章
[Wang, Peipei]的文章
必应学术
必应学术中相似的文章
[Yang, Zhi]的文章
[Zuo, Xi-Nian]的文章
[Wang, Peipei]的文章
相关权益政策
暂无数据
收藏/分享
文件名: WOS_000308770300042.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。