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Unified SPM-ICA for fMRI analysis
Hu, DW; Yan, LR; Liu, YD; Zhou, ZT; Friston, KJ; Tan, CL; Wu, DX; D. W. Hu
2005-04-15
Source PublicationNEUROIMAGE
ISSN1053-8119
SubtypeArticle
Volume25Issue:3Pages:746-755
AbstractA widely used tool for functional magnetic resonance imaging (fMRI) data analysis, statistical parametric mapping (SPM), is based on the general linear model (GLM). SPM therefore requires a priori knowledge or specific assumptions about the time courses contributing to signal changes. In contradistinction, independent component analysis (ICA) is a data-driven method based on the assumption that the causes of responses are statistically independent. Here we describe a unified method, which combines ICA, temporal ICA (tICA), and SPM for analyzing fMRI data. tICA was applied to fMRI datasets to disclose independent components, whose number was determined by the Bayesian information criterion (BIC). The resulting components were used to construct the design matrix of a GLM. Parameters were estimated and region ally-specific statistical inferences were made about activations in the usual way. The sensitivity and specificity were evaluated using Monte Carlo simulations. The receiver operating characteristic (ROC) curves indicated that the unified SPM-ICA method had a better performance. Moreover, SPM-ICA was applied to fMRI datasets from twelve normal subjects performing left and right hand movements. The areas identified corresponded to motor (premotor, sensorimotor areas and SMA) areas and were consistently task related. Part of the frontal lobe, parietal cortex, and cingulate gyrus also showed transiently task-related responses. The unified method requires less supervision than the conventional SPM and enables classical inference about the expression of independent components. Our results also suggest that the method has a higher sensitivity than SPM analyses.; A widely used tool for functional magnetic resonance imaging (fMRI) data analysis, statistical parametric mapping (SPM), is based on the general linear model (GLM). SPM therefore requires a priori knowledge or specific assumptions about the time courses contributing to signal changes. In contradistinction, independent component analysis (ICA) is a data-driven method based on the assumption that the causes of responses are statistically independent. Here we describe a unified method, which combines ICA, temporal ICA (tICA), and SPM for analyzing fMRI data. tICA was applied to fMRI datasets to disclose independent components, whose number was determined by the Bayesian information criterion (BIC). The resulting components were used to construct the design matrix of a GLM. Parameters were estimated and region ally-specific statistical inferences were made about activations in the usual way. The sensitivity and specificity were evaluated using Monte Carlo simulations. The receiver operating characteristic (ROC) curves indicated that the unified SPM-ICA method had a better performance. Moreover, SPM-ICA was applied to fMRI datasets from twelve normal subjects performing left and right hand movements. The areas identified corresponded to motor (premotor, sensorimotor areas and SMA) areas and were consistently task related. Part of the frontal lobe, parietal cortex, and cingulate gyrus also showed transiently task-related responses. The unified method requires less supervision than the conventional SPM and enables classical inference about the expression of independent components. Our results also suggest that the method has a higher sensitivity than SPM analyses. (c) 2004 Elsevier Inc. All rights reserved.
Keywordindependent component analysis (ICA) statistical parametric mapping (SPM) functional MRI brain activation motor paradigm consistently task-felated responses transiently task-related responses
Subject Area心理学研究方法
Indexed BySCI
Language英语
WOS IDWOS:000228383500011
Citation statistics
Cited Times:56[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/5825
Collection中国科学院心理研究所回溯数据库(1956-2010)
Corresponding AuthorD. W. Hu
Affiliation1.Natl Univ Def Technol, Coll Mechatron & Automat, Changsha 410073, Hunan, Peoples R China
2.Chinese Acad Sci, Inst Psychol, Key Lab Mental Hlth, Beijing 100101, Peoples R China
3.Wellcome Dept Imaging Neurosci, Inst Neurol, London WC1N 3BG, England
4.Ctr S Univ, Xiangya Hosp 2, Dept Radiol, Changsha 410011, Peoples R China
5.Ctr S Univ, Xiangya Hosp 2, Res Ctr Psychol, Changsha 410011, Peoples R China
Recommended Citation
GB/T 7714
Hu, DW,Yan, LR,Liu, YD,et al. Unified SPM-ICA for fMRI analysis[J]. NEUROIMAGE,2005,25(3):746-755.
APA Hu, DW.,Yan, LR.,Liu, YD.,Zhou, ZT.,Friston, KJ.,...&D. W. Hu.(2005).Unified SPM-ICA for fMRI analysis.NEUROIMAGE,25(3),746-755.
MLA Hu, DW,et al."Unified SPM-ICA for fMRI analysis".NEUROIMAGE 25.3(2005):746-755.
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