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Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data
Du, Yuhui1,2; Lin, Dongdong1; Yu, Qingbao1; Sui, Jing1,3,4; Chen, Jiayu1; Rachakonda, Srinivas1; Adali, Tulay5; Calhoun, Vince D.1,6; Yuhui Du
Source PublicationFRONTIERS IN NEUROSCIENCE
2017-05-19
Volume11Issue:2017-5-19Pages:267
SubtypeArticle
AbstractSpatial group independent component analysis (GICA) methods decompose multiple-subject functional magnetic resonance imaging (fMRI) data into a linear mixture of spatially independent components (ICs), some of which are subsequently characterized as brain functional networks. Group information guided independent component analysis (GIG-ICA) as a variant of GICA has been proposed to improve the accuracy of the subject-specific ICs estimation by optimizing their independence. Independent vector analysis (IVA) is another method which optimizes the independence among each subject's components and the dependence among corresponding components of different subjects. Both methods are promising in neuroimaging study and showed a better performance than the traditional GICA. However, the difference between IVA and GIG-ICA has not been well studied. A detailed comparison between them is demanded to provide guidance for functional network analyses. In this work, we employed multiple simulations to evaluate the performances of the two approaches in estimating subject-specific components and time courses under conditions of different data quality and quantity, varied number of sources generated and inaccurate number of components used in computation, as well as the presence of spatially subject-unique sources. We also compared the two methods using healthy subjects' test-retest resting-state fMRI data in terms of spatial functional networks and functional network connectivity (FNC). Results from simulations support that GIG-ICA showed better recovery accuracy of both components and time courses than IVA for those subject-common sources, and IVA outperformed GIG-ICA in component and time course estimation for the subject-unique sources. Results from real fMRI data suggest that GIG-ICA resulted in more reliable spatial functional networks and yielded higher and more robust modularity property of FNC, compared to IVA. Taken together, GIG-ICA is appropriate for estimating networks which are consistent across subjects, while IVA is able to estimate networks with great inter-subject variability or subject-unique property.
KeywordFunctional Magnetic Resonance Imaging (Fmri) Brain Functional Networks Independent Component Analysis (Ica) Group Information Guided Ica (gig-Ica) Independent Vector Analysis (Iva)
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.3389/fnins.2017.00267
WOS KeywordINDEPENDENT VECTOR ANALYSIS ; GRAPH-THEORETICAL ANALYSIS ; DEFAULT MODE NETWORK ; PSYCHOTIC BIPOLAR DISORDER ; RESONANCE-IMAGING DATA ; COMPONENT ANALYSIS ; RESTING-STATE ; SCHIZOAFFECTIVE DISORDERS ; SCHIZOPHRENIA-PATIENTS ; SUBJECT VARIABILITY
Indexed BySCI
Language英语
Funding OrganizationNational Institutes of Health(R01EB006841 ; National Science Foundation (NSF)(1016619 ; Centers of Biomedical Research Excellence (COBRE)(P20RR021938/P20GM103472) ; NSF(1618551 ; natural science foundation of Shanxi(2016021077) ; R01REB020407) ; 1539067) ; 1631838)
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000406525300001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20291
Collection脑网络组研究中心
Corresponding AuthorYuhui Du
Affiliation1.Mind Res Network, Albuquerque, NM 87106 USA
2.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Shanxi, Peoples R China
3.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China
4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing, Peoples R China
5.Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Baltimore, MD 21228 USA
6.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
Recommended Citation
GB/T 7714
Du, Yuhui,Lin, Dongdong,Yu, Qingbao,et al. Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data[J]. FRONTIERS IN NEUROSCIENCE,2017,11(2017-5-19):267.
APA Du, Yuhui.,Lin, Dongdong.,Yu, Qingbao.,Sui, Jing.,Chen, Jiayu.,...&Yuhui Du.(2017).Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data.FRONTIERS IN NEUROSCIENCE,11(2017-5-19),267.
MLA Du, Yuhui,et al."Comparison of IVA and GIG-ICA in Brain Functional Network Estimation Using fMRI Data".FRONTIERS IN NEUROSCIENCE 11.2017-5-19(2017):267.
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