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Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition
Kuang, Li-Dan1; Lin, Qiu-Hua1; Gong, Xiao-Feng1; Cong, Fengyu2,3; Sui, Jing4,5; Calhoun, Vince D.6,7
Source PublicationJOURNAL OF NEUROSCIENCE METHODS
2015-12-30
Volume256Pages:127-140
SubtypeArticle
AbstractBackground: Canonical polyadic decomposition (CPD) may face a local optimal problem when analyzing multi-subject fMRI data with inter-subject variability. Beckmann and Smith proposed a tensor PICA approach that incorporated an independence constraint to the spatial modality by combining CPD with ICA, and alleviated the problem of inter-subject spatial map (SM) variability.
KeywordCanonical Polyadic Decomposition (Cpd) Independent Component Analysis (Ica) Multi-subject Fmri Data Inter-subject Variability Tensor Pica Shift-invariant Cp (sCp)
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.1016/j.jneumeth.2015.08.023
WOS KeywordRESTING-STATE NETWORKS ; TENSOR DECOMPOSITIONS ; DEFAULT-MODE ; MRI DATA ; BRAIN ; CONNECTIVITY ; ALGORITHMS ; SIMULATION ; MOTION ; ICA
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61379012 ; "100 Talents Plan" of Chinese Academy of Sciences ; NSF(0840895 ; NIH(R01EB005846 ; Fundamental Research Funds for the Central Universities (China)(DUT14RC(3)037) ; China Scholarship Council ; 61105008 ; 0715022) ; 5P20GM103472) ; 61331019 ; 81471367)
WOS Research AreaBiochemistry & Molecular Biology ; Neurosciences & Neurology
WOS SubjectBiochemical Research Methods ; Neurosciences
WOS IDWOS:000366618400014
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10643
Collection脑网络组研究中心
Affiliation1.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Sch Informat & Commun Engn, Dalian 116024, Peoples R China
2.Dalian Univ Technol, Fac Elect Informat & Elect Engn, Dept Biomed Engn, Dalian 116024, Peoples R China
3.Univ Jyvaskyla, Dept Math Informat Technol, SF-40351 Jyvaskyla, Finland
4.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
6.Mind Res Network, Albuquerque, NM 87106 USA
7.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
Recommended Citation
GB/T 7714
Kuang, Li-Dan,Lin, Qiu-Hua,Gong, Xiao-Feng,et al. Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition[J]. JOURNAL OF NEUROSCIENCE METHODS,2015,256:127-140.
APA Kuang, Li-Dan,Lin, Qiu-Hua,Gong, Xiao-Feng,Cong, Fengyu,Sui, Jing,&Calhoun, Vince D..(2015).Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition.JOURNAL OF NEUROSCIENCE METHODS,256,127-140.
MLA Kuang, Li-Dan,et al."Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition".JOURNAL OF NEUROSCIENCE METHODS 256(2015):127-140.
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