Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition

	Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition
	Kuang, Li-Dan 1; Lin, Qiu-Hua 1; Gong, Xiao-Feng 1; Cong, Fengyu 2,3; Sui, Jing4,5 ; Calhoun, Vince D.6,7
刊名	JOURNAL OF NEUROSCIENCE METHODS
	2015-12-30
卷号	256 页码:127-140
关键词	Canonical polyadic decomposition (CPD) Independent component analysis (ICA) Multi-subject fMRI data Inter-subject variability Tensor PICA Shift-invariant CP (SCP)
英文摘要	Background: 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.
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
类目[WOS]	Biochemical Research Methods ; Neurosciences
研究领域[WOS]	Biochemistry & Molecular Biology ; Neurosciences & Neurology
关键词[WOS]	RESTING-STATE NETWORKS ; TENSOR DECOMPOSITIONS ; DEFAULT-MODE ; MRI DATA ; BRAIN ; CONNECTIVITY ; ALGORITHMS ; SIMULATION ; MOTION ; ICA
收录类别	SCI
语种	英语
WOS记录号	WOS:000366618400014
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/10643]
专题	自动化研究所_脑网络组研究中心
作者单位	1.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
推荐引用方式 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.