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Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia

文献类型：期刊论文

作者Kuang, Li-Dan1; Lin, Qiu-Hua1; Gong, Xiao-Feng1; Cong, Fengyu2,3; Sui, Jing4,5,6; Calhoun, Vince D.6,7
刊名JOURNAL OF NEUROSCIENCE METHODS
出版日期2018-07-01
卷号304页码:24-38
关键词Independent component analysis (ICA) Complex-valued fMRI data Model order Component splitting Phase data Schizophrenia
ISSN号0165-0270
DOI10.1016/j.jneumeth.2018.02.013
通讯作者Lin, Qiu-Hua(qhlin@dlut.edu.cn)
英文摘要Background: Component splitting at higher model orders is a widely accepted finding for independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. However, our recent study found that intact components occurred with subcomponents at higher model orders. New method: This study investigated model order effects on ICA of resting-state complex-valued fMRI data from 82 subjects, which included 40 healthy controls (HCs) and 42 schizophrenia patients. In addition, we explored underlying causes for distinct component splitting between complex-valued data and magnitude-only data by examining model order effects on ICA of phase fMRI data. A best run selection method was proposed to combine subject averaging and a one-sample t-test. We selected the default mode network (DMN)-, visual-, and sensorimotor-related components from the best run of ICA at varying model orders from 10 to 140. Results: Results show that component integration occurred in complex-valued and phase analyses, whereas component splitting emerged in magnitude-only analysis with increasing model order. Incorporation of phase data appears to play a complementary role in preserving integrity of brain networks. Comparison with existing method(s): When compared with magnitude-only analysis, the intact DMN component obtained in complex-valued analysis at higher model orders exhibited highly significant subject-level differences between HCs and patients with schizophrenia. We detected significantly higher activity and variation in anterior areas for HCs and in posterior areas for patients with schizophrenia. Conclusions: These results demonstrate the potential of complex-valued fMRI data to contribute generally and specifically to brain network analysis in identification of schizophrenia-related changes. (C) 2018 Elsevier B.V. All rights reserved.
WOS关键词INDEPENDENT COMPONENT ANALYSIS ; FUNCTIONAL NETWORK CONNECTIVITY ; BLIND SEPARATION ; VECTOR ANALYSIS ; IMAGING DATA ; GROUP PICA ; MRI DATA ; BRAIN ; BOLD ; BIPOLAR
资助项目National Natural Science Foundation of China[61379012] ; National Natural Science Foundation of China[61671106] ; National Natural Science Foundation of China[61331019] ; National Natural Science Foundation of China[81471742] ; National Natural Science Foundation of China[81471367] ; 100 Talents Plan of the Chinese Academy of Sciences ; Chinese Academy of Sciences[XDB02060005] ; National High Tech Development Plan (863 plan)[2015AA020513] ; NSF[0840895] ; NSF[1539067] ; NSF[0715022] ; NIH[R01EB005846] ; NIH[5P20GM103472] ; Fundamental Research Funds for the Central Universities (China)[DUT14RC(3)037] ; Supercomputing Center of Dalian University of Technology
WOS研究方向Biochemistry & Molecular Biology ; Neurosciences & Neurology
语种英语
WOS记录号WOS:000435622300003
出版者ELSEVIER SCIENCE BV
资助机构National Natural Science Foundation of China ; 100 Talents Plan of the Chinese Academy of Sciences ; Chinese Academy of Sciences ; National High Tech Development Plan (863 plan) ; NSF ; NIH ; Fundamental Research Funds for the Central Universities (China) ; Supercomputing Center of Dalian University of Technology
源URL[http://ir.ia.ac.cn/handle/173211/21817]  
专题自动化研究所_脑网络组研究中心
通讯作者Lin, Qiu-Hua
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, Jyvaskyla, Finland
4.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 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. Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia[J]. JOURNAL OF NEUROSCIENCE METHODS,2018,304:24-38.
APA Kuang, Li-Dan,Lin, Qiu-Hua,Gong, Xiao-Feng,Cong, Fengyu,Sui, Jing,&Calhoun, Vince D..(2018).Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia.JOURNAL OF NEUROSCIENCE METHODS,304,24-38.
MLA Kuang, Li-Dan,et al."Model order effects on ICA of resting-state complex-valued fMRI data: Application to schizophrenia".JOURNAL OF NEUROSCIENCE METHODS 304(2018):24-38.

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