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Chinese Academy of Sciences Institutional Repositories Grid
Parallel group ICA plus ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia

文献类型:期刊论文

AuthorQi, Shile2,3; Sui, Jing4,5,6,7; Chen, Jiayu2,3; Liu, Jingyu2,3; Jiang, Rongtao4,5,6; Silva, Rogers2,3; Iraji, Armin2,3; Damaraju, Eswar2,3; Salman, Mustafa2,3; Lin, Dongdong2,3
SourceHUMAN BRAIN MAPPING
Issued Date2019-09-01
Volume40Issue:13Pages:3795-3809
ISSN1065-9471
Keywordgroup independent component analysis multimodal fusion parallel independent component analysis schizophrenia subjects' variability temporal information
DOI10.1002/hbm.24632
Corresponding AuthorSui, Jing(jing.sui@nlpr.ia.ac.cn) ; Calhoun, Vince D.(vcalhoun@gsu.edu)
English AbstractThere is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.
WOS KeywordINDEPENDENT COMPONENT ANALYSIS ; GRAY-MATTER ABNORMALITIES ; WORKING-MEMORY ; FMRI DATA ; MULTIMODAL FUSION ; CONNECTIVITY ; METAANALYSIS ; DISORDER ; INFERENCES ; PREDICTION
Funding ProjectBeijing Municipal Science and Technology Commission[Z181100001518005] ; Natural Science Foundation of China[61773380] ; Natural Science Foundation of China[81471367] ; NIH[1R01MH094524] ; NIH[P20GM103472] ; NIH[P30GM122734] ; NIH[R01EB005846] ; NIH[R56MH117107] ; National Science Foundation (NSF)[1539067] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB03040100]
WOS Research AreaNeurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
Language英语
WOS IDWOS:000478645900007
PublisherWILEY
Funding OrganizationBeijing Municipal Science and Technology Commission ; Natural Science Foundation of China ; NIH ; National Science Foundation (NSF) ; Strategic Priority Research Program of the Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/27757]  
Collection中国科学院自动化研究所
Corresponding AuthorSui, Jing; Calhoun, Vince D.
Affiliation1.Univ Minnesota, Dept Psychiat, Minneapolis, MN 55455 USA
2.Mind Res Network, Albuquerque, NM USA
3.Emory, Georgia Tech, Georgia State, Triinst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA 30303 USA
4.Brainnetome Ctr, Beijing 100190, Peoples R China
5.Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Beijing, Peoples R China
7.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
8.Georgia State Univ, Dept Psychol, Univ Plaza, Atlanta, GA 30303 USA
9.Univ New Mexico, Dept Psychiat, Albuquerque, NM 87131 USA
10.Univ Calif San Francisco, Dept Psychiat, San Francisco, CA USA
Recommended Citation
GB/T 7714
Qi, Shile,Sui, Jing,Chen, Jiayu,et al. Parallel group ICA plus ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia[J]. HUMAN BRAIN MAPPING,2019,40(13):3795-3809.
APA Qi, Shile.,Sui, Jing.,Chen, Jiayu.,Liu, Jingyu.,Jiang, Rongtao.,...&Calhoun, Vince D..(2019).Parallel group ICA plus ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia.HUMAN BRAIN MAPPING,40(13),3795-3809.
MLA Qi, Shile,et al."Parallel group ICA plus ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia".HUMAN BRAIN MAPPING 40.13(2019):3795-3809.

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