CASIA OpenIR
Parallel group ICA plus ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia
Qi, Shile1,2; Sui, Jing3,4,5,6; Chen, Jiayu1,2; Liu, Jingyu1,2; Jiang, Rongtao3,4,5; Silva, Rogers1,2; Iraji, Armin1,2; Damaraju, Eswar1,2; Salman, Mustafa1,2; Lin, Dongdong1,2; Fu, Zening1,2; Zhi, Dongmei3,4,5; Turner, Jessica A.7; Bustillo, Juan8; Ford, Judith M.9; Mathalon, Daniel H.9; Voyvodic, James10; McEwen, Sarah11; Preda, Adrian12; Belger, Aysenil13; Potkin, Steven G.12; Mueller, Bryon A.14; Adali, Tulay15; Calhoun, Vince D.1,2,7,8,16
Source PublicationHUMAN BRAIN MAPPING
ISSN1065-9471
2019-09-01
Volume40Issue:13Pages:3795-3809
Corresponding AuthorSui, Jing(jing.sui@nlpr.ia.ac.cn) ; Calhoun, Vince D.(vcalhoun@gsu.edu)
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.
Keywordgroup independent component analysis multimodal fusion parallel independent component analysis schizophrenia subjects' variability temporal information
DOI10.1002/hbm.24632
WOS KeywordINDEPENDENT COMPONENT ANALYSIS ; GRAY-MATTER ABNORMALITIES ; WORKING-MEMORY ; FMRI DATA ; MULTIMODAL FUSION ; CONNECTIVITY ; METAANALYSIS ; DISORDER ; INFERENCES ; PREDICTION
Indexed BySCI
Language英语
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] ; Beijing 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]
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
WOS Research AreaNeurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectNeurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000478645900007
PublisherWILEY
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/27758
Collection中国科学院自动化研究所
Corresponding AuthorSui, Jing; Calhoun, Vince D.
Affiliation1.Mind Res Network, Albuquerque, NM USA
2.Emory, Georgia Tech, Georgia State, Triinst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA 30303 USA
3.Brainnetome Ctr, Beijing 100190, Peoples R China
4.Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Beijing, Peoples R China
6.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
7.Georgia State Univ, Dept Psychol, Univ Plaza, Atlanta, GA 30303 USA
8.Univ New Mexico, Dept Psychiat, Albuquerque, NM 87131 USA
9.Univ Calif San Francisco, Dept Psychiat, San Francisco, CA USA
10.Duke Univ, Dept Radiol, Durham, NC 27710 USA
11.Univ Calif San Diego, Sch Med, Dept Psychiat, 9500 Gillman Dr, La Jolla, CA 92093 USA
12.Univ Calif Irvine, Dept Psychiat, Irvine, CA 92717 USA
13.Univ N Carolina, Sch Med, Dept Psychiat, Chapel Hill, NC 27515 USA
14.Univ Minnesota, Dept Psychiat, Minneapolis, MN 55455 USA
15.Univ Maryland Baltimore Country, Dept CSEE, Baltimore, MD USA
16.Univ New Mexico, Dept ECE, Albuquerque, NM 87131 USA
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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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