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Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia
Falakshahi, Haleh1,2; Vergara, Victor M.2; Liu, Jingyu2,3; Mathalon, Daniel H.4; Ford, Judith M.4; Voyvodic, James5; Mueller, Bryon A.6; Belger, Aysenil7; McEwen, Sarah4; Potkin, Steven G.; Preda, Adrian; Rokham, Hooman1,2; Sui, Jing2,8; Turner, Jessica A.9; Plis, Sergey2,3; Calhoun, Vince D.1,2,10
Source PublicationIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
ISSN0018-9294
2020-09-01
Volume67Issue:9Pages:2572-2584
Corresponding AuthorFalakshahi, Haleh(hfalakshahi@gatech.edu)
AbstractObjective: Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ). Methods: We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method. Results: Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components. Conclusion: We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality. Significance: The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.
KeywordFunctional magnetic resonance imaging Diseases Graphical models Psychiatry Correlation Translational research Connectivity covariance matrix data fusion default mode network dMRI fMRI GGM graphical model joint estimation partial correlation precision matrix sMRI
DOI10.1109/TBME.2020.2964724
WOS KeywordINDEPENDENT COMPONENT ANALYSIS ; WHITE-MATTER ABNORMALITIES ; DEFAULT MODE ; CONNECTIVITY ANALYSIS ; COGNITIVE DYSMETRIA ; NETWORK ; FMRI ; DYSFUNCTION ; DISORDER ; REVEALS
Indexed BySCI
Language英语
Funding ProjectNIH[R01EB020407] ; NIH[R01EB006841] ; NIH[P20GM103472] ; NIH[P30GM122734] ; NSF[1539067]
Funding OrganizationNIH ; NSF
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:000562053800017
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/40554
Collection脑网络组研究
Corresponding AuthorFalakshahi, Haleh
Affiliation1.Georgia Inst Technol, Dept Elect & Comp Engn, Atlanta, GA 30322 USA
2.Triinst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA 30300 USA
3.Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA
4.Univ Calif, Dept Psychiat, Davis, CA USA
5.Duke Univ, Dept Radiol, Durham, NC 27706 USA
6.Univ Minnesota, Dept Psychiat, Minneapolis, MN 55455 USA
7.Univ N Carolina, Dept Psychiat, Sch Med, Chapel Hill, NC 27515 USA
8.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
9.Georgia State Univ, Dept Psychol, Univ Plaza, Atlanta, GA 30303 USA
10.Emory Univ, Atlanta, GA 30322 USA
Recommended Citation
GB/T 7714
Falakshahi, Haleh,Vergara, Victor M.,Liu, Jingyu,et al. Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia[J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,2020,67(9):2572-2584.
APA Falakshahi, Haleh.,Vergara, Victor M..,Liu, Jingyu.,Mathalon, Daniel H..,Ford, Judith M..,...&Calhoun, Vince D..(2020).Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia.IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,67(9),2572-2584.
MLA Falakshahi, Haleh,et al."Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia".IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 67.9(2020):2572-2584.
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