Extraction of time-varying spatio-temporal networks using parameter-tuned constrained IVA

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dc.contributor.advisorAdali, Tulay
dc.contributor.authorBHINGE, SUCHITA
dc.contributor.authorMowakeaa, Rami
dc.contributor.authorCalhoun, Vince D
dc.contributor.authorAdali, Tulay
dc.contributor.departmentDepartment of Computer Science and Electrical Engineeringen_US
dc.date.accessioned2019-08-20T13:53:09Z
dc.date.available2019-08-20T13:53:09Z
dc.date.issued2019-07
dc.description.abstractDynamic functional connectivity (dFC) analysis is an effective way to capture the networks that are functionally associated and continuously changing over the scanning period. However, these methods mostly analyze the dynamic associations across the activation patterns of the spatial networks while assuming that the spatial networks are stationary. Hence, a model that allows for the variability in both domains and reduces the assumptions imposed on the data provides an effective way for extracting spatio-temporal networks. Independent vector analysis is a joint blind source separation technique that allows for estimation of spatial and temporal features while successfully preserving variability. However, its performance is affected for higher number of datasets. Hence, we develop an effective two-stage method to extract time-varying spatial and temporal features using IVA, mitigating the problems with higher number of datasets while preserving the variability across subjects and time. The first stage is used to extract reference signals using group independent component analysis (GICA) that are used in a parameter-tuned constrained IVA (pt-cIVA) framework to estimate time-varying representations of these signals by preserving the variability through tuning the constraint parameter. This approach effectively captures variability across time from a large-scale resting-state fMRI data acquired from healthy controls and patients with schizophrenia and identifies more functionally relevant connections that are significantly different among healthy controls and patients with schizophrenia, compared with the widely used GICA method alone.en_US
dc.description.sponsorshipNational Institute of Biomedical Imaging and Bioengineering under Grant R01 EB 020407, National Science Foundation under Grant 1631838, National Science Foundation-Computing and Communication Foundations under Grant 1618551en_US
dc.description.urihttps://ieeexplore.ieee.org/document/8624617en_US
dc.format.extent11 pagesen_US
dc.genrejournal articles postprintsen_US
dc.identifierdoi:10.13016/m2slju-ikvj
dc.identifier.citationS. Bhinge, R. Mowakeaa, V. D. Calhoun and T. Adalı, "Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA," in IEEE Transactions on Medical Imaging, vol. 38, no. 7, pp. 1715-1725, July 2019. doi: 10.1109/TMI.2019.2893651 keywords: {Functional magnetic resonance imaging;Feature extraction;Estimation;Data models;Adaptation models;Blind source separation;Independent component analysis;Blind source separation;connectivity analysis;dimensionality reduction;fMRI analysis}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8624617&isnumber=8750971en_US
dc.identifier.issn0278-0062
dc.identifier.uri10.1109/TMI.2019.2893651
dc.identifier.urihttp://hdl.handle.net/11603/14418
dc.language.isoen_USen_US
dc.publisherIEEE Transaction on Medical Imagingen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights©2019 IEEE
dc.subjectBlind source separationen_US
dc.subjectConnectivity analysisen_US
dc.subjectDimensionality reductionen_US
dc.subjectfMRI analysisen_US
dc.titleExtraction of time-varying spatio-temporal networks using parameter-tuned constrained IVAen_US
dc.title.alternativeExtraction of time-varying spatiotemporal networks using parameter-tuned constrained IVAen_US
dc.typeTexten_US

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