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dc.contributor.authorYu, Qingbao
dc.contributor.authorDu, Yuhui
dc.contributor.authorChen, Jiayu
dc.contributor.authorSui, Jing
dc.contributor.authorAdali, Tulay
dc.contributor.authorPearlson, Godfrey
dc.contributor.authorCalhoun, Vince D.
dc.date.accessioned2018-05-25T14:31:31Z
dc.date.available2018-05-25T14:31:31Z
dc.date.issued2018
dc.descriptioncopyright 2018 IEEEen_US
dc.description.abstractAbstract: Human brain connectivity is complex. Graph-theory-based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multimodal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesses. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this paper provides a guide for developing new powerful tools to explore complex brain networks.en_US
dc.description.sponsorshipThis work is supported by the National Institutes of Health (NIH) grants including a COBRE grant (P20GM103472/5P20RR021938), R01 grants (R01EB005846, 1R01EB006841, 1R01DA040487, R01REB020407, R01EB000840, and R37MH43775) and the National Science Foundation (NSF) grants #1539067, #1618551 and #1631838. This work is also partly supported by the “100 Talents Plan” of Chinese Academy of Sciences, the state high-tech development plan of China (863) 2015AA020513 (PI: JS), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB02060005), Chinese NSF (81471367, 61773380, PI: Sui J; 61703253, PI: YHD) and Natural Science Foundation of Shanxi (2016021077, PI: YHD).en_US
dc.description.urihttps://ieeexplore.ieee.org/document/8347205/en_US
dc.format.extent29 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/M2R20S04M
dc.identifier.citationYu, Qingbao, et al. "Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs." Proceedings of IEEE, Vol. 106, Issue 5. pp.. 886 - 906, DOI: 10.1109/JPROC.2018.2825200en_US
dc.identifier.uri10.1109/JPROC.2018.2825200
dc.identifier.urihttp://hdl.handle.net/11603/10866
dc.language.isoen_USen_US
dc.publisherIEEEen_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.rightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
dc.subjectbrain modelingen_US
dc.subjectgraph theoryen_US
dc.subjectdiseasesen_US
dc.subjectfunctional magnetic resonance imagingen_US
dc.subjectmental disordersen_US
dc.subjectimage edge detectionen_US
dc.subjectdetection algorithmsen_US
dc.subjectbrain graphen_US
dc.titleApplication of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphsen_US
dc.typeTexten_US


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