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dc.contributor.authorChung, Moo K
dc.contributor.authorHuang, Shih-Gu
dc.contributor.authorGritsenko, Andrey
dc.contributor.authorShen, Li
dc.contributor.authorLee, Hyekyoung
dc.date.accessioned2021-09-08T06:17:22Z
dc.date.available2021-09-08T06:17:22Z
dc.date.issued2019-11-06
dc.identifier.citationChung, M. K., Huang, S.-G., Gritsenko, A., Shen, L., & Lee, H. (2019). Statistical Inference on the Number of Cycles in Brain Networks. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759222
dc.identifier.isbn9781538636411
dc.identifier.issn1945-7928
dc.identifier.pmid31687091
dc.identifier.doi10.1109/isbi.2019.8759222
dc.identifier.urihttp://hdl.handle.net/10754/671106
dc.description.abstractA cycle in a graph is a subset of a connected component with redundant additional connections. If there are many cycles in a connected component, the connected component is more densely connected. While the number of connected components represents the integration of the brain network, the number of cycles represents how strong the integration is. However, enumerating cycles in the network is not easy and often requires brute force enumerations. In this study, we present a new scalable algorithm for enumerating the number of cycles in the network. We show that the number of cycles is monotonically decreasing with respect to the filtration values during graph filtration. We further develop a new statistical inference framework for determining the significance of the number of cycles. The methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the functional human brain network.
dc.description.sponsorshipWe thank Martin Lindquist of Johns Hopkins University, Hernando Ombao of King Abdullah University of Science and Technology, Gregory Kirk of University of Wisconsin-Madison and Alex DiChristofano of Washington University at St. Louise for supports and discussions
dc.publisherIEEE
dc.relation.urlhttps://ieeexplore.ieee.org/document/8759222/
dc.rightsArchived with thanks to Proceedings. IEEE International Symposium on Biomedical Imaging
dc.titleSTATISTICAL INFERENCE ON THE NUMBER OF CYCLES IN BRAIN NETWORKS.
dc.typeArticle
dc.identifier.journalProceedings. IEEE International Symposium on Biomedical Imaging
dc.identifier.pmcidPMC6827564
dc.eprint.versionPost-print
dc.contributor.institutionUniversity of Wisconsin, Madison, USA
dc.contributor.institutionUniversity of Pennsylvania, Philadelphia, USA
dc.contributor.institutionSeoul National University, Seoul, Korea
dc.identifier.volume2019-April
dc.identifier.pages113-116
dc.identifier.eid2-s2.0-85073901475


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