Permanent link to this recordhttp://hdl.handle.net/10754/671106
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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.
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
SponsorsWe 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
PubMed Central IDPMC6827564
CollectionsPublications Acknowledging KAUST Support
- Exact topological inference of the resting-state brain networks in twins.
- Authors: Chung MK, Lee H, DiChristofano A, Ombao H, Solo V
- Issue date: 2019
- A UNIVARIATE PERSISTENT BRAIN NETWORK FEATURE BASED ON THE AGGREGATED COST OF CYCLES FROM THE NESTED FILTRATION NETWORKS.
- Authors: Farazi M, Zhan L, Lepore N, Thompson PM, Wang Y
- Issue date: 2020
- A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative.
- Authors: Kuang L, Han X, Chen K, Caselli RJ, Reiman EM, Wang Y, Alzheimer's Disease Neuroimaging Initiative.
- Issue date: 2019 Mar
- Detecting modules in biological networks by edge weight clustering and entropy significance.
- Authors: Lecca P, Re A
- Issue date: 2015
- Neutral space analysis for a Boolean network model of the fission yeast cell cycle network.
- Authors: Ruz GA, Timmermann T, Barrera J, Goles E
- Issue date: 2014 Nov 25