TY - JOUR

T1 - STATISTICAL INFERENCE ON THE NUMBER OF CYCLES IN BRAIN NETWORKS.

AU - Chung, Moo K

AU - Huang, Shih-Gu

AU - Gritsenko, Andrey

AU - Shen, Li

AU - Lee, Hyekyoung

N1 - KAUST Repository Item: Exported on 2021-09-09
Acknowledgements: We 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
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2019/11/6

Y1 - 2019/11/6

N2 - A 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.

AB - A 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.

UR - http://hdl.handle.net/10754/671106

UR - https://ieeexplore.ieee.org/document/8759222/

UR - http://www.scopus.com/inward/record.url?scp=85073901475&partnerID=8YFLogxK

U2 - 10.1109/isbi.2019.8759222

DO - 10.1109/isbi.2019.8759222

M3 - Article

C2 - 31687091

VL - 2019-April

SP - 113

EP - 116

JO - Proceedings. IEEE International Symposium on Biomedical Imaging

JF - Proceedings. IEEE International Symposium on Biomedical Imaging

SN - 1945-7928

ER -