Exact topological inference of the resting-state brain networks in twins
Exact topological inference of the resting-state brain networks in twins
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DOI：
10.1162/netn_a_00091
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发表时间：
2019-01-01
影响因子：
4.7
通讯作者：
Solos, Victor
中科院分区：
文献类型：
--
作者：
Chung, Moo K.;Lee, Hyekyoung;Solos, Victor
大脑网络中的循环是具有冗余附加连接的连接组件的子集。如果一个连通分支中有许多圈，则该连通分支的连通性更强。连接组件的数量代表了大脑网络的整合，而循环的数量则代表了整合的强度。然而，目前还不清楚如何对大脑网络中的周期数进行统计推断。在这项研究中，我们提出了一个新的统计推断框架，通过Kolmogorov-Smirnov（KS）距离来确定循环数的重要性，该距离最近被引入，通过使用第零个Betti数来测量不同过滤值之间的网络之间的相似性。在本文中，我们展示了如何将该方法扩展到第一个Betti数，它测量循环的数量。性能分析使用具有地面实况的随机网络模拟进行。通过使用一个双胞胎成像研究，它提供了生物学的基础事实，该方法被应用于确定周期的数量是否是一个统计学上显着的遗传网络功能的休息状态的功能连接在217双胞胎从人类连接组项目。生成结果时使用的MATLAB代码以及连接矩阵可在mchung/TDA”> http://www.stat.wisc.edu/similar至mchung/TDA上找到。作者摘要在本文中，我们提出了一个新的拓扑距离的基础上Kolmogorov-Smirnov（KS）距离，适用于大脑网络，并比较它们与其他拓扑网络距离，包括Gromov-Hausdorff（GH）距离。KS-距离最近被引入来通过使用第零Betti数来测量不同过滤值的网络之间的相似性，该Betti数测量连接组件的数量。在本文中，我们展示了如何将该方法扩展到第一个Betti数，它测量循环的数量。性能分析使用具有地面实况的随机网络模拟进行。使用双成像研究，它提供了生物的地面真相（网络差异），我们证明，KS距离的第零和第一贝蒂数有能力确定遗传性。
A cycle in a brain network 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. Whereas the number of connected components represents the integration of the brain network, the number of cycles represents how strong the integration is. However, it is unclear how to perform statistical inference on the number of cycles in the brain network. In this study, we present a new statistical inference framework for determining the significance of the number of cycles through the Kolmogorov-Smirnov (KS) distance, which was recently introduced to measure the similarity between networks across different filtration values by using the zeroth Betti number. In this paper, we show how to extend the method to the first Betti number, which measures the number of cycles. The performance analysis was conducted using the random network simulations with ground truths. By using a twin imaging study, which provides biological ground truth, the methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the resting-state functional connectivity in 217 twins obtained from the Human Connectome Project. The MATLAB codes as well as the connectivity matrices used in generating results are provided at mchung/TDA">http://www.stat.wisc.edu/similar to mchung/TDA. Author SummaryIn this paper, we propose a new topological distance based on the Kolmogorov-Smirnov (KS) distance that is adapted for brain networks, and compare them against other topological network distances including the Gromov-Hausdorff (GH) distances. KS-distance is recently introduced to measure the similarity between networks across different filtration values by using the zeroth Betti number, which measures the number of connected components. In this paper, we show how to extend the method to the first Betti number, which measures the number of cycles. The performance analysis was conducted using random network simulations with ground truths. Using a twin imaging study, which provides biological ground truth (of network differences), we demonstrate that the KS distances on the zeroth and first Betti numbers have the ability to determine heritability.