脑网络之间的拓扑距离。

Moo K Chung, Hyekyoung Lee, Victor Solo, Richard J Davidson, Seth D Pollak
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引用次数: 42

摘要

许多现有的大脑网络距离是基于矩阵规范的。元素方面的差异可能无法捕获潜在的拓扑差异。此外,矩阵规范对异常值敏感。一些极端的边权值可能会严重影响距离。因此,有必要开发能够识别拓扑结构的网络距离。本文引入了Gromov-Hausdorff (GH)和Kolmogorov-Smirnov (KS)距离。在基于持续同源的脑网络模型中,常使用高距离。在随机网络仿真中,对比了ks距离与矩阵范数和gh距离的优越性能。然后将ks距离用于表征受虐儿童的多模态MRI和DTI研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Topological Distances Between Brain Networks.

Topological Distances Between Brain Networks.

Topological Distances Between Brain Networks.

Topological Distances Between Brain Networks.

Many existing brain network distances are based on matrix norms. The element-wise differences may fail to capture underlying topological differences. Further, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children.

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