Topological Distances Between Brain Networks.

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

Abstract

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.

Abstract Image

Abstract Image

Abstract Image

脑网络之间的拓扑距离。
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。
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