期刊
CONNECTOMICS IN NEUROIMAGING
卷 10511, 期 -, 页码 161-170出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-67159-8_19
关键词
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资金
- NIH [MH61285, MH68858, MH84051, UL1TR000427]
- Brain Initiative Grant [EB022856]
- Basic Science Research Program through the National Research Foundation (NRF) of Korea [NRF-2016R1D1A1B03935463]
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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