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. 2019 Oct 30;10:4937. doi: 10.1038/s41467-019-12915-x

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — Robustness analysis using node deletion for the lower resolution connectivity matrices ( $116 \times 116$ ) generated using the AAL atlas and MGH-USC DSI datasets. The size of the largest component, global efficiency and entropy are computed (with or without transformation of the connectivity matrix weights) after targeted removal of nodes with decreasing strength, betweenness centrality, or curvature. The top row shows results for the original connectivity matrix while the bottom row shows results after Gaussian transformation of its weights as done in Alstott et al.¹¹