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. 2022 Dec 20;22:810. doi: 10.1186/s12888-022-04450-9

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — Global network properties of D-AD and nD-AD patients for different sparsity thresholds. (A) Small-worldness; (B) network global efficiency; (C) network local efficiency. The three groups (D-AD, nD-AD, and NC) show properties that are typical of small-world networks. Global network properties are similar between AD patients with depression and those without depression. The NC groups presents significantly larger global and local efficiency than D-AD and nD-AD groups. nD-AD, non-depressed AD patients; D-AD, depressed AD patients; NC, normal controls