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. 2018 Aug 16;8:12269. doi: 10.1038/s41598-018-30869-w

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© The Author(s) 2018

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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Scatterplot of leaf fraction for eyes-closed and eyes-open resting-state, based on scalp (top row) and source analysis (bottom row) and for each connectivity measure (different columns). The blue and orange arrows indicate the direction of the shift in network topology when comparing the eye-closed and eyes-open condition. Note that for the AEC and PLV the leaf fraction is higher in the eyes-open condition than in the eyes-closed condition for the scalp-level MSTs, whereas the opposite condition effect is found for the source-level MSTs.