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. 2020 Nov 16;10:19843. doi: 10.1038/s41598-020-76935-0

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© The Author(s) 2020

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 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/.

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Global efficiency is reduced by context and context/social exposure. (A) Small world index at various densities. (B) Clustering coefficient at various densities. (C) Characteristic path length at various densities. (D) Global efficiency at a threshold of 10%. (E–G) 3D connectome maps with nodes scaled by local efficiency in control (E), context exposed (F), and context/social exposed (G). Sale bar represent edge weights values (Pearson r threshold 0.15). (H) Distribution of local efficiency vs. clustering coefficient values for all nodes and all subjects in control, context and context/social exposed rats. (I) Distribution of search information vs. path transitivity values for all edges and all subjects in control, context and context/social exposed rats. (J) Distribution of number of steps or hops between node pairs vs. routing efficiency values for all edges and all subjects in control, context and context/social exposed rats. Matrix density threshold for (D–J) is 10%. In (B,C), blue, orange, red line plots are for randomized (r) versions of functional connectivity matrices. In (H–J), gray color dots represent data points for nodes or edges of randomized graphs. Data are mean ± standard error. *Context and **context/social group significantly different from control (ANOVA with Tukey–Kramer post hoc test).