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. 2021 Mar 8;4:301. doi: 10.1038/s42003-021-01832-9

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© The Author(s) 2021

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. 3 — The informational content of each network in terms of percent maximum of network normalized behavior and disease entropy. High disease entropy corresponds to a network that is associated with a higher variety of diseases [from n = 43 International Classification of Disease (10th version) diagnostic categories] and is non-specific to one or few diseases. High behavior entropy corresponds to a functional network associated with a high variety of Behaviors (from n = 56 BrainMap behavior domains) and is also not specialized. Only those n = 15 networks that are significantly matched between co-alteration and task-activation ICA are displayed; the size of data points is proportional to their spatial correlation (0.31 ≤ r ≤ 0.62). The linear model a had the following parameters: β = 0.60; p = 0.0006; df = 13; Adjusted R² = 0.53. Panel b excludes the medial visual network from view, where color corresponds to the fitted values of the model. NN network normalized, MDN medial dorsal nucleus, DMN default-mode network, Thal. thalamus, L./R. left/right, Cent. central. Source data are provided in Supplementary Data 1.