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. 2020 Sep 30;10:16096. doi: 10.1038/s41598-020-72317-8

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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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Overview of methods. (A) We searched for the topic “DMN” in Neurosynth to create a functional mask, and then constrained the mask to 4 anatomical regions that belong to the DMN by using the Harvard–Oxford probabilistic atlas. (B) We applied k-means clustering to determine functionally different subregions within each of the 4 regions. (C) We generated whole-brain coactivation profiles to reveal functional networks for different regions. (D) Functional profiles were generated to identify which cognitive processes or health conditions best predicted each region’s (or subregion’s) activity. Figure was created using Python and Adobe Illustrator.