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. 2022 Aug 22;13:4933. doi: 10.1038/s41467-022-32595-4

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© The Author(s) 2022

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. 1 — a The diffusion MRI data of 1065 subjects were used. b The data were reconstructed to calculate the diffusion distribution for fiber tracking. c For each subject, 52 white matter bundles were mapped using automated tractography. The track recognition was based on trajectory similarity with a tractography atlas without using the cortical parcellations. The tracking results were aggregated to construct a population-based probability atlas of 52 white matter pathways. d Cortical regions from cortical parcellations and the white matter trajectories of each subject were used to derive the connectome matrix. e The results from each subject were accumulated to construct the tract-region connectome based on population probability. f Hierarchical clustering was applied to the row vectors of the connectome to derive the hierarchical relation of cortical regions. g Hierarchical clustering was applied to the column vectors to derive the hierarchical relation of white matter bundles.