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. 2023 Oct 24;14:6744. doi: 10.1038/s41467-023-42053-4

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© The Author(s) 2023

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. 7 — a The increments (blue) and increase percentages (red) of prediction accuracy with the addition of high-frequency eigenmodes for unimodal and transmodal regions. b The spatial distribution of the top 10% of nodes with the highest percentage increases in prediction accuracy. c Growth curves of prediction accuracy for unimodal and transmodal groups. The horizontal axis indicates the proportion of the high-frequency eigenmodes added to the prediction model while the vertical axis is the percentage increase in R. At each adding step, the high-frequency eigenmode is randomly selected (10 repetitions). The average percentage increase is shown in solid lines while standard deviations are expressed as dash lines. Source data are provided as a Source Data file.