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. 2024 Jun 4;15:4745. doi: 10.1038/s41467-024-48781-5

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© The Author(s) 2024

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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Fig. 5 — Pipeline PDiv (portrait divergence) in a given dataset is plotted against the proportion of participants in the same dataset for whom the within-subject PDiv (baseline vs follow up) is smaller than between-subject PDiv. Each data-point represents one pipeline (n = 768). Pipelines above the red line meet the within-between criterion such that portrait divergence is smaller for within-subject test–retest compared to between subject comparisons. Shading indicates standard error of the fitted line to model the linear relationship between the two respective variables. Spearman correlation coefficient (two-sided) is used to assess the strength of relationship and its statistical significance. Source data are provided as a Source Data file.