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. 2023 Jul 10;26(8):1449–1460. doi: 10.1038/s41593-023-01359-8

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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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Extended Data Fig. 2 — A density plot representing the distribution of pathological accumulation of common biomarkers of ADAD. Here, separate density curves are plotted for carriers of the PSEN1 (purple) and APP (blue) mutations. While we typically think of ADAD as a homogenous form of AD, each mutation conveys a variable impact on the phenotypic expression of these common biomarkers. Values represent z-scores relative to the unimpaired mutation non-carriers. The black dashed line represents the mean value for mutation non-carriers, while colored dashed lines represent mean z-scores for each group, respectively. Amyloid deposition represents PiB SUVR uptake (n = 281), hypometabolism is derived from FDG SUVR update (n = 296), cortical atrophy is a measure of cortical thickness (n = 318), hippocampal atrophy is a measure of hippocampal volume (n = 318), clinical symptoms represent MMSE scores (n = 316), and cognitive decline represents accuracy on a composite of general cognitive tasks (n = 305). Plot demographics: n = 318, average age = 39.4, proportion females = 56%.