Figure 1 3D heat maps generated by GBM

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Figure 1 3D heat maps generated by GBM Figure 1 3D heat maps generated by GBM. (A) Entorhinal cortex; (B) parahippocampal gyrus; (C) inferior temporal ... Figure 1 3D heat maps generated by GBM. (A) Entorhinal cortex; (B) parahippocampal gyrus; (C) inferior temporal cortex; (D) posterior cingulate; and (E) the prefrontal region of interest. For each heat map, four age ranges are represented: 50–59, 60–69, 70–79 and 80–89 years. Flortaucipir SUVR is on the x-axis, cortical thickness (inverted scale) on the y-axis and predicted memory z-score on the z-axis as depicted by the colour code for each individual graph. Heat map ranges are set at the 3rd and 97th percentiles of the distribution of each biomarker by decade of age. The key findings are that the entorhinal cortex shows the strongest associations between the biomarkers and memory z-score. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 February 2019, Pages 1148–1160, https://doi.org/10.1093/brain/awz025 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 2 2D renderings of relationships of flortaucipir SUVR, cortical thickness and memory z-scores for entorhinal ... Figure 2 2D renderings of relationships of flortaucipir SUVR, cortical thickness and memory z-scores for entorhinal cortex. Predicted memory z-score is shown on the y-axis, while (A) cortical thickness (inverted scale) and (B) flortaucipir SUVR are on the x-axis. A family of curves is shown in colour code that represents: (A) different levels of flortaucipir SUVR and (B) cortical thickness. These two sets of graphs represent in 2D that which is depicted in Fig. 1A in 3D. As with the heat maps, the ranges for depicted values were set at the 3rd and 97th percentiles of the distribution of each biomarker by decade of age. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 February 2019, Pages 1148–1160, https://doi.org/10.1093/brain/awz025 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 3 3D heat maps generated by GBM for entorhinal cortex broken down by three levels of global PIB SUVR. For each, ... Figure 3 3D heat maps generated by GBM for entorhinal cortex broken down by three levels of global PIB SUVR. For each, four age ranges are represented: 50–59, 60–69, 70–79 and 80–89 years. Flortaucipir SUVR is on the x-axis, Cortical thickness on the y-axis (inverted scale) and predicted memory z-score on the z-axis as depicted by the colour code for each individual graph. Heat map ranges were set at the 3rd and 97th percentiles of the distribution of each biomarker by decade of age. The main finding illustrated is that the lowest memory performance, as indicated on the heat map occurred with the lowest levels of cortical thickness, the highest levels of flortaucipir SUVR in the setting of PIB SUVR >1.8. Also illustrated here is the relationship between global PIB SUVR and the distributions of entorhinal cortex flortaucipir SUVR and entorhinal cortex cortical thickness. Flortaucipir SUVR >1.4 was not seen in the lowest global PIB SUVR strata, and very low flortaucipir SUVR was infrequent in the highest global PIB SUVR strata. Similarly, while all levels of cortical thickness were seen in the lowest two global PIB SUVR strata, very thick entorhinal cortex cortical thickness levels were infrequent in the highest global PIB SUVR strata. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 February 2019, Pages 1148–1160, https://doi.org/10.1093/brain/awz025 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 4 3D heat maps generated by GBM for entorhinal cortex Figure 4 3D heat maps generated by GBM for entorhinal cortex. (A) Global PIB SUVR versus flortaucipir SUVR; and (B) ... Figure 4 3D heat maps generated by GBM for entorhinal cortex. (A) Global PIB SUVR versus flortaucipir SUVR; and (B) global PIB SUVR versus cortical thickness. For each, four age ranges are represented: 50–59, 60–69, 70–79 and 80–89 years. Global PIB SUVR is on the x-axis in A and B. In A, flortaucipir SUVR is on the y-axis, and in B, cortical thickness (inverted scale) is on the y-axis. Predicted memory z-scores are represented on the z-axis as depicted by the colour code for each individual graph. The heat maps’ ranges were set at the 3rd and 97th percentiles of the distribution of each biomarker by decade of age. The third pairing of biomarker—entorhinal cortex flortaucipir SUVR versus cortical thickness—heat map is found in Fig. 1A. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 February 2019, Pages 1148–1160, https://doi.org/10.1093/brain/awz025 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 5 3D heat maps generated by GBM for entorhinal cortex Figure 5 3D heat maps generated by GBM for entorhinal cortex. (A) Flortaucipir SUVR versus cortical thickness ... Figure 5 3D heat maps generated by GBM for entorhinal cortex. (A) Flortaucipir SUVR versus cortical thickness (inverted scale); (B) regional (entorhinal) PIB SUVR versus flortaucipir SUVR; and (C) regional (entorhinal) PIB SUVR versus cortical thickness. For each, four age ranges are represented: 50–59, 60–69, 70–79 and 80–89 years. Predicted memory z-scores are represented on the z-axis as depicted by the colour code for each individual graph. Heat map ranges were set at the 3rd and 97th percentiles of the distribution of each biomarker. The flortaucipir SUVR versus cortical thickness heat map (A) was similar to that seen (Fig. 1A) when global PIB SUVR was in the model. The entorhinal cortex regional PIB SUVR versus cortical thickness heat map (C) was also similar to that seen (Fig. 4B) with global PIB. However, the heat map of regional PIB SUVR versus regional flortaucipir showed a much attenuated relationship with PIB SUVR compared to the heat map with global PIB SUVR (Fig. 4A), suggesting that entorhinal cortex PIB SUVR and flortaucipir SUVR share more variance than do global PIB SUVR and entorhinal cortex flortaucipir SUVR. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 February 2019, Pages 1148–1160, https://doi.org/10.1093/brain/awz025 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 6 The bivariate relationships between each biomarker Figure 6 The bivariate relationships between each biomarker. (A) Entorhinal cortical thickness (inverted scale); (B) ... Figure 6 The bivariate relationships between each biomarker. (A) Entorhinal cortical thickness (inverted scale); (B) Entorhinal flortaucipir SUVR; and (C) global PIB SUVR and predicted memory z-score (y-axis) are shown on the top row. The curves are right-truncated at the 10th smallest or 10th largest of the distribution of each biomarker. The y-axis values for memory z-scores range from +0.5 to −1.5. The small vertical lines on the x-axis each represent individual participants. In the bottom row are shown scatterplots of the distribution for biomarkers in three groups: cognitively unimpaired participants between the ages of 50 and 59 years, non-demented participants 60 years and older; and subjects with Alzheimer’s disease (AD) dementia (n = 83) from the Mayo Clinic Alzheimer’s Disease Research Center and MCSA. The latter group was not included in GBM modelling and is shown here for comparison purposes. The figure shows the relationships of the ‘critical segments’ of each biomarker’s range (i.e. the range where memory z-scores show the greatest decline in non-demented study participants). ERC = entorhinal cortex. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Brain, Volume 142, Issue 4, 12 February 2019, Pages 1148–1160, https://doi.org/10.1093/brain/awz025 The content of this slide may be subject to copyright: please see the slide notes for details.