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. 2020 Nov 30;10:144. doi: 10.1186/s13550-020-00733-y

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© The Author(s) 2020

Open AccessThis 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 — Galleria of individual examples with both semi-quantitative analyses to conventional and digital controls. Three examples of SPM-T map images from semi-quantitative analyses to conventional (left panel) and digital (right panel) controls of patients with AD (a 74-year-old woman with MMSE score of 19, b 70-year-old woman with MMSE score of 21, c 66-year-old man with MMSE score of 23). One example of SPM-T map images from semi-quantitative analyses to conventional (left panel) and digital (right panel) controls from a healthy subject (d 54-year-old woman with MMSE score of 29). SPM-T maps are projected onto two-dimensional slices of T1-weighted MRIs (from the base to the top of the skull)