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. 2020 Oct 20;7:358. doi: 10.1038/s41597-020-00692-y

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© The Author(s) 2020

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. 2 — Validation of neuroanatomical heterogeneity within the dataset. In (a), an example of a reconstructed image from the dataset following X-ray acquisition, highlighting the regions of interest in the sample. From top to bottom: somatosensory cortex (CTX); striatum (STR); the thalamic reticular nucleus (TRN) and the ventral posterior nucleus (VP) of thalamus; zona incerta (ZI); and hypothalamus (HYP). (b) Examples of the microstructures identified manually within the different ROIS, including cells, axons and blood vessels. These examples each span a roughly 300 × 300 micron field-of-view and highlight the architectural diversity within and across regions of interest. (c) The distribution of pixel intensities across four selected regions of interest within the dataset (CTX, STR, TRN, ZI). (d) The distribution of pixels divided by underlying microstructure class (cell, blood vessel, axon) within each region of interest. In (e,f), we show the KL-divergence between: the pixel intensity distributions across the selected regions (e), and the microstructural composition of selected regions as measured with dense manual annotations (f).