DOI: 10.5281/zenodo.8316357
title: Enhanced atlases and flatmaps of rodent neocortex

description: <strong>Flatmap of mouse isocortex and barrel column annotations in CCFv3 space (10 µm resolution)</strong> Contents of mouse_isocortex_enhanced.zip: <strong>add_barrel_annotations.py</strong> Python script to add barrel annotations to the annotations file from CCFv3. <strong>annotation_barrels.feather</strong> Table for barrel annotations, with voxel positions, flat positions, layers and hemisphere information. <strong>annotation_barrels.nrrd</strong> Annotation of individual barrels and barrel columns per layer. <strong>depth.nrrd</strong> Streamline-derived absolute cortical depth in µm. <strong>flatmap.nrrd</strong> Mapping of voxel centers in a single hemisphere to 2D coordinates in the range X,Y=[0,1]. <strong>flatmap_both.nrrd</strong> Mapping of voxel centers in both hemispheres to 2D coordinates in the range X=[0,2], Y=[0,1]. <strong>flatplot.py</strong> Python script to plot volumetric data in flat space (flat view), using the package datashader. <strong>hierarchy.json</strong> Region hierarchy in AIBS format extended with barrel annotations. <strong>thickness.nrrd</strong> Streamline-derived cortical thickness in µm. <strong><em>Add barrel annotations to CCFv3</em></strong> Requires data file annotation_10.nrrd from CCFv3 release and Python package voxcell. The script introduces annotations of individual barrels and barrel columns (split per layer) in the SSp-bfd region. The input files are the 10 µm annotation volume from CCFv3, and the provided <em>barrel_annotations.nrrd</em>. The output is consistent with the provided extended hierarchy file <em>hierarchy.json</em>. <pre><code class="language-bash">python add_barrel_annotations.py annotation_10.nrrd annotation_barrels.nrrd annotation_barrels_10.nrrd</code></pre> <em><strong>Generate flat views of volumetric data</strong></em> The provided script <em>flatplot.py</em> requires the following Python packages: voxcell, datashader, colorcet, seaborn (optional). For example, to generate a flat view of mean cortical thickness, run the following command: <pre><code class="language-bash">python flatplot.py --autospan -r mean -c sns:viridis flatmap_both.nrrd thickness.nrrd thickness</code></pre> <strong>Enhanced atlas and flatmap of P14 rat somatosensory cortex</strong> Contents of rat_sscx_enhanced.zip: <strong>brain_regions.nrrd</strong> Region annotations for rat somatosensory cortex. <strong>example.py</strong> Example Python script for loading and analyzing atlas datasets, using the package voxcell. <strong>flatmap.nrrd</strong> Mapping of voxel centers to flat 2D coordinates in the range X,Y=[0,1]. <strong>hexgrid.nrrd</strong> Decomposition of somatosensory cortex into 263 hexagonal columns of approximately equal size. <strong>hierarchy.json</strong> Region hierarchy in AIBS format. <strong>orientation.nrrd</strong> Local orientation towards pia, stored as a quaternion. <strong>relative_depth.nrrd</strong> Relative cortical depth in the range [0,1]. <strong>shells.nrrd</strong> Boundaries of somatosensory cortex (top, bottom, sides). <strong>thickness.nrrd</strong> Cortical thickness in µm. Together, relative depth and flat coordinates make a 3D coordinate system adapted to the shape of the somatosensory cortex. The principal axis (local orientation) is orthogonal to layer boundaries, and the remaining two axes span the flat space and are locally orthogonal to the principal axis.
description: This work was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government's ETH Board of the Swiss Federal Institutes of Technology.