Decoding Grey Matter - Meshes

<p dir="ltr">The following meshes are associated with the paper 'Decoding Gray Matter: large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals'</p><p dir="ltr">Grey matter structure is a key focus in neuroscience, as cell morphology varies by type and can be affected by neurological conditions. Understanding these variations is essential for studying brain function and disease. Diffusion-weighted MRI (dMRI) is a powerful tool for examining cellular microstructure in vivo, but its accuracy depends on identifying which morphological features influence its measurements. Despite growing interest, no systematic report has defined key neural cell traits.</p><p dir="ltr">We analysed more than 11,800 3D cellular reconstructions, obtained from <a href="https://neuromorpho.org/" rel="noreferrer" target="_blank">Neuromorpho.org</a>, across three species and nine cell types, establishing reference values for critical traits. </p><p dir="ltr">To complement the statistical analysis, here we also provide high resolution 3D surface meshes representative of each cell type and species. For each cell type we provide 50 .ply meshes and the corresponding cellular skeleton saved as an .swc file. All meshes were meshed in blender using matlab scripts, which are publicly available on <a href="https://github.com/Charlie-Aird/Decoding-Grey-Matter" rel="noreferrer" target="_blank">github</a>. These meshes are fully compatible with Monte Carlo simulators, offering a valuable resource for the modelling community.</p>