Decoding Gray Matter: large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals

Aird-Rossiter, Charlie, Zhang, Hui, Alexander, Daniel C., Jones, Derek K. ORCID: https://orcid.org/0000-0003-4409-8049 and Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967 2025. Decoding Gray Matter: large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals. Communications Biology Item availability restricted.

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Abstract

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. We analysed more than 11,800 3D cellular reconstructions across three species and nine cell types, establishing reference values for critical traits. These fall into three categories: structural, shape, and topological features. Beyond defining these traits, we assess their relevance for dMRI, identifying which neural features it can be sensitive to. This work provides essential benchmarks for gray matter research, aiding in the interpretation of neuroimaging data and improving brain tissue models. To complement the statistical analysis, we also provide high resolution 3D surface meshes representative of each cell type and species. These meshes are fully compatible with Monte Carlo simulators, offering a valuable resource for the modelling community.

Item Type:	Article
Status:	In Press
Schools:	Schools > Computer Science & Informatics Schools > Psychology Research Institutes & Centres > Cardiff University Brain Research Imaging Centre (CUBRIC)
Publisher:	Nature Research
Date of First Compliant Deposit:	24 October 2025
Date of Acceptance:	23 October 2025
Last Modified:	27 Oct 2025 15:15
URI:	https://orca.cardiff.ac.uk/id/eprint/181880

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