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

TL;DR

This study systematically analyzes over 11,500 neural cell reconstructions across species and types to establish morphological benchmarks, enhancing the interpretation of diffusion MRI signals in grey matter microstructure research.

Contribution

It provides the first comprehensive reference dataset of neural cell morphological features and explores their detectability with diffusion MRI, aiding neuroimaging interpretation.

Findings

Identified key morphological traits distinguishing neural cell types.

Established reference values for structural, shape, and topological features.

Linked cellular features to diffusion MRI detectability.

Abstract

The structure of grey matter has long been 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 non-invasive tool for examining cellular microstructure in vivo. However, for dMRI to accurately reflect cellular features, it is crucial to determine which aspects of morphology influence its measurements. Proper interpretation of dMRI data depends on understanding its sensitivity to different cellular characteristics. Despite growing interest in cellular morphology, there has been no systematic report on the key features defining different neural cell types. To address this, we analyzed over 11,500 three-dimensional cellular reconstructions across three species and nine cell types, establishing reference values for critical morphological traits. These traits fall into three categories: structural features that define the cell's skeletal framework, shape features that describe spatial organization, and topological features that break down cellular structure to distinguish cell types. Beyond reporting these reference values, we examine their relevance for dMRI, identifying which neural features dMRI can detect and which cell types may be distinguishable. To complement the statistical analysis, we also provide high resolution 3D surface meshes representative of each cell type and species. This work provides essential benchmarks for grey matter research, offering new guidelines on linking neuroimaging measurements to neurobiology. These reference values will be a valuable resource for neuroscientists and neuroimaging researchers, aiding in the interpretation of imaging data and the refinement of brain tissue models.