# Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling

## TL;DR

This paper introduces a method to create realistic and watertight 3D models of neurons for detailed simulations of brain cell functions.

## Contribution

A robust method for generating realistic and watertight neuronal meshes from morphological data is presented.

## Key findings

- The method successfully generates watertight meshes for various cortical neuron morphologies.
- The approach is extended to synthetic astrocytic morphologies with plausible biological detail.
- Volumetric meshes are created for scalable in silico reaction-diffusion simulations.

## Abstract

Understanding the intracellular dynamics of brain cells entails performing three-dimensional molecular simulations incorporating ultrastructural models that can capture cellular membrane geometries at nanometer scales. While there is an abundance of neuronal morphologies available online, e.g. from NeuroMorpho.Org, converting those fairly abstract point-and-diameter representations into geometrically realistic and simulation-ready, i.e. watertight, manifolds is challenging. Many neuronal mesh reconstruction methods have been proposed; however, their resulting meshes are either biologically unplausible or non-watertight. We present an effective and unconditionally robust method capable of generating geometrically realistic and watertight surface manifolds of spiny cortical neurons from their morphological descriptions. The robustness of our method is assessed based on a mixed dataset of cortical neurons with a wide variety of morphological classes. The implementation is seamlessly extended and applied to synthetic astrocytic morphologies that are also plausibly biological in detail. Resulting meshes are ultimately used to create volumetric meshes with tetrahedral domains to perform scalable in silico reaction-diffusion simulations for revealing cellular structure–function relationships. Availability and implementation: Our method is implemented in NeuroMorphoVis, a neuroscience-specific open source Blender add-on, making it freely accessible for neuroscience researchers.