MASCAF: A Cable Model Fitting Pipeline for Topologically Complex Surface Meshes.

Area of Science:

• Computational Neuroscience
• Neuroscience
• Biophysics

Background:

• Multicompartmental simulations require neuronal morphology as cable models (e.g., SWC format).
• Existing tools struggle with complex 3D surface meshes, especially those with topological holes like toric spines.
• Toric spines in barn owl auditory neurons exhibit high curvature, branching density, and loops, posing challenges for modeling.

Purpose of the Study:

• To present a robust pipeline for fitting cable models to complex 3D neuronal surface meshes.
• To address limitations in existing software for handling non-tree morphologies and topological holes.
• To enable high-resolution simulations of morphologically complex neurons.

Main Methods:

• Developed the Mesh and Skeleton Cable Fitting (MASCAF) pipeline.
• Utilized mean-curvature flow skeletonization for fitting cable models to surface meshes.
• Demonstrated reconstruction of loops in Arbor and NEURON simulation software.

Main Results:

• MASCAF successfully fits cable models to complex 3D surface meshes, including those with holes.
• The pipeline reconstructs loop structures, enabling simulation of non-tree morphologies.
• Validation using geometric and simulator-based methods confirms the accuracy of the fitted models.

Conclusions:

• MASCAF provides a topologically robust, deterministic, and open-source solution for cable model fitting.
• The pipeline bridges the gap between neuronal imaging data and high-resolution simulation capabilities.
• Applicable to general neuronal morphologies, enhancing computational neuroscience research.