Robust Cable Models for Complex Neuronal Morphologies

We present a free and open-source, semi-automated, topologically robust pipeline for fitting cable models to 3D surface mesh morphology data of neuronal membranes, particularly suited to structures with complex shapes and topological holes. The motivation for this work is the discovery of morphologically complex neuronal spines on the auditory space-specific neurons of the barn owl, dubbed "toric spines", notable for their high curvature, branching density, and topological holes. Multicompartmental simulation software requires morphology to be represented as cable models, yet existing software tools for fitting cable models to complex 3D surface meshes generally assume acyclic topology, and thus did not work well for toric spines. We present the Mesh and Skeleton Cable Fitting (MASCAF) pipeline and software, which fits a cable model to a closed 3D surface mesh using mean-curvature flow skeletonization, and automatically handles cyclic topology. In this paper, we demonstrate how MASCAF is applied to fit cable models, how loop connections can be reconstructed in multicompartmental simulation software, and how the results can be validated using geometry and simulator-based methods. While non-tree morphologies such as toric spines are neuroanatomically special, our software pipeline provides a cable-model fitting approach for surface mesh data that is topologically robust, deterministic, open-source, and applicable to general morphologies, thereby closing a crucial gap between neuronal imaging and high-resolution simulation.