ElasticPath2Path: Automated morphological classification of neurons by elastic path matching

TL;DR

ElasticPath2Path is a novel method that accurately classifies neuron morphology by combining graph metrics and elastic path matching, improving over existing techniques without simplifying neuronal structures.

Contribution

It introduces a new elastic path matching framework that integrates graph-theoretic and differential geometry approaches for neuronal classification.

Findings

Outperforms existing methods in neuronal classification accuracy

Effectively captures complex neuronal morphology without structural simplification

Provides a robust metric for comparing neuronal structures

Abstract

In the study of neurons, morphology influences function. The complexity in the structure of neurons poses a challenge in the identification and analysis of similar and dissimilar neuronal cells. Existing methodologies carry out structural and geometrical simplifications, which substantially change the morphological statistics. Using digitally-reconstructed neurons, we extend the work of Path2Path as ElasticPath2Path, which seamlessly integrates the graph-theoretic and differential-geometric frameworks. By decomposing a neuron into a set of paths, we derive graph metrics, which are path concurrence and path hierarchy. Next, we model each path as an elastic string to compute the geodesic distance between the paths of a pair of neurons. Later, we formulate the problem of finding the distance between two neurons as a path assignment problem with a cost function combining the graph metrics and the geodesic deformation of paths. ElasticPath2Path is shown to have superior performance over the state of the art.