Exploring morphological motifs for a single neuron based on multiple 3D reconstructions

Abstract

The morphology of individual neurons is useful to study structures and functions of nervous system. Researchers have invented many semi or fully automatic tracing methods to efficiently generate a reconstruction from a single neuron. Different tracing methods have different design principles, and could produce different reconstructions. However, the \"common substructures\" of various reconstructions, called morphological motifs, should be highly reliable. In this work, we propose a Vaa3D based framework to explore morphological motifs of 3D reconstructions from a single neuron. The framework contains four steps: (1) resampling and sorting each reconstruction according to a standard reconstruction, such as a gold standard reconstruction, consensus reconstruction or a certain reliable reconstruction; (2) applying local alignment algorithm for each pair of the standard reconstruction and a reconstruction, or each pair of two reconstructions; (3) constructing overlaps based on selected points in local alignment pairs; (4) obtaining morphological motifs by post-processing these overlaps. Under the proposed framework, three methods were implemented and tested on a dataset of 73 fruitfly neurons released by the BigNeuron project (http://bigneuron.org), which contains a gold standard reconstruction, a consensus reconstruction and about 40 automatic reconstructions for each neuron. We quantitatively evaluated these three methods to choose reliable morphological motifs.