Large scale similarity search across digital reconstructions of neural morphology

Abstract

Most functions of the nervous system depend on neuronal and glial morphology. Continuous advances in microscopic imaging and tracing software have provided an increasingly abundant availability of 3D reconstructions of arborizing dendrites, axons, and processes, allowing their detailed study. However, efficient, large-scale methods to rank neural morphologies by similarity to an archetype are still lacking. Using the NeuroMorpho.Org database, we present a similarity search software enabling fast morphological comparison of hundreds of thousands of neural reconstructions from any species, brain regions, cell types, and preparation protocols. We compared the performance of different morphological measurements: 1) summary morphometrics calculated by L-Measure, 2) persistence vectors, a vectorized descriptor of branching structure, 3) the combination of the two. In all cases, we also investigated the impact of applying dimensionality reduction using principal component analysis (PCA). We assessed qualitative performance by gauging the ability to rank neurons in order of visual similarity. Moreover, we quantified information content by examining explained variance and benchmarked the ability to identify occasional duplicate reconstructions of the same specimen. The results indicate that combining summary morphometrics and persistence vectors with applied PCA provides an information rich characterization that enables efficient and precise comparison of neural morphology. The execution time scaled linearly with data set size, allowing seamless live searching through the entire NeuroMorpho.Org content in fractions of a second. We have deployed the similarity search function as an open-source online software tool both through a user-friendly graphical interface and as an API for programmatic access.