Topological Sholl Descriptors For Neuronal Clustering and Classification

Abstract

MotivationGiven that neuronal morphology can widely vary among cell classes, brain regions, and animal species, accurate quantitative descriptions allowing classification of large sets of neurons is essential for their structural and functional characterization. However, robust and unbiased computational methods currently used to characterize groups of neurons are scarce. ResultsIn this work, we introduce a novel and powerful technique to study neuronal morphologies. We develop mathematical descriptors that quantitatively characterize structural differences among neuronal cell types and thus allow for their accurate classification. Each Sholl descriptor that is assigned to a neuron is a function of a distance from the soma with values in real numbers or more general metric spaces. To illustrate the use of Sholl descriptors, six datasets were retrieved from the large public repository http://neuromorpho.org/ comprising neuronal reconstructions from different species and brain regions. Sholl descriptors were subsequently computed, and standard clustering methods enhanced with detection and metric learning algorithms were then used to objectively cluster and classify each dataset. Importantly, our descriptors outperformed conventional techniques and thus provide a practical and effective approach to the classification of diverse neuronal cell types, with the potential for discovery of subclasses of neurons.