Abstract
SummaryNeural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish.Video
Highlights
Correlating the functional properties and behavioral relevance of neurons with their cell type is a basic activity in neural circuit research
Neuron images were thresholded and skeletonized (Lee et al, 1994) using Fiji (Schindelin et al, 2012), thresholded images were converted to the point and tangent vector representation (Masse et al, 2012) using our R package nat (Jefferis and Manton, 2014), and tangent vectors were computed as the first eigenvector of a singular value decomposition (SVD) of each point and its five nearest neighbors
NBLAST Scores Can Distinguish Kenyon Cell Classes We investigated whether NBLAST scores can be used to cluster neurons, potentially revealing functional classes
Summary
Correlating the functional properties and behavioral relevance of neurons with their cell type is a basic activity in neural circuit research. While there is no universally accepted definition of neuron type, key descriptors include morphology, position within the nervous system, genetic markers, connectivity, and intrinsic electrophysiological signatures (Migliore and Shepherd, 2005; Bota and Swanson, 2007; Rowe and Stone, 1977). Despite this ambiguity, neuron type is a key abstraction, helping to reveal organizational principles and enabling results to be compared and collated across research groups. Genetic approaches to sparse and combinatorial labeling have enabled increasingly large-scale characterization of single-neuron morphology (Jefferis and Livet, 2012)
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