Abstract
The study of vocal communication in animal models provides key insight to the neurogenetic basis for speech and communication disorders. Current methods for vocal analysis suffer from a lack of standardization, creating ambiguity in cross-laboratory and cross-species comparisons. Here, we present VoICE (Vocal Inventory Clustering Engine), an approach to grouping vocal elements by creating a high dimensionality dataset through scoring spectral similarity between all vocalizations within a recording session. This dataset is then subjected to hierarchical clustering, generating a dendrogram that is pruned into meaningful vocalization “types” by an automated algorithm. When applied to birdsong, a key model for vocal learning, VoICE captures the known deterioration in acoustic properties that follows deafening, including altered sequencing. In a mammalian neurodevelopmental model, we uncover a reduced vocal repertoire of mice lacking the autism susceptibility gene, Cntnap2. VoICE will be useful to the scientific community as it can standardize vocalization analyses across species and laboratories.
Highlights
We present a method for the semi-automatic clustering of finch song syllables and mouse ultrasonic vocalizations (USVs) through hierarchical clustering and automated dendrogram trimming
We provide a method for quantifying the syntactical similarity between bird songs and mouse USVs to assess the impact of experimental manipulation on vocal behavior
Our initial goal was to develop a method for grouping similar vocalizations together in the absence of input from the user, in order to generate a vocal syntax in an unbiased fashion
Summary
In contrast to the stereotyped adult songs of zebra finches, a high degree of call-to-call variability exists in the mouse ultrasonic repertoire Despite this variability, 10 distinct retrieval call categories have been defined and adopted (or modified), allowing for quantitative analyses of vocal signals generated by neonatal mice[12]. Use of VoICE replicates the finding of reduced numbers of retrieval calls in pups lacking the Cntnap[2] gene, an established model of autism[15], and uncovers changes in the repertoire of these animals These findings establish this approach as a reliable, high-throughput method that faithfully captures known features of avian and rodent vocalizations and is capable of uncovering novel changes in this critical phenotypic trait
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