Deficiency of the paternally inherited gene Magel2 alters the development of separation-induced vocalization and maternal behavior in mice.

The behavior of offspring results from the combined expression of maternal and paternal genes. Genomic imprinting silences some genes in a parent-of-origin specific manner, a process that, among all animals, occurs only in mammals. How genomic imprinting affects the behavior of mammalian offspring, however, remains poorly understood. Here, we studied how the loss of the paternally inherited gene Magel2 in mouse pups affects the emission of separation-induced ultrasonic vocalizations (USV). Using quantitative analysis of more than 1000 USVs, we characterized the rate of vocalizations as well as their spectral features from postnatal days 6-12 (P6-P12), a critical phase of mouse development that covers the peak of vocal behavior in pups. Our analyses show that Magel2 deficient offspring emit separation-induced vocalizations at lower rates and with altered spectral features mainly at P8. We also show that dams display altered behavior towards their own Magel2 deficient offspring at this age. In a test to compare the retrieval of two pups, dams retrieve wildtype control pups first and faster than Magel2 deficient offspring. These results suggest that the loss of Magel2 impairs the expression of separation-induced vocalization in pups as well as maternal behavior at a specific age of postnatal development, both of which support the pups' growth and development.

Analysis of ultrasonic vocalizations from mice using computer vision and machine learning.

Mice emit ultrasonic vocalizations (USVs) that communicate socially relevant information. To detect and classify these USVs, here we describe VocalMat. VocalMat is a software that uses image-processing and differential geometry approaches to detect USVs in audio files, eliminating the need for user-defined parameters. VocalMat also uses computational vision and machine learning methods to classify USVs into distinct categories. In a data set of >4000 USVs emitted by mice, VocalMat detected over 98% of manually labeled USVs and accurately classified ≈86% of the USVs out of 11 USV categories. We then used dimensionality reduction tools to analyze the probability distribution of USV classification among different experimental groups, providing a robust method to quantify and qualify the vocal repertoire of mice. Thus, VocalMat makes it possible to perform automated, accurate, and quantitative analysis of USVs without the need for user inputs, opening the opportunity for detailed and high-throughput analysis of this behavior.