High-precision spatial analysis of mouse courtship vocalization behavior reveals sex and strain differences

Abstract

Mice display a wide repertoire of vocalizations that varies with sex, strain, and context. Especially during social interaction, including sexually motivated dyadic interaction, mice emit sequences of ultrasonic vocalizations (USVs) of high complexity. As animals of both sexes vocalize, a reliable attribution of USVs to their emitter is essential. The state-of-the-art in sound localization for USVs in 2D allows spatial localization at a resolution of multiple centimeters. However, animals interact at closer ranges, e.g. snout-to-snout. Hence, improved algorithms are required to reliably assign USVs. We present a novel algorithm, SLIM (Sound Localization via Intersecting Manifolds), that achieves a 2–3-fold improvement in accuracy (13.1–14.3 mm) using only 4 microphones and extends to many microphones and localization in 3D. This accuracy allows reliable assignment of 84.3% of all USVs in our dataset. We apply SLIM to courtship interactions between adult C57Bl/6J wildtype mice and those carrying a heterozygous Foxp2 variant (R552H). The improved spatial accuracy reveals that vocalization behavior is dependent on the spatial relation between the interacting mice. Female mice vocalized more in close snout-to-snout interaction while male mice vocalized more when the male snout was in close proximity to the female's ano-genital region. Further, we find that the acoustic properties of the ultrasonic vocalizations (duration, Wiener Entropy, and sound level) are dependent on the spatial relation between the interacting mice as well as on the genotype. In conclusion, the improved attribution of vocalizations to their emitters provides a foundation for better understanding social vocal behaviors.

Simultaneous recording of ultrasonic vocalizations and sniffing from socially interacting individual rats using a miniature microphone

“…Next, we validated that the separation of USVs according to the emitter was precise, even during close contact between the animals, a condition in which previous studies struggled to properly separate vocalizations made by freely interacting animals 19–21 . For this, we analyzed all video files using TrackRodent software 28 and extracted all time epochs in which the subject animal either physically contacted the stimulus animal during free interaction (Fig.…”

“…This arrangement, however, restricts the types of interaction between animals and does not allow them to freely contact each other. Other studies combined an array of microphones around the arena and performed computational analysis of the various signals to decide which animal emitted a given USV 19–21 . The efficiency of this technique was limited to cases where the heads of the animals were distant from each other by several centimeters (up to ∼1.5 cm with accuracy of 84.3% 21 ).…”

“…Other studies combined an array of microphones around the arena and performed computational analysis of the various signals to decide which animal emitted a given USV 19–21 . The efficiency of this technique was limited to cases where the heads of the animals were distant from each other by several centimeters (up to ∼1.5 cm with accuracy of 84.3% 21 ). As far as we know, our system is the first one that allows for fully reliable direct recording of vocalization made by each animal separately, regardless of its position relative to other animals.…”

“…While the first obstacle was recently solved using multiple computational tools that make use of machine learning algorithms to detect and analyze USVs (for comprehensive review see references 16,17 ), the second obstacle remained. Although recent studies using multiple arena microphones assigned USVs to specific individuals separated by a barrier 18 , it remains almost impossible to distinguish and categorize USVs emitted by several individuals during free social interactions according to the emitter 19–21 .…”

Multimodal system for recording individual-level behaviors in songbird groups

“…Also, it remains open how well vocalizations can be separated from the five stationary microphones alone. In groups of mice and using microphone arrays and a camera, it has been shown that vocalizations can be assigned to individual animals by combining sound source localization and animal position tracking 41 . But because of frequent vocal jamming in songbirds, we doubt these approaches will make the use of animal-borne transmitters obsolete in song-learning studies to be performed in the BirdPark where subtle song changes need to be tracked in developmental time.…”