# Interplay between ultrasonic vocalisation characteristics and behavioural contexts in female mice

Elodie Ey, Fabrice de Chaumont, Benoît Forget, Yann Hérault, Thomas Bourgeron

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Elodie Ey, Fabrice de Chaumont, Benoît Forget, Yann Hérault, Thomas Bourgeron. Interplay between ultrasonic vocalisation characteristics and behavioural contexts in female mice. NeuroFrance 2023, May 2023, Lyon, France. **hal-05217536**

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Submitted on 21 Aug 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Interplay between ultrasonic vocalisation characteristics and behavioural contexts in female mice

Elodie Ey¹, Fabrice de Chaumont², Benoît Forget², Yann Hérault¹, Thomas Bourgeron²

¹ CNRS UMR 7104, IGBMC, Illkirch; Université de Strasbourg; ² GHFC, CNRS UMR 3571, Institut Pasteur, Paris, Sorbonne Paris Université, Paris

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## 1. Introduction

The diversity of social communication systems across taxa underlines the need of distinctive investigations in each of them to understand their evolutionary relationships. Some rodent species use ultrasonic vocalisations (USVs) to communicate within conspecifics. In particular, the **diversity of the types of USVs** emitted by mice is intriguing. Scientists tried to capture it by classifying call types according to their acoustic features in exclusive call types (up to 10 or 12 types; ex: Scattoni et al. 2008 *PLoS One*). These types of USVs present acoustic characteristics that appeared distinctive to mice (Lahvis et al. 2011 *Genes Brain Behav*). However, to what extent these different call types refer to specific behaviours that co-occur remains unclear. The present project aims at deciphering whether **acoustic features of some USVs could predict the behaviour** that is co-occurring. For that purpose, we abandoned the classical constrained contexts of short social encounters in novel environments after social isolation and focused on the **spontaneous behaviours of pairs of familiar female mice** living undisturbed for two days. Preliminary analyses focused on three acoustic features and one specific sequence of USVs.

## 3. High noise

A

<figure><img src="image_5.png" /><figcaption>Fig. 2: A & B. Spectrograms of examples of USVs including a high noisy feature in two different pairs.</figcaption></figure>

B

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High and noisy USVs (Fig. 2A) were rare (Fig. 1C) and the shortest with the highest frequency characteristics (Fig. 1D). Almost all (242 over 245) occurred when one animal was jumping against the wall (Fig. 2B). They are likely to stem from the **physical noise** of paws on the Plexiglas walls.

## 4. Long simple

A

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B

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C

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USVs with a long simple feature (Fig. 3A) were used in **social sniffing of mid intensity** (mostly in ano-genital sniffing; Fig. 3B). Surprisingly, the relative ratios of USVs emitted in nose-nose, nose-head and nose-anogenital contacts were very similar (Fig. 3C).

<caption>Fig. 3: A. Spectrograms of examples of USVs including a long and simple feature. B. Proportion of USVs with a long simple feature occurring with each manually labelled behaviour. C. Proportion of USVs labelled with each behaviour or combination of behaviours occurring with each of the three acoustic features of interest.</caption>

## 2. Methods

C57BL/6J adult female mice equipped with subcutaneous RFID tags at 6 weeks of age were housed in pairs. We recorded them at 8 weeks of age continuously for 2 days and 2 nights using the Live Mouse Tracker system (LMT, de Chaumont et al. 2019 *Nat Biomed Eng*). This tracking was synchronised with USV recordings (Avisoft SAS Lab Pro recorder; sampling 300 kHz; 16-bits; 1s pre-trigger, 1s hold time; duration >0.005 s; trigger event: 2% energy in 25–125 kHz with entropy <50%). USVs were automatically detected with LMT USV toolbox (de Chaumont et al. 2021 *Front Behav Neurosci*). In parallel, the whole setting was video-recorded using 4 webcams and one thermal camera. All these recording systems were synchronised and data were gathered in the LMT database for each experiment. Data exploration was conducted using python pipelines. Behaviours were automatically extracted using LMT. Data were also manually labelled using a newly developed user-friendly application: labelling of acoustic characteristics on the spectrograms (selection of USVs and validation by printing a map containing all selected spectrograms; Fig. 1A), and co-occurring behaviours of selected USVs (Fig. 1B) based on a user-defined ethogram.

## 5. Low-frequency component

A

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B

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C

<figure><img src="image_12.png" /><figcaption>Fig. 4: A. Spectrograms of examples of USVs including a low frequency component around 40 kHz. B. Proportion of USVs with a low component occurring with each manually labelled behaviour. C. Proportion of USVs labelled with specific behaviours or combination of behaviours occurring with each of the three acoustic features of interest.</figcaption></figure>

Low-frequency component USVs displayed the lowest minimum frequency, given their component around 40 kHz (Fig. 4A). USVs with this characteristic were rarely emitted in nose-nose contact, but more regularly associated with **one animal being behind** the other one. **Mounting behaviour** was frequently associated with USVs having a low-frequency component feature (Fig. 4B). The profile of proportion of USVs associated with follow+nose on the back and nose on the back+paw on the back were similar to the mounting profile (Fig. 4C). These two behavioural combinations might represent early stages of mounting.

## 7. Conclusions

The implementation of USV monitoring allows to **refine the behavioural description** of freely behaving mice and could lead to better phenotyping of mouse models of neuropsychiatric disorders. We expect this refinement to help understand the large inter-individual variability observed. USVs can also **help to measure hierarchy**, even in female mice. Indeed, some behaviours frequently associated with males (mounting, urine deposits) were also observed in females. The urine marking function should be further explored by investigating whether the other mouse also visits urine deposits for which no USV sequences were emitted and whether it correlates with asymmetry in their social behaviours, to clarify the communicative function of these signals. One of the next steps will be to **identify the emitter of USVs**, using a sound camera that will be synchronised with LMT, and to **consider sequences of similar USVs** (as we did for urination sequences).

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B

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C

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D

<figure><img src="image_16.png" /><figcaption>Fig. 1: A. Manual labelling of USVs. B. Manual annotation of co-occurring behaviours for each USV; the 4 webcam, the LMT tracking and the thermal view are synchronised with USV spectrograms. C. Proportion of USVs containing one of the 3 feature. D. Selected acoustic variables of USVs with the 3 features (Linear Mixed Model; p<0.001 for feature, pair and their interaction).</figcaption></figure>

## 6. Urination sequences

A

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B

<figure><img src="image_18.png" /><figcaption>Fig. 5: A. Spectrograms of sequences of USVs emitted during urination (mouse F23, in the F23-F24 pair). B. Corresponding timeline depicting urination event, proximity (10 cm) around the urination spot as well as long chase and contact events for 2 min before and 2 min after the urination event.</figcaption></figure>

We identified specific sequences of USVs, characterised by the repetition of similar relatively **simple USVs with a regular temporal organisation** (Fig. 5A). These sequences occurred when **one animal was in a corner depositing a large quantity of urine** (confirmed on thermal view). In most pairs, both females of the pair emitted these sequences over days or nights (F21-F22: 0-4; F23-F24: 11-5; F25-F26: 10-9; F27-F28: 0-1; F29-F30: 2-9; F37-F38: 7-6). Over the 50 sequences recorded when both individuals were active, 40 of these (80%) were followed by a visit of the other female to the corner of the urine deposit within 2 min (Fig. 5B).

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Institut Pasteur

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Université
de Strasbourg

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Paris Cité

Université
Paris Cité