How can I test ultrasonic emissions from mice? - briefly
Use a calibrated ultrasonic microphone and recording system inside a sound‑attenuated chamber, then process the data with spectrogram software to identify frequencies above 20 kHz. Acclimate the animals and suppress ambient noise to obtain reliable measurements.
How can I test ultrasonic emissions from mice? - in detail
Assessing ultrasonic vocalizations produced by laboratory rodents requires specialized hardware, calibrated procedures, and rigorous data handling.
First, select an appropriate microphone capable of detecting frequencies up to 100 kHz. Condenser or piezoelectric transducers designed for bioacoustic research are standard; they should be paired with a low‑noise preamplifier and a high‑sample‑rate analog‑to‑digital converter (minimum 250 kS/s, 16‑bit resolution). Mount the sensor at least 10 cm from the animal to avoid acoustic shadowing while maintaining a clear line of sight.
Second, prepare the testing environment. Use an anechoic or heavily dampened chamber to suppress reflections. Control ambient temperature (22 ± 1 °C) and lighting, as these variables influence vocal output. Provide a sound‑proof enclosure for the recording equipment to prevent external interference.
Third, define the experimental stimulus. Typical protocols employ one of the following triggers:
- Social isolation – place the mouse in a clean cage without conspecifics for 5–10 min.
- Maternal separation – separate pups from the dam and record for 2–3 min.
- Acoustic startle – present a brief broadband noise burst (80–120 dB SPL) and capture the immediate ultrasonic response.
Record each session for a predetermined duration (commonly 3–5 min) and repeat the trial across multiple individuals (n ≥ 8) to obtain statistically meaningful data.
Fourth, process the raw waveforms. Apply a high‑pass filter at 20 kHz to remove low‑frequency noise, then convert the signal into a spectrogram using a short‑time Fourier transform (window length 256–512 samples, 75 % overlap). Extract quantitative parameters such as peak frequency, bandwidth, duration, inter‑call interval, and call rate. Software packages like Avisoft‑SASLab Pro, MATLAB with the BioSig toolbox, or open‑source tools such as DeepSqueak can automate these measurements.
Fifth, conduct statistical analysis. Verify normality of each metric (Shapiro‑Wilk test) and select appropriate tests (t‑test, ANOVA, or non‑parametric equivalents). Include effect sizes and confidence intervals to convey the magnitude of observed differences. When comparing across experimental groups, adjust for multiple comparisons using the Bonferroni or false‑discovery‑rate method.
Finally, document all hardware specifications, calibration procedures, and environmental conditions in the experimental log. Store raw audio files in lossless format (e.g., WAV) and maintain a backup on a secure server. Consistent reporting enables reproducibility and facilitates meta‑analyses across laboratories.