Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats

1. Pre/Post-training Recordings

1. Begin ultrasonic monitoring with Avisoft Recorder using the configuration shown in Figure 2.
2. Ultrasonic vocalizations can be monitored either visually by watching the real-time spectrogram, or aurally with headphones plugged into the ultrasound recording interface.
3. Place a sexually receptive (in estrus) female rat into the home cage of the subject male rat. Signs of estrus include lordosis, ear wiggling, and rapid darting. Females in estrus elicit more vocalizations from males than non-sexually receptive females.
4. After the male has shown interest in the female (e.g., sniffing, chasing, and/or mounting), remove the female and record the male’s vocalizations for 1-2 minutes.
5. If the male does not vocalize immediately, briefly return the female rat to the male’s home cage to stimulate vocalization.

2. Vocal Training

1. Begin ultrasonic monitoring with Avisoft Recorder using the same configuration shown in Figure 2.
2. Place the male rat in his home cage under the microphone.
3. Put a female rat into the male’s home cage. As stated previously, the female rat should be in estrus to maximize the vocalization response from the male.
4. Once the male has shown interest in the female remove the female.
5. Immediately after the male produces the target vocalization, reward simultaneously with a pen click and a brief presentation of the water bottle or food treat. Initially, any 50-kHz frequency modulated (Figure 5B) call is rewarded. As training progresses only increasingly complex strings of calls, such as strings of 5 to 10 calls in rapid succession, are reinforced.
6. Continue the training session until you have given 30 reinforcements. This typically takes between 5 – 10 minutes.

3. Preparing for Acoustic Analysis

1. Always archive the original sound files and make edits and measurements on a working copy, as the analysis will require filtering and erasing noise from each file.
2. Using the batch mode of Avisoft SASLab Pro, high pass filter all the sound files to be analyzed at 25 kHz to remove unwanted noise below the ultrasonic vocalizations.
3. In order to use the automatic detection feature of SASLab Pro, the noise threshold must first be determined. To do this, open a sound file and create a spectrogram using the settings shown in Figure 3.
4. The Automatic Parameter Measurements display should be setup according to the settings shown in Figure 4.
5. Find an area of the spectrogram where there are no calls or noises; this is the background noise.
6. Using the “automatic (single threshold)” option, set the threshold in the Element separation section of the Automatic Parameter Measurements setup dialog box to just above the background noise by adjusting the black line in the power spectrum window above the spectrogram. In Figure 4, the threshold has been set to -55 dB.
7. At the bottom of the dialog box, set the “Reject if peak amplitude is less than” value to 1 dB below the threshold found in the previous step. These two values should now be used for all sound files analyzed, assuming all recording settings were the same.

4. Vocalization Identification

1. To label the vocalizations using the automatic threshold set above, first create a spectrogram with the above settings.
2. Manually erase any noise that meets the following criteria: 1) has been wrongly identified as a vocalization, 2) is affecting the discrimination of the beginning and/or end of the vocalization, and/or 3) has been wrongly identified as the highest or lowest frequency in the vocalization (indicated by the upper and lower red lines, respectively). Alternatively, the start/stop times of the vocalization labels can be adjusted manually after creating permanent labels in step 4.5 below. Noise affecting the high and low frequency measurements, however, must be manually erased.
3. In some cases a vocalization will not be able to be separated from the surrounding noises, such as the sound of bedding during locomotion. In these cases, the call cannot be accurately analyzed and should be erased.
4. Once all vocalizations have been identified, permanently erase the deleted sections of the spectrogram from the sound file by selecting “Remove erased spectrogram sections from waveform…” from the Tools drop-down menu. If this command is not executed before the spectrogram is closed, all erasing work will be lost.
5. Create permanent labels in the sound file by clicking the ‘edit’ button in the Element Separation section of the Automatic Parameter Measurements setup window.
6. Visually and aurally review each labeled vocalization to determine its category: simple, frequency modulated (FM), or harmonic. Assign the appropriate label using the pre-defined text-modules. Examples of each vocalization category are shown in Figure 5.
7. Close the spectrogram, save the sound file, and continue with the next sound file to be analyzed.

5. Acoustic Measurements

1. After vocalizations in all the sound files have been identified, use the batch processing tool (Tools: Batch Processing) to automatically measure the acoustic parameters of all the files.
2. In the Batch processing dialogue box, select “Automatic Parameter Measurements” from the drop-down menu.
3. Select the “process all files in the selected folder” checkbox then click the “folder” button and select the folder where the sound files are saved.
4. Click “Start” and each file will be automatically analyzed according to the settings last used in the Automatic Parameter Measurement setup dialogue box.
5. The measurements will be saved in a text file that can be defined using the DDE Parameters/Log File Setting command. This text file can then be imported into a program for statistical analysis.

6. Representative Results:

Ultrasonic vocalizations of rats are affected by physiological changes associated with aging and 6-OHDA Parkinson disease models. ¹³ Typically, we see a reduction in quality of the ultrasonic vocalizations characterized by a reduction of the following quantitative acoustic parameters: bandwidth, peak frequency, duration, and intensity. Figure 6 shows the frequency modulated vocalizations of a rat in the PD model at three different time points: baseline, after induction of PD, and after vocalization training. In the PD condition, the call shows a reduction in bandwidth, duration, and intensity. Additionally, the frequency modulation has become irregular.

In addition to degradation of specific acoustic parameters, we also observe less overall complexity in the types of vocalizations produced in the aged and parkinsonism rats. For example, after induction of PD a rat produces a greater number of simple calls and fewer frequency modulated calls. Following vocalization training, many acoustic parameters, such as duration and intensity, approach baseline levels and the number of complex calls increases (Figure 6). Aged rats manifest similar degradation of acoustic parameters in their ultrasonic vocalizations. We are currently investigating the effects of vocal training in aged rats.

Figure 1. Number of vocalizations per session (target met by all rats each week) and vocalization rate (mean and standard error shown) both increase over a 6-week training period in a group of both young (9 mo) and old (32 mo) Fischer 344/Brown Norway F1 rats.

Figure 2. Representative vocalizations of each of the three categories of 50 kHz vocalizations; (A) simple, (B) frequency-modulated, and (C) harmonic.

Figure 3. Representative frequency-modulated vocalizations from a single rat at (A) baseline, (B) post-PD induction, and (C) after vocalization training.