Automating ultrasonic vocalization analyses: the WAAVES program.

J Neurosci Methods

The University of Texas at Austin, Department of Psychology, Behavioral Neuroscience, 108 East Dean Keeton Avenue, Stop A8000, Austin, TX 78712-1043, USA.

Published: September 2013

Background: Human emotion is a crucial component of drug abuse and addiction. Ultrasonic vocalizations (USVs) elicited by rodents are a highly translational animal model of emotion in drug abuse studies. A major roadblock to comprehensive use of USV data is the overwhelming burden to attain accurate USV assessment in a timely manner. One of the most accurate methods of analyzing USVs, human auditory detection with simultaneous spectrogram inspection, requires USV sound files to be played back 4% normal speed.

New Method: WAAVES (WAV-file Automated Analysis of Vocalizations Environment Specific) is an automated USV assessment program utilizing MATLAB's Signal and Image Processing Toolboxes in conjunction with a series of customized filters to separate USV calls from background noise, and accurately tabulate and categorize USVs as flat or frequency-modulated (FM) calls. In the current report, WAAVES functionality is demonstrated by USV analyses of cocaine self-administration data collected over 10 daily sessions.

Results: WAAVES counts are significantly correlated with human auditory counts (r(48)=0.9925; p<0.001). Statistical analyses used WAAVES output to examine individual differences in USV responses to cocaine, cocaine-associated cues and relationships between USVs, cocaine intake and locomotor activity.

Comparison With Existing Method: WAAVES output is highly accurate and provides tabulated data in approximately 0.3% of the time required when using human auditory detection methods.

Conclusions: The development of a customized USV analysis program, such as WAAVES streamlines USV assessment and enhances the ability to utilize USVs as a tool to advance drug abuse research and ultimately develop effective treatments.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3931607PMC
http://dx.doi.org/10.1016/j.jneumeth.2013.06.006DOI Listing

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