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. Published by Elsevier B.V.
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. Published by Elsevier B.V.
Authors: Esther Y Maier; Allison M Ahrens; Sean T Ma; Timothy Schallert; Christine L Duvauchelle Journal: Behav Brain Res Date: 2010-05-12 Impact factor: 3.332
Authors: Allison M Ahrens; Sean T Ma; Esther Y Maier; Christine L Duvauchelle; Timothy Schallert Journal: Behav Brain Res Date: 2008-09-03 Impact factor: 3.332
Authors: Sean T Ma; Esther Y Maier; Allison M Ahrens; Timothy Schallert; Christine L Duvauchelle Journal: Behav Brain Res Date: 2010-04-09 Impact factor: 3.332
Authors: David J Barker; David H Root; Sisi Ma; Shaili Jha; Laura Megehee; Anthony P Pawlak; Mark O West Journal: Psychopharmacology (Berl) Date: 2010-06-23 Impact factor: 4.530
Authors: Jeffrey Burgdorf; Roger A Kroes; Joseph R Moskal; James G Pfaus; Stefan M Brudzynski; Jaak Panksepp Journal: J Comp Psychol Date: 2008-11 Impact factor: 2.231
Authors: James M Reno; Neha Thakore; Rueben Gonzales; Timothy Schallert; Richard L Bell; W Todd Maddox; Christine L Duvauchelle Journal: Alcohol Clin Exp Res Date: 2015-04-01 Impact factor: 3.455
Authors: Nitish Mittal; Neha Thakore; James M Reno; Richard L Bell; W Todd Maddox; Timothy Schallert; Christine L Duvauchelle Journal: Alcohol Date: 2017-09-14 Impact factor: 2.405
Authors: James M Reno; Neha Thakore; Lawrence K Cormack; Timothy Schallert; Richard L Bell; W Todd Maddox; Christine L Duvauchelle Journal: Alcohol Clin Exp Res Date: 2017-02-23 Impact factor: 3.455
Authors: Neha Thakore; James M Reno; Rueben A Gonzales; Timothy Schallert; Richard L Bell; W Todd Maddox; Christine L Duvauchelle Journal: Behav Brain Res Date: 2016-01-21 Impact factor: 3.332
Authors: Catherine B Ashley; Ryan D Snyder; James E Shepherd; Catalina Cervantes; Nitish Mittal; Sheila Fleming; Jaxon Bailey; Maisie D Nievera; Sharmin Islam Souleimanova; Bill Nyaoga; Lauren Lichtenfeld; Alicia R Chen; W Todd Maddox; Christine L Duvauchelle Journal: Brain Sci Date: 2021-06-29