Abstract
Itch is an aversive somatic sense that elicits the desire to scratch. In animal models of itch, scratching behavior is frequently used as a proxy for itch, and this behavior is typically assessed through visual quantification. However, manual scoring of videos has numerous limitations, underscoring the need for an automated approach. Here, we propose a novel automated method for acoustic detection of mouse scratching. Using this approach, we show that chloroquine-induced scratching behavior in C57BL/6 mice can be quantified with reasonable accuracy (85% sensitivity, 75% positive predictive value). This report is the first method to apply supervised learning techniques to automate acoustic scratch detection.
Highlights
Chronic itch is major clinical problem that remains ill-addressed
For recordings 1 to 5 we show the out-of-bag accuracy, and for recording 6 we show the accuracy of predicting scratch bouts with a classifier trained on recording 5, with and without neighborhood adjustment (Sec 3.2.2)
We proposed a method for acoustic detection of mouse scratching behavior
Summary
Chronic itch is major clinical problem that remains ill-addressed. There are hundreds of pathological conditions that result in chronic itch, such as atopic dermatitis, psoriasis, hepatic disease, renal disease, HIV and multiple sclerosis [1]. Rodent models of acute or chronic itch are frequently used for both basic and preclinical research studies aimed at understanding itch and developing better therapies [3,4,5,6]. In these models, the scratching behavior is used as a proxy for itch. Scratch bouts are a highly conserved itch behavior that is characterized by a series of individual hindpaw-mediated scratches (typically three to six) in rapid succession, followed by licking of the hindpaw This behavior has been quantified as an indirect measure of itch manually by recording behavior and scoring videos manually. These issues underscored the need to develop an automated method for the detection of scratching that is inexpensive and scalable
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
