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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5497976 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179662 | PLOS |
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