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Automated acoustic detection of mouse scratching

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Listed:
  • Peter Elliott
  • Max G’Sell
  • Lindsey M Snyder
  • Sarah E Ross
  • Valérie Ventura

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.

Suggested Citation

  • Peter Elliott & Max G’Sell & Lindsey M Snyder & Sarah E Ross & Valérie Ventura, 2017. "Automated acoustic detection of mouse scratching," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-16, July.
    • Handle: RePEc:plo:pone00:0179662
    DOI: 10.1371/journal.pone.0179662
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    References listed on IDEAS

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    1. Yan-Gang Sun & Zhou-Feng Chen, 2007. "A gastrin-releasing peptide receptor mediates the itch sensation in the spinal cord," Nature, Nature, vol. 448(7154), pages 700-703, August.
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