Automated acoustic detection of mouse scratching

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

Measurement of Itch

Itch is a major symptom of skin disease. Yet, until the last thirty years the investigation of itch has been ignored by researchers. One important reason has been the lack of reliable and sensitive measurement. We can only indirectly measure different aspects of itch perception which is performed on several levels of the nervous system. Physiological itch participates in the defence of the organism from harmful agents and involves multiple steps including the peripheral receptor, the afferent nerve transmitting the impulse to the spinal cord, the signal processing in the dorsal horn and finally transmission to higher cerebral centres in the thalamus and the cortex.1 The perceived itch induces scratching with the purpose of removing harmful agents from the skin. Scratching has for a long time been recognised as the most objective and reliable measurement of itch.2 This chapter provides a historical and critical review of the objective and subjective methods of itch measurement both in experimental and in clinical setting