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Machine-Learning Based Automatic and Real-time Detection of Mouse Scratching Behaviors

Title
Machine-Learning Based Automatic and Real-time Detection of Mouse Scratching Behaviors
Authors
Park, IngyuLee, KyeonghoBishayee, KausikJeon, Hong JinLee, HyosangLee, Unjoo
DGIST Authors
Lee, Hyosang
Issue Date
2019-02
Citation
Experimental Neurobiology, 28(1), 54-61
Type
Article
Article Type
Article
Author Keyword
Machine learning; Decision tree; Mouse; Scratching; Pruritus; Itch
Keyword
CENTRAL MECHANISMS; QUANTIFICATION; SYSTEM; MICE; MODEL
ISSN
1226-2560
Abstract
Scratching is a main behavioral response accompanied by acute and chronic itch conditions, and has been quantified as an objective correlate to assess itch in studies using laboratory animals. Scratching has been counted mostly by human annotators, which is a time-consuming and laborious process. It has been attempted to develop automated scoring methods using various strategies, but they often require specialized equipment, costly software, or implantation of device which may disturb animal behaviors. To complement limitations of those methods, we have adapted machine learning-based strategy to develop a novel automated and real-time method detecting mouse scratching from experimental movies captured using monochrome cameras such as a webcam. Scratching is identified by characteristic changes in pixels, body position, and body size by frame as well as the size of body. To build a training model, a novel two-step J48 decision tree-inducing algorithm along with a C4.5 post-pruning algorithm was applied to three 30-min video recordings in which a mouse exhibits scratching following an intradermal injection of a pruritogen, and the resultant frames were then used for the next round of training. The trained method exhibited, on average, a sensitivity and specificity of 95.19% and 92.96%, respectively, in a performance test with five new recordings. This result suggests that it can be used as a non-invasive, automated and objective tool to measure mouse scratching from video recordings captured in general experimental settings, permitting rapid and accurate analysis of scratching for preclinical studies and high throughput drug screening.
URI
http://hdl.handle.net/20.500.11750/9693
DOI
10.5607/en.2019.28.1.54
Publisher
한국뇌신경과학회
Related Researcher
  • Author Lee, Hyosang Laboratory of Affective Neuroscience
  • Research Interests Neuronal circuit mechanisms underlying emotional behaviors; 정서적 행동을 매개하는 신경회로 연구
Files:
There are no files associated with this item.
Collection:
Department of Brain and Cognitive SciencesLaboratory of Affective Neuroscience1. Journal Articles


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