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Updated: Sep 2, 2025

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

Automated scratching detection system for black mouse using deep learning.

Naoaki Sakamoto¹, Taiga Haraguchi¹, Koji Kobayashi¹

¹Department of Animal Radiology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan.

Frontiers in Physiology

|August 8, 2022

Summary

This summary is machine-generated.

Researchers developed an automated method to detect scratching behavior in black mice. This convolutional recurrent neural network (CRNN) model significantly improves prediction accuracy for this common research model of itching.

Keywords:

convolutional neural network itching neural network pruritus scratching behavior

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Area of Science:

Neuroscience
Animal Behavior
Computational Biology

Background:

Scratching behavior evaluation is crucial for understanding itching mechanisms and developing anti-itch medications.
Traditional human observation methods for quantifying scratching are labor-intensive and have low throughput.
Previous automated scratching detection using a convolutional recurrent neural network (CRNN) was effective in white mice but not black mice.

Purpose of the Study:

To develop and validate an improved CRNN model for accurate detection of scratching behavior in black mice (C57BL/6).
To enhance the prediction accuracy of automated scratching detection across different mouse strains.

Main Methods:

Scratching behavior was induced in black mice via serotonin administration and recorded via video.
Videos were manually annotated frame-by-frame to label scratching instances.
A CRNN model was trained on annotated data from black mice, incorporating posterior filters to refine predictions.

Main Results:

The newly trained CRNN model demonstrated high accuracy in detecting scratching behavior in black mice.
Achieved a sensitivity of 98.1% and a positive predictive rate of 94.0% for scratching detection.
The model's performance indicates successful adaptation for the black mouse strain.

Conclusions:

The developed CRNN model with posterior filters effectively predicts scratching behavior in black mice.
This refined automated workflow enhances the reliability and efficiency of scratching behavior analysis.
The study highlights the potential for strain-independent application of this automated detection method in preclinical research.