Automated analysis of mouse rearing using deep learning
Rodent rearing behavior is frequently assessed as an indicator of anxiety and exploratory tendencies. This study developed a convolutional recurrent neural network (CRNN) model to detect mouse rearing using overhead videos. Behavioral data from C57BL/6 mice under light and dark conditions were manually labeled frame-by-frame and used to train the CRNN model. Model performance was evaluated on separate test videos, achieving a sensitivity of 89.2 %, comparable to human observation. The model reliably detected increased rearing following caffeine administration and distinguished differences between day and night activity patterns.
Authors
- Takahisa Murata (ORCID: https://orcid.org/0000-0002-3328-0796)
- Naoaki Sakamoto (ORCID: https://orcid.org/0000-0003-0546-4018)
- Masahiro Fukuda (ORCID: https://orcid.org/0000-0003-0172-8661)
- Koji Kobayashi (ORCID: https://orcid.org/0000-0001-7223-607X)
- Yusuke Miyazaki (ORCID: https://orcid.org/0009-0006-8032-0136)
- Keisuke Omori (ORCID: https://orcid.org/0000-0003-0159-9994)
Institutions
- Tokyo University of Agriculture (JP)
- The University of Tokyo (JP)
Publication Details
- Journal
- Journal of Pharmacological Sciences
- Published
- 2025-06-13
- DOI
- https://doi.org/10.1016/j.jphs.2025.06.002
- Primary Topic
- Zebrafish Biomedical Research Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science
- Japan Science and Technology Agency
- Adaptable and Seamless Technology Transfer Program through Target-Driven R and D