Long-term deep phenotyping of behavioral traits in mice using homecage monitoring
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1-s2.0-S0149763425004543-main.pdf (2.08 MB)
0000-0001-7124-510X Date
2025-11-02
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Elsevier
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Abstract
Neuropsychiatric disorders represent a significant global health challenge, and a deeper understanding of their underlying neurobiology is urgently needed. Rodent models are indispensable in this pursuit, yet traditional behavioral phenotyping often relies on short-duration tests in artificial settings, raising concerns about ecological validity, stress confounds, and limited translational relevance. This paper begins by reviewing these limitations and highlighting the growing shift towards long-term, continuous monitoring of animals within their semi-naturalistic environment. Building on this context and the critical need for robust methodologies, this paper outlines a comprehensive workflow for studying behavior of rodent cohorts in their home cages using AI-supported video tracking. Key steps include the design of experimental setups, video preprocessing, animal tracking, pose estimation, supervised or unsupervised interpretation of behavior, statistical analysis and visualization. The protocol encompasses an affordable and versatile pipeline for data acquisition and statistical interpretation. Ultimately, this work aims to provide researchers with both a critical overview of the field and a practical guide to implementing these powerful techniques, thereby fostering the generation of reproducible, high-quality data to enhance the depth and translational potential of neuroscience and behavioral research.
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Benjamin Jurek, Patrick Schlegel, Bartosz Zglinicki, London Aman, Veronika Kovarova, Sowmya Narayan, Rebecca Florea, Patrycja Ziuzia, Aleksandra Badura, Mathias V Schmidt, Michal Slezak, Long-term deep phenotyping of behavioral traits in mice using homecage monitoring, Neuroscience & Biobehavioral Reviews, Volume 180, 2026, 106453