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◆ Scientific Reports2026-09-02· Analytics

Mouse bio-behavioral phenotyping using a digital homecage framework for long-timescale, high-resolution, and multi-factor data collection and analytics

Nicolette Ognjanovski, Anjesh Ghimire, Pho J. Hale, David Kim, Ivo H. Cerda, Ethan Goldiez, Simeone Marino, Antwan Green, Paul J. Fitzgerald, Priya Vijayakumar, Deniz Kirca, M.K.H. Tong, Noah Muscat, Kennedy Knopf, M. B. Cook, Ming‐Yi Tang, Chen Yang, Lézio Soares Bueno-Júnior, Ridge Weston, Tangyu Liu, Jeremiah P. Hartner, Ivo D. Dinov, Brendon O. Watson

原始摘要(英文原文)· Original abstract
Long-term monitoring of behavioral and physiological processes is essential for elucidating complex brain-based phenomena and disorders that develop over extended periods, such as chronic stress, circadian disruption, and metabolic syndromes. Though digital phenotyping is well-established in humans via smart devices, comparable solutions for rodent models remain limited. To address this gap, we present the Digital Homecage (DHC) system, an open-source platform that enables uninterrupted, long-timescale recording of over 20 behavioral metrics in single-housed mice. The DHC integrates video capture, operant task modules, and wheel-running data to achieve sub-second resolution in monitoring behaviors such as actigraphy, sleep, grooming, and food choice over periods spanning weeks. Initial data validate the system's capability to reveal circadian patterns across multiple spontaneous behaviors, consistently reflecting nocturnal activity. Designed for seamless integration with brain recording technologies, the DHC offers unique opportunities for longitudinal analyses of chronic conditions and neuropsychiatric syndromes. Its accessibility and modular design promote community-driven innovation, establishing the DHC as a transformative tool for advancing the study of complex traits in rodent models of brain function and behavior.
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Mouse bio-behavioral phenotyping using a digital homecage framework for long-timescale, high-resolution, and multi-factor data collection and analytics — 科研速览 Science Skim