Perry Logan J, Ratcliff Gavin E, Mayo Arthur, Perez Blanca E, Rays Wahba Larissa, Nikhil K L, Lenzen William C, Li Yangyuan, Mar Jordan, Farhy-Tselnicker Isabella, Li Wanhe, Jones Jeff R
Department of Biology, Texas A&M University, College Station, TX 77843, USA.
Institute for Neuroscience, Texas A&M University, College Station, TX 77843, USA.
Cell Rep Methods. 2025 May 19;5(5):101050. doi: 10.1016/j.crmeth.2025.101050.
Long-term analysis of animal behavior has been limited by reliance on real-time sensors or manual scoring. Existing machine learning tools can automate analysis but often fail under variable conditions or ignore temporal dynamics. We developed a scalable pipeline for continuous, real-time acquisition and classification of behavior across multiple animals and conditions. At its core is a self-supervised vision model paired with a lightweight classifier that enables robust performance with minimal manual labeling. Our system achieves expert-level performance and can operate indefinitely across diverse recording environments. As a proof-of-concept, we recorded 97 mice over 2 weeks to test whether sex hormones influence circadian behaviors. We discovered sex- and estrogen-dependent rhythms in behaviors such as digging and nesting. We introduce the Circadian Behavioral Analysis Suite (CBAS), a modular toolkit that supports high-throughput, long-timescale behavioral phenotyping, allowing for the temporal analysis of behaviors that were previously difficult or impossible to observe.
对动物行为的长期分析一直受到对实时传感器或人工评分依赖的限制。现有的机器学习工具可以实现分析自动化，但在可变条件下往往会失败，或者忽略时间动态。我们开发了一种可扩展的流程，用于在多种动物和条件下对行为进行连续、实时的采集和分类。其核心是一个自我监督的视觉模型，与一个轻量级分类器相结合，能够以最少的人工标注实现强大的性能。我们的系统达到了专家级性能，并且可以在各种不同的记录环境中无限期运行。作为概念验证，我们在两周内记录了97只小鼠，以测试性激素是否会影响昼夜节律行为。我们发现了诸如挖掘和筑巢等行为中存在性别和雌激素依赖性节律。我们推出了昼夜行为分析套件（CBAS），这是一个模块化工具包，支持高通量、长时间尺度的行为表型分析，能够对以前难以或无法观察到的行为进行时间分析。
