Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.
Program in Neuroscience, Harvard University, Cambridge, MA 02138, USA.
Neuron. 2021 Feb 3;109(3):420-437.e8. doi: 10.1016/j.neuron.2020.11.016. Epub 2020 Dec 18.
In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat's head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking. We demonstrate CAPTURE's ability to comprehensively profile the kinematics and sequential organization of natural rodent behavior, its variation across individuals, and its perturbation by drugs and disease, including identifying perseverative grooming states in a rat model of fragile X syndrome. CAPTURE significantly expands the range of behaviors and contexts that can be quantitatively investigated, opening the door to a new understanding of natural behavior and its neural basis.
在哺乳动物动物模型中，高分辨率运动跟踪仅限于在受限环境中的短暂会话，限制了我们探究自然行为及其神经基础的能力。为了解决这个问题，我们开发了 CAPTURE（使用反射器嵌入进行连续附肢和姿势跟踪），这是一种行为监测系统，它结合了运动捕捉和深度学习，可连续跟踪大鼠头部、躯干和四肢的 3D 运动学，在自由行为动物中可长达一周的时间尺度。与现有的行为跟踪卷积网络方法相比，CAPTURE 在精度和稳健性方面提高了 10 到 100 倍。我们证明了 CAPTURE 能够全面分析自然啮齿动物行为的运动学和顺序组织、个体之间的差异以及药物和疾病的干扰，包括在脆性 X 综合征大鼠模型中识别持续梳理状态。CAPTURE 大大扩展了可以进行定量研究的行为和上下文的范围，为深入了解自然行为及其神经基础打开了大门。
