Institute for Regenerative Medicine (IREM), University of Zurich, Campus Schlieren, Wagistrasse 12, 8952, Schlieren, Switzerland.
Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland.
BMC Biol. 2022 Oct 15;20(1):232. doi: 10.1186/s12915-022-01434-9.
Stroke research heavily relies on rodent behavior when assessing underlying disease mechanisms and treatment efficacy. Although functional motor recovery is considered the primary targeted outcome, tests in rodents are still poorly reproducible and often unsuitable for unraveling the complex behavior after injury.
Here, we provide a comprehensive 3D gait analysis of mice after focal cerebral ischemia based on the new deep learning-based software (DeepLabCut, DLC) that only requires basic behavioral equipment. We demonstrate a high precision 3D tracking of 10 body parts (including all relevant joints and reference landmarks) in several mouse strains. Building on this rigor motion tracking, a comprehensive post-analysis (with >100 parameters) unveils biologically relevant differences in locomotor profiles after a stroke over a time course of 3 weeks. We further refine the widely used ladder rung test using deep learning and compare its performance to human annotators. The generated DLC-assisted tests were then benchmarked to five widely used conventional behavioral set-ups (neurological scoring, rotarod, ladder rung walk, cylinder test, and single-pellet grasping) regarding sensitivity, accuracy, time use, and costs.
We conclude that deep learning-based motion tracking with comprehensive post-analysis provides accurate and sensitive data to describe the complex recovery of rodents following a stroke. The experimental set-up and analysis can also benefit a range of other neurological injuries that affect locomotion.
在评估潜在疾病机制和治疗效果时，中风研究严重依赖于啮齿动物行为。尽管功能性运动恢复被认为是主要的靶向结果，但啮齿动物的测试仍然难以重现，并且常常不适合揭示损伤后的复杂行为。
在这里，我们基于新的基于深度学习的软件（DeepLabCut，DLC）提供了一种全面的局灶性脑缺血后小鼠 3D 步态分析，该软件仅需要基本的行为设备。我们展示了几种小鼠品系中 10 个体部（包括所有相关关节和参考标记）的高精度 3D 跟踪。在此严格的运动跟踪的基础上，全面的后分析（具有> 100 个参数）揭示了中风后 3 周内运动特征的生物相关差异。我们进一步使用深度学习细化了广泛使用的阶梯梯级测试，并将其性能与人类注释者进行比较。生成的 DLC 辅助测试随后与五种广泛使用的常规行为设置（神经评分、转棒、阶梯梯级行走、圆筒测试和单颗粒抓取）在灵敏度、准确性、时间使用和成本方面进行了基准测试。
我们得出结论，基于深度学习的运动跟踪和全面的后分析可提供准确、敏感的数据来描述中风后啮齿动物的复杂恢复情况。该实验设置和分析还可以受益于其他一系列影响运动的神经损伤。
