Institute of Clinical Neuroimmunology, University Hospital, LMU Munich, 81377, Munich, Germany.
Biomedical Center Munich (BMC), Faculty of Medicine, LMU Munich, 82152, Planegg-Martinsried, Germany.
Commun Biol. 2022 Feb 15;5(1):131. doi: 10.1038/s42003-022-03077-6.
In neuroscience research, the refined analysis of rodent locomotion is complex and cumbersome, and access to the technique is limited because of the necessity for expensive equipment. In this study, we implemented a new deep learning-based open-source toolbox for Automated Limb Motion Analysis (ALMA) that requires only basic behavioral equipment and an inexpensive camera. The ALMA toolbox enables the consistent and comprehensive analyses of locomotor kinematics and paw placement and can be applied to neurological conditions affecting the brain and spinal cord. We demonstrated that the ALMA toolbox can (1) robustly track the evolution of locomotor deficits after spinal cord injury, (2) sensitively detect locomotor abnormalities after traumatic brain injury, and (3) correctly predict disease onset in a multiple sclerosis model. We, therefore, established a broadly applicable automated and standardized approach that requires minimal financial and time commitments to facilitate the comprehensive analysis of locomotion in rodent disease models.
在神经科学研究中，对啮齿动物运动的精细分析既复杂又繁琐，而且由于需要昂贵的设备，因此该技术的获取受到限制。在这项研究中，我们实现了一个新的基于深度学习的开源工具包，用于自动肢体运动分析（ALMA），该工具包仅需要基本的行为设备和廉价的相机。ALMA 工具包能够对运动学和爪子放置进行一致且全面的分析，并且可应用于影响大脑和脊髓的神经疾病。我们证明，ALMA 工具包可以（1）稳健地跟踪脊髓损伤后运动缺陷的演变，（2）敏感地检测创伤性脑损伤后的运动异常，以及（3）正确预测多发性硬化症模型中的疾病发作。因此，我们建立了一种广泛适用的自动化和标准化方法，该方法只需最低的财务和时间承诺，即可促进啮齿动物疾病模型中运动的全面分析。
