IEEE Trans Biomed Eng. 2018 May;65(5):1133-1139. doi: 10.1109/TBME.2017.2701204. Epub 2017 Aug 24.
Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored.
We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net).
Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation.
A machine learning method for automated analysis of data from the Noldus Catwalk system was established.
Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.
对动物疾病模型的步态分析可为体内化合物的综合影响提供有价值的见解，从而有助于临床前药物开发。本文旨在为 Noldus Catwalk 系统建立一种计算步态分析方法，该系统可自动捕获和存储足迹。
我们提出了一种基于机器学习的 Catwalk 系统方法，该方法包括步态分解、定义和提取有意义的特征、多变量步序对齐、特征选择以及不同分类器（梯度提升机、随机森林和弹性网络）的训练。
通过动物-wise 的留一法交叉验证，我们证明我们的方法可以可靠地区分帕金森病动物模型和多个对照组的运动模式。此外，我们表明我们可以预测不同脑损伤的时间点和类型，甚至可以预测干预应用的脑区。我们通过统计技术对分类器中的特征进行了深入分析，以进行模型解释。
建立了一种用于自动分析 Noldus Catwalk 系统数据的机器学习方法。
我们的工作表明，机器学习有能力以多变量的方式区分基于行走行为的药理学相关动物组。该方法的其他有趣方面包括从过去的实验中学习、随着更多数据的出现而不断改进以及对未来研究中的单个动物进行预测的能力。
