Machine Learning and Data Analytics Lab, Department of Computer Science, Faculty of Engineering, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), 91052 Erlangen-Nürnberg, Germany.
Department of Electronics Engineering, Satya Wacana Christian University, Salatiga 50711, Indonesia.
eNeuro. 2019 Nov 1;6(6). doi: 10.1523/ENEURO.0100-19.2019. Print 2019 Nov/Dec.
Gait analysis of transgenic mice and rats modeling human diseases often suffers from the condition that those models exhibit genotype-driven differences in body size, weight, and length. Thus, we hypothesized that scaling by the silhouette length improves the reliability of gait analysis allowing normalization for individual body size differences. Here, we computed video-derived silhouette length and area parameters from a standard markerless gait analysis system using image-processing techniques. By using length- and area-derived data along with body weight and age, we systematically scaled individual gait parameters. We compared these different scaling approaches and report here that normalization for silhouette length improves the validity and reliability of gait analysis in general. The application of this silhouette length scaling to transgenic Huntington disease mice and Parkinson´s disease rats identifies the remaining differences reflecting more reliable, body length-independent motor functional differences. Overall, this emphasizes the need for silhouette-length-based intra-assay scaling as an improved standard approach in rodent gait analysis.
这些模型在体型、体重和长度方面表现出由基因型驱动的差异。因此,我们假设通过轮廓长度进行缩放可以提高步态分析的可靠性,从而实现对个体体型差异的归一化。在这里,我们使用图像处理技术从标准的无标记步态分析系统中计算出视频衍生的轮廓长度和面积参数。通过使用长度和面积衍生的数据以及体重和年龄,我们系统地缩放了个体步态参数。我们比较了这些不同的缩放方法,并在此报告说,对轮廓长度进行归一化通常可以提高步态分析的有效性和可靠性。将这种轮廓长度缩放应用于转基因亨廷顿病小鼠和帕金森病大鼠,可以确定反映更可靠、与身体长度无关的运动功能差异的剩余差异。总体而言,这强调了在啮齿动物步态分析中需要基于轮廓长度的内标缩放作为改进的标准方法。