Mossakowski Medical Research Centre Polish Academy of Sciences, Warsaw, Poland.
Warsaw Medical University, Warsaw, Poland.
Sci Rep. 2016 Dec 16;6:39331. doi: 10.1038/srep39331.
In rodents, detection and quantification of motor impairments is difficult. The traction test (inverted grid with mice clinging to the underside) currently has no objective rating system. We here developed and validated the semi-automatic MATLAB script TracMouse for unbiased detection of video-recorded movement patterns. High precision videos were analyzed by: (i) principal identification of anatomical paw details frame-by-frame by an experimentally blinded rater; (ii) automatic retrieval of proxies by TracMouse for individual paws. The basic states of Hold and Step were discriminated as duration and frequency, and these principle parameters were converted into static and dynamic endpoints and their discriminating power assessed in a dopaminergic lesion model. Relative to hind paws, forepaws performed ~4 times more steps, they were ~20% longer, and Hold duration was ~5 times shorter in normal C57Bl/6 mice. Thus, forepaw steps were classified as exploratory, hind paw movement as locomotive. Multiple novel features pertaining to paw sequence, step lengths and exploratory touches were accessible through TracMouse and revealed subtle Parkinsonian phenotypes. Novel proxies using TracMouse revealed previously unidentified features of movement and may aid the understanding of (i) brain circuits related to motor planning and execution, and (ii) phenotype detection in experimental models of movement disorders.
在啮齿动物中,运动障碍的检测和量化较为困难。目前,牵引测试(倒置网格,老鼠紧贴在网格底部)还没有客观的评分系统。我们在这里开发并验证了一个半自动化的 MATLAB 脚本 TracMouse,用于无偏检测视频记录的运动模式。通过以下两种方法对高精度视频进行分析:(i)由实验性盲法评估者逐帧对解剖学爪子细节进行主要识别;(ii)TracMouse 自动检索各爪子的代理变量。基本的 HOLD 和 STEP 状态通过时长和频率进行区分,然后将这些主要参数转化为静态和动态终点,并在多巴胺能损伤模型中评估其区分能力。与后脚相比,前脚的动作次数约多 4 倍,长度约长 20%,在正常 C57Bl/6 小鼠中 HOLD 时长约短 5 倍。因此,前脚的动作被归类为探索性的,而后脚的运动则是运动性的。通过 TracMouse 还可以获得与爪子序列、步长和探索性接触有关的多个新特征,这些新特征揭示了微妙的帕金森病表型。使用 TracMouse 的新代理变量揭示了运动障碍实验模型中运动的以前未识别的特征,可能有助于理解(i)与运动规划和执行相关的大脑回路,以及(ii)表型检测。