Becker April M, Meyers Eric, Sloan Andrew, Rennaker Robert, Kilgard Michael, Goldberg Mark P
University of Texas Southwestern Medical Center, Department of Neurology and Neurotherapeutics, Dallas, TX, United States; University of Texas Southwestern Medical Center, Neuroscience PhD Program, Dallas, TX, United States.
University of Texas at Dallas, Erik Jonsson School of Engineering and Computer Science, Dallas, TX, United States.
J Neurosci Methods. 2016 Jan 30;258:16-23. doi: 10.1016/j.jneumeth.2015.10.004. Epub 2015 Oct 17.
Behavioral models relevant to stroke research seek to capture important aspects of motor skills typically impaired in human patients, such as coordination of distal musculature. Such models may focus on mice since many genetic tools are available for use only in that species and since the training and behavioral demands of mice can differ from rats even for superficially similar behavioral readouts. However, current mouse assays are time consuming to train and score, especially in a manner producing continuous quantification. An automated assay of mouse forelimb function may provide advantages for quantification and speed, and may be useful for many applications including stroke research.
We present an automated assay of distal forelimb function. In this task, mice reach forward, grip and pull an isometric handle with a prescribed force. The apparatus partially automates the training process so that mice can be trained quickly and simultaneously.
Using this apparatus, it is possible to measure long-lasting impairment in success rate, force pulled, latency to pull, and latency to success up to 22 weeks following photothrombotic cortical strokes in mice.
COMPARISON WITH EXISTING METHOD(S): This assessment measures forelimb function as do pellet reach tasks, but it utilizes a different motion and provides automatic measures that can ease and augment the research process.
This high-throughput behavioral assay can detect long-lasting motor impairments, eliminates the need for subjective scoring, and produces a rich, continuous data set from which many aspects of the reach and grasp motion can be automatically extracted.
与中风研究相关的行为模型旨在捕捉人类患者中通常受损的运动技能的重要方面,例如远端肌肉组织的协调。此类模型可能侧重于小鼠,因为许多基因工具仅可用于该物种,而且即使对于表面上相似的行为读数,小鼠的训练和行为要求也可能与大鼠不同。然而,当前的小鼠检测方法在训练和评分时耗时较长,尤其是以产生连续定量的方式。小鼠前肢功能的自动化检测可能在定量和速度方面具有优势,并且可能对包括中风研究在内的许多应用有用。
我们提出了一种远端前肢功能的自动化检测方法。在此任务中,小鼠向前伸展、抓握并以规定的力拉动一个等距手柄。该装置部分自动化了训练过程,以便可以快速且同时地训练小鼠。
使用该装置,可以测量小鼠光血栓性皮质中风后长达22周的成功率、拉力、拉动潜伏期和成功潜伏期的长期损伤。
该评估与颗粒到达任务一样测量前肢功能,但它采用了不同的动作并提供了可以简化和增强研究过程的自动测量方法。
这种高通量行为检测方法可以检测长期的运动损伤,无需主观评分,并产生丰富的连续数据集,从中可以自动提取伸展和抓握动作的许多方面。