Establishing a reliable gait evaluation method for rodent studies.
Establishing a reliable gait evaluation method for rodent studies.
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DOI：
10.1016/j.jneumeth.2017.03.017
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发表时间：
2017-05-01
影响因子：
3
通讯作者：
Jia X
中科院分区：
文献类型：
--
作者：
Chen H;Du J;Zhang Y;Barnes K;Jia X
猫步是临床前研究中最常用的评估步态恢复的工具之一，然而，目前对于猫步采集的众多步态参数中哪些能够可靠地模拟恢复还没有达成共识。对结果有相互矛盾的解释，以及许多常见但很少报道的问题，如高跟鞋行走和依从性差。我们开发了一种系统的手动分类方法，克服了常见的问题，如脚后跟行走和依从性差。通过纠正自动化错误和删除不一致的步态周期，我们分离出了更可靠的分析记录片段。有髓轴突的定量组织形态计量学分析也评估了恢复结果。虽然40%-60%的跑动在没有人工干预的情况下被错误地分类，但我们用我们的新方法纠正了所有的错误，并表明站立时间、占空比和摆动速度能够跟踪随着时间的推移和试验组之间的显著差异(所有P<；0.05)。印刷面积和强度参数的可用性需要进一步验证，而不仅仅是T型台的能力。目前还没有解决脚后跟行走和依从性差等问题的策略，因此没有一组标准的参数可以供研究人员用来报告他们的发现。手动分类是生成可靠的T台步态数据的关键步骤，站立时间、占空比和摆动速度是评估步态恢复的合适参数。使用静态参数(如打印面积和强度)时应格外谨慎。
CatWalk is one of the most popular tools for evaluating gait recovery in preclinical research, however, there is currently no consensus on which of the many gait parameters captured by CatWalk can reliably model recovery. There are conflicting interpretations of results, along with many common but seldom reported problems such as heel walking and poor compliance. We developed a systematic manual classification method that overcomes common problems such as heel walking and poor compliance. By correcting automation errors and removing inconsistent gait cycles, we isolated stretches of recordings that are more reliable for analysis. Recovery outcome was also assessed by quantitative histomorphometric analysis of myelinated axons. While 40–60% of runs were erroneously classified without manual intervention, we corrected all errors with our new method, and showed that Stand Time, Duty Cycle, and Swing Speed are able to track significant differences over time and between experimental groups (all p<0.05). The usability of print area and intensity parameters requires further validation beyond the capabilities of CatWalk. There is currently no strategy that addresses problems such as heel walking and poor compliance, and therefore no standard set of parameters that researchers can rely on to report their findings. Manual classification is a crucial step to generate reliable CatWalk data, and Stand Time, Duty Cycle, and Swing Speed are suitable parameters for evaluating gait recovery. Static parameters such as print area and intensity should be used with extreme caution.