TY - JOUR
T1 - Establishing a reliable gait evaluation method for rodent studies
AU - Chen, Huanwen
AU - Du, Jian
AU - Zhang, Yifan
AU - Barnes, Kevin
AU - Jia, Xiaofeng
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - Background 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. New method 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. Results 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. Comparison with existing method(s) 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. Conclusion 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.
AB - Background 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. New method 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. Results 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. Comparison with existing method(s) 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. Conclusion 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.
KW - CatWalk
KW - Functional recovery
KW - Gait
KW - Heel walking
KW - Peripheral nerve
KW - Rat
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U2 - 10.1016/j.jneumeth.2017.03.017
DO - 10.1016/j.jneumeth.2017.03.017
M3 - Article
C2 - 28351803
AN - SCOPUS:85017522220
SN - 0165-0270
VL - 283
SP - 92
EP - 100
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
ER -