Huanwen Chen1, Jian Du1, Yifan Zhang2, Kevin Barnes1, Xiaofeng Jia3. 1. Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA. 2. Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. 3. Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Department of Orthopedics, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. Electronic address: xjia@som.umaryland.edu.
Abstract
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.
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.
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