Automated quantitative gait analysis in animal models of movement disorders

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Abstract

Background: Accurate and reproducible behavioral tests in animal models are of major importance in the development and evaluation of new therapies for central nervous system disease. In this study we investigated for the first time gait parameters of rat models for Parkinson's disease (PD), Huntington's disease (HD) and stroke using the Catwalk method, a novel automated gait analysis test. Static and dynamic gait parameters were measured in all animal models, and these data were compared to readouts of established behavioral tests, such as the cylinder test in the PD and stroke rats and the rotarod tests for the HD group.Results: Hemiparkinsonian rats were generated by unilateral injection of the neurotoxin 6-hydroxydopamine in the striatum or in the medial forebrain bundle. For Huntington's disease, a transgenic rat model expressing a truncated huntingtin fragment with multiple CAG repeats was used. Thirdly, a stroke model was generated by a photothrombotic induced infarct in the right sensorimotor cortex. We found that multiple gait parameters were significantly altered in all three disease models compared to their respective controls. Behavioural deficits could be efficiently measured using the cylinder test in the PD and stroke animals, and in the case of the PD model, the deficits in gait essentially confirmed results obtained by the cylinder test. However, in the HD model and the stroke model the Catwalk analysis proved more sensitive than the rotarod test and also added new and more detailed information on specific gait parameters.Conclusion: The automated quantitative gait analysis test may be a useful tool to study both motor impairment and recovery associated with various neurological motor disorders. © 2010 Vandeputte et al; licensee BioMed Central Ltd.

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Vandeputte, C., Taymans, J. M., Casteels, C., Coun, F., Ni, Y., Van Laere, K., & Baekelandt, V. (2010). Automated quantitative gait analysis in animal models of movement disorders. BMC Neuroscience, 11. https://doi.org/10.1186/1471-2202-11-92

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