Karakostas T, Hsiang S, Boger H, Middaugh L, Granholm AC. Three-dimensional rodent motion analysis and neurodegenerative disorders.
J Neurosci Methods 2013;
231:31-7. [PMID:
24129039 DOI:
10.1016/j.jneumeth.2013.09.009]
[Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2013] [Revised: 09/08/2013] [Accepted: 09/09/2013] [Indexed: 11/30/2022]
Abstract
BACKGROUND
Three-dimensional (3D) motion analysis is established in investigating, human pathological motion. In the field of gait, its use results in the objective identification of primary, and secondary causes of deviations, many current interventions are the result of pre- and post-testing, and it was shown recently that it can result in decreased number of surgeries and overall cost of care. Consequently, recent attempts have implemented 3D motion analysis using rat models to study, parkinsonism. However, to-date, a 3D user friendly analytical approach using rodent models to, identify etiologies of age-related motor impairment and accompanying pathologies has not been, implemented.
NEW METHOD
We have developed and presented all aspects of a 3D, three body-segment rodent model, to analyze motions of the lower, upper and head segments between rodents of parkinsonism-type and, normal aging during free walking. Our model does not require transformation matrices to describe the, position of each body-segment. Because body-segment positions are not considered to consist of three, rotations about the laboratory axes, the rotations are not sequence dependent.
RESULTS
Each body-segment demonstrated distinct 3D movement patterns. The parkinsonism-type, genotype walked slower with less range of motion, similarly to patients with parkinsonism.
COMPARISON WITH EXISTING METHODS
This is the first model considering the rodent's body as three, distinct segments. To the best of our knowledge, it is the first model to ever consider and report the 3D, head motion patterns.
CONCLUSIONS
This novel approach will allow unbiased analysis of spontaneous locomotion in mouse, models of parkinsonism or normal aging.
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