Tasos Karakostas1, Simon Hsiang2, Heather Boger3, Lawrence Middaugh3, Ann-Charlotte Granholm3. 1. Motion Analysis Center and Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA; Department of Physical Therapy, Medical University of South Carolina, Charleston, SC, USA; Department of Orthopaedic Surgery, Northwestern University, Chicago, IL, USA. Electronic address: tkarakosta@ric.org. 2. Department of Industrial Engineering, Texas Tech University, Lubbock, TX, USA. 3. Departments of Neurosciences and Psychiatry, and The Center on Aging, Medical University of South Carolina, Charleston, SC, USA.
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.
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.
Authors: Samarjit Das; Laura Trutoiu; Akihiko Murai; Dunbar Alcindor; Michael Oh; Fernando De la Torre; Jessica Hodgins Journal: Conf Proc IEEE Eng Med Biol Soc Date: 2011
Authors: Heather A Boger; Ann-Charlotte Granholm; Jacqueline F McGinty; Lawrence D Middaugh Journal: Prog Neurobiol Date: 2009-10-21 Impact factor: 11.685
Authors: J K Madete; A Klein; A Fuller; R C Trueman; A E Rosser; S B Dunnett; C A Holt Journal: Proc Inst Mech Eng H Date: 2010-11 Impact factor: 1.617