Eric Meyers1, Anil Sindhurakar2, Rachel Choi3, Ruby Solorzano4, Taylor Martinez3, Andrew Sloan5, Jason Carmel6, Michael P Kilgard7, Robert L Rennaker8, Seth Hays9. 1. The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX 75080-3021, United States; The University of Texas at Dallas, Erik Jonsson School of Engineering and Computer Science, 800 West Campbell Road, Richardson, TX 75080-3021, United States. Electronic address: ecm081000@utdallas.edu. 2. Burke Medical Research Institute, 785 Mamaroneck Avenue, White Plains, NY 10605, United States. 3. The University of Texas at Dallas, School of Behavioral Brain Sciences, 800 West Campbell Road, GR41, Richardson, TX 75080-3021, United States. 4. The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX 75080-3021, United States. 5. Vulintus Inc., 17217 Waterview Pkwy, Ste 1.202BB, Dallas, TX 75252, United States. 6. Burke Medical Research Institute, 785 Mamaroneck Avenue, White Plains, NY 10605, United States; Weill Cornell Medical College, Brain Mind Research Institute and Departments of Neurology and Pediatrics, United States. 7. The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX 75080-3021, United States; The University of Texas at Dallas, School of Behavioral Brain Sciences, 800 West Campbell Road, GR41, Richardson, TX 75080-3021, United States; The University of Texas at Dallas, Erik Jonsson School of Engineering and Computer Science, 800 West Campbell Road, Richardson, TX 75080-3021, United States. 8. The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX 75080-3021, United States; Vulintus Inc., 17217 Waterview Pkwy, Ste 1.202BB, Dallas, TX 75252, United States; The University of Texas at Dallas, Erik Jonsson School of Engineering and Computer Science, 800 West Campbell Road, Richardson, TX 75080-3021, United States. 9. The University of Texas at Dallas, Texas Biomedical Device Center, 800 West Campbell Road, Richardson, TX 75080-3021, United States; The University of Texas at Dallas, Erik Jonsson School of Engineering and Computer Science, 800 West Campbell Road, Richardson, TX 75080-3021, United States.
Abstract
BACKGROUND: Neurological injuries or disease can impair the function of motor circuitry controlling forearm supination, and recovery is often limited. Preclinical animal models are essential tools for developing therapeutic interventions to improve motor function after neurological damage. Here we describe the supination assessment task, an automated measure of quantifying forelimb supination in the rat. NEW METHOD: Animals were trained to reach out of a slot in a cage, grasp a spherical manipulandum, and supinate the forelimb. The angle of the manipulandum was measured using a rotary encoder. If the animal exceeded the predetermined turn angle, a reward pellet was delivered. This automated task provides a large, high-resolution dataset of turn angle over time. Multiple parameters can be measured including success rate, peak turn angle, turn velocity, area under the curve, and number of rotations per trial. The task provides a high degree of flexibility to the user, with both software and hardware parameters capable of being adjusted. RESULTS: We demonstrate the supination assessment task can effectively measure significant deficits in multiple parameters of rotational motor function for multiple weeks in two models of ischemic stroke. COMPARISON WITH EXISTING METHODS: Preexisting motor assays designed to measure forelimb supination in the rat require high-speed video analysis techniques. This operant task provides a high-resolution, quantitative end-point dataset of turn angle, which obviates the necessity of video analysis. CONCLUSIONS: The supination assessment task represents a novel, efficient method of evaluating forelimb rotation and may help decrease the cost and time of running experiments.
BACKGROUND:Neurological injuries or disease can impair the function of motor circuitry controlling forearm supination, and recovery is often limited. Preclinical animal models are essential tools for developing therapeutic interventions to improve motor function after neurological damage. Here we describe the supination assessment task, an automated measure of quantifying forelimb supination in the rat. NEW METHOD: Animals were trained to reach out of a slot in a cage, grasp a spherical manipulandum, and supinate the forelimb. The angle of the manipulandum was measured using a rotary encoder. If the animal exceeded the predetermined turn angle, a reward pellet was delivered. This automated task provides a large, high-resolution dataset of turn angle over time. Multiple parameters can be measured including success rate, peak turn angle, turn velocity, area under the curve, and number of rotations per trial. The task provides a high degree of flexibility to the user, with both software and hardware parameters capable of being adjusted. RESULTS: We demonstrate the supination assessment task can effectively measure significant deficits in multiple parameters of rotational motor function for multiple weeks in two models of ischemic stroke. COMPARISON WITH EXISTING METHODS: Preexisting motor assays designed to measure forelimb supination in the rat require high-speed video analysis techniques. This operant task provides a high-resolution, quantitative end-point dataset of turn angle, which obviates the necessity of video analysis. CONCLUSIONS: The supination assessment task represents a novel, efficient method of evaluating forelimb rotation and may help decrease the cost and time of running experiments.
Authors: April M Becker; Eric Meyers; Andrew Sloan; Robert Rennaker; Michael Kilgard; Mark P Goldberg Journal: J Neurosci Methods Date: 2015-10-17 Impact factor: 2.390
Authors: Risa Kawai; Timothy Markman; Rajesh Poddar; Raymond Ko; Antoniu L Fantana; Ashesh K Dhawale; Adam R Kampff; Bence P Ölveczky Journal: Neuron Date: 2015-04-16 Impact factor: 17.173
Authors: Seth A Hays; Navid Khodaparast; Andrea Ruiz; Andrew M Sloan; Daniel R Hulsey; Robert L Rennaker; Michael P Kilgard Journal: Neuroreport Date: 2014-06-18 Impact factor: 1.837
Authors: Olivier Lambercy; Ludovic Dovat; Hong Yun; Seng Kwee Wee; Christopher W K Kuah; Karen S G Chua; Roger Gassert; Theodore E Milner; Chee Leong Teo; Etienne Burdet Journal: J Neuroeng Rehabil Date: 2011-11-16 Impact factor: 4.262
Authors: Andrew M Sloan; Melyssa K Fink; Amber J Rodriguez; Adam M Lovitz; Navid Khodaparast; Robert L Rennaker; Seth A Hays Journal: PLoS One Date: 2015-10-27 Impact factor: 3.240
Authors: Katherine S Adcock; Tanya Danaphongse; Sarah Jacob; Harshini Rallapalli; Miranda Torres; Zainab Haider; Armin Seyedahmadi; Robert A Morrison; Robert L Rennaker; Michael P Kilgard; Seth A Hays Journal: Sci Rep Date: 2022-06-11 Impact factor: 4.996
Authors: Eric C Meyers; Bleyda R Solorzano; Justin James; Patrick D Ganzer; Elaine S Lai; Robert L Rennaker; Michael P Kilgard; Seth A Hays Journal: Stroke Date: 2018-01-25 Impact factor: 7.914
Authors: Samuel D Butensky; Thelma Bethea; Joshua Santos; Anil Sindhurakar; Eric Meyers; Andrew M Sloan; Robert L Rennaker; Jason B Carmel Journal: J Vis Exp Date: 2017-09-28 Impact factor: 1.355
Authors: Patrick D Ganzer; Michael J Darrow; Eric C Meyers; Bleyda R Solorzano; Andrea D Ruiz; Nicole M Robertson; Katherine S Adcock; Justin T James; Han S Jeong; April M Becker; Mark P Goldberg; David T Pruitt; Seth A Hays; Michael P Kilgard; Robert L Rennaker Journal: Elife Date: 2018-03-13 Impact factor: 8.140
Authors: Katherine S Adcock; Daniel R Hulsey; Tanya Danaphongse; Zainab Haider; Robert A Morrison; Michael P Kilgard; Seth A Hays Journal: Pain Rep Date: 2021-09-16