Chelsea C Wong1, Dhakshin S Ramanathan2, Tanuj Gulati1, Seok Joon Won1, Karunesh Ganguly3. 1. Neurology & Rehabilitation Service, San Francisco VA Medical Center, San Francisco, CA, United States; Department of Neurology, University of California, San Francisco, CA, United States. 2. Neurology & Rehabilitation Service, San Francisco VA Medical Center, San Francisco, CA, United States; Psychiatry Service, San Francisco VA Medical Center, San Francisco, CA, United States; Department of Psychiatry, University of California, San Francisco, CA, United States. 3. Neurology & Rehabilitation Service, San Francisco VA Medical Center, San Francisco, CA, United States; Department of Neurology, University of California, San Francisco, CA, United States. Electronic address: karunesh.ganguly@ucsf.edu.
Abstract
BACKGROUND: Rodent forelimb reaching behaviors are commonly assessed using a single-pellet reach-to-grasp task. While the task is widely recognized as a very sensitive measure of distal limb function, it is also known to be very labor-intensive, both for initial training and the daily assessment of function. NEW METHOD: Using components developed by open-source electronics platforms, we have designed and tested a low-cost automated behavioral box to measure forelimb function in rats. Our apparatus, made primarily of acrylic, was equipped with multiple sensors to control the duration and difficulty of the task, detect reach outcomes, and dispense pellets. Our control software, developed in MATLAB, was also used to control a camera in order to capture and process video during reaches. Importantly, such processing could monitor task performance in near real-time. RESULTS: We further demonstrate that the automated apparatus can be used to expedite skill acquisition, thereby increasing throughput as well as facilitating studies of early versus late motor learning. The setup is also readily compatible with chronic electrophysiological monitoring. COMPARISON WITH EXISTING METHODS: Compared to a previous version of this task, our setup provides a more efficient method to train and test rodents for studies of motor learning and recovery of function after stroke. The unbiased delivery of behavioral cues and outcomes also facilitates electrophysiological studies. CONCLUSIONS: In summary, our automated behavioral box will allow high-throughput and efficient monitoring of rat forelimb function in both healthy and injured animals. Published by Elsevier B.V.
BACKGROUND: Rodent forelimb reaching behaviors are commonly assessed using a single-pellet reach-to-grasp task. While the task is widely recognized as a very sensitive measure of distal limb function, it is also known to be very labor-intensive, both for initial training and the daily assessment of function. NEW METHOD: Using components developed by open-source electronics platforms, we have designed and tested a low-cost automated behavioral box to measure forelimb function in rats. Our apparatus, made primarily of acrylic, was equipped with multiple sensors to control the duration and difficulty of the task, detect reach outcomes, and dispense pellets. Our control software, developed in MATLAB, was also used to control a camera in order to capture and process video during reaches. Importantly, such processing could monitor task performance in near real-time. RESULTS: We further demonstrate that the automated apparatus can be used to expedite skill acquisition, thereby increasing throughput as well as facilitating studies of early versus late motor learning. The setup is also readily compatible with chronic electrophysiological monitoring. COMPARISON WITH EXISTING METHODS: Compared to a previous version of this task, our setup provides a more efficient method to train and test rodents for studies of motor learning and recovery of function after stroke. The unbiased delivery of behavioral cues and outcomes also facilitates electrophysiological studies. CONCLUSIONS: In summary, our automated behavioral box will allow high-throughput and efficient monitoring of rat forelimb function in both healthy and injured animals. Published by Elsevier B.V.
Entities:
Keywords:
Electrophysiology; Motor learning; Reach
Authors: James M Conner; Andrew Culberson; Christine Packowski; Andrea A Chiba; Mark H Tuszynski Journal: Neuron Date: 2003-06-05 Impact factor: 17.173
Authors: Seth A Hays; Navid Khodaparast; Andrew M Sloan; Daniel R Hulsey; Maritza Pantoja; Andrea D Ruiz; Michael P Kilgard; Robert L Rennaker Journal: J Neurosci Methods Date: 2012-11-23 Impact factor: 2.390
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: Eric Meyers; Anil Sindhurakar; Rachel Choi; Ruby Solorzano; Taylor Martinez; Andrew Sloan; Jason Carmel; Michael P Kilgard; Robert L Rennaker; Seth Hays Journal: J Neurosci Methods Date: 2016-03-11 Impact factor: 2.390
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: 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