April M Becker1, Eric Meyers2, Andrew Sloan2, Robert Rennaker2, Michael Kilgard3, Mark P Goldberg4. 1. University of Texas Southwestern Medical Center, Department of Neurology and Neurotherapeutics, Dallas, TX, United States; University of Texas Southwestern Medical Center, Neuroscience PhD Program, Dallas, TX, United States. 2. University of Texas at Dallas, Erik Jonsson School of Engineering and Computer Science, Dallas, TX, United States. 3. University of Texas at Dallas, School of Behavioral and Brain Sciences, Dallas, TX, United States. 4. University of Texas Southwestern Medical Center, Department of Neurology and Neurotherapeutics, Dallas, TX, United States; University of Texas Southwestern Medical Center, Neuroscience PhD Program, Dallas, TX, United States. Electronic address: mark.goldberg@UTSouthwestern.edu.
Abstract
BACKGROUND: Behavioral models relevant to stroke research seek to capture important aspects of motor skills typically impaired in human patients, such as coordination of distal musculature. Such models may focus on mice since many genetic tools are available for use only in that species and since the training and behavioral demands of mice can differ from rats even for superficially similar behavioral readouts. However, current mouse assays are time consuming to train and score, especially in a manner producing continuous quantification. An automated assay of mouse forelimb function may provide advantages for quantification and speed, and may be useful for many applications including stroke research. NEW METHOD: We present an automated assay of distal forelimb function. In this task, mice reach forward, grip and pull an isometric handle with a prescribed force. The apparatus partially automates the training process so that mice can be trained quickly and simultaneously. RESULTS: Using this apparatus, it is possible to measure long-lasting impairment in success rate, force pulled, latency to pull, and latency to success up to 22 weeks following photothrombotic cortical strokes in mice. COMPARISON WITH EXISTING METHOD(S): This assessment measures forelimb function as do pellet reach tasks, but it utilizes a different motion and provides automatic measures that can ease and augment the research process. CONCLUSIONS: This high-throughput behavioral assay can detect long-lasting motor impairments, eliminates the need for subjective scoring, and produces a rich, continuous data set from which many aspects of the reach and grasp motion can be automatically extracted.
BACKGROUND: Behavioral models relevant to stroke research seek to capture important aspects of motor skills typically impaired in humanpatients, such as coordination of distal musculature. Such models may focus on mice since many genetic tools are available for use only in that species and since the training and behavioral demands of mice can differ from rats even for superficially similar behavioral readouts. However, current mouse assays are time consuming to train and score, especially in a manner producing continuous quantification. An automated assay of mouse forelimb function may provide advantages for quantification and speed, and may be useful for many applications including stroke research. NEW METHOD: We present an automated assay of distal forelimb function. In this task, mice reach forward, grip and pull an isometric handle with a prescribed force. The apparatus partially automates the training process so that mice can be trained quickly and simultaneously. RESULTS: Using this apparatus, it is possible to measure long-lasting impairment in success rate, force pulled, latency to pull, and latency to success up to 22 weeks following photothrombotic cortical strokes in mice. COMPARISON WITH EXISTING METHOD(S): This assessment measures forelimb function as do pellet reach tasks, but it utilizes a different motion and provides automatic measures that can ease and augment the research process. CONCLUSIONS: This high-throughput behavioral assay can detect long-lasting motor impairments, eliminates the need for subjective scoring, and produces a rich, continuous data set from which many aspects of the reach and grasp motion can be automatically extracted.
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