Vance Gao1, Martha Hotz Vitaterna2, Fred W Turek3. 1. Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, 2205 Tech Drive, Hogan Hall 2-160, Evanston, IL 60208, United States. Electronic address: v-gao@u.northwestern.edu. 2. Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, 2205 Tech Drive, Hogan Hall 2-160, Evanston, IL 60208, United States. Electronic address: m-vitaterna@northwestern.edu. 3. Center for Sleep and Circadian Biology, Department of Neurobiology, Northwestern University, 2205 Tech Drive, Hogan Hall 2-160, Evanston, IL 60208, United States. Electronic address: fturek@northwestern.edu.
Abstract
BACKGROUND: The forced swim test (FST) is used to predict the effectiveness of novel antidepressant treatments. In this test, a mouse or rat is placed in a beaker of water for several minutes, and the amount of time spent passively floating is measured; antidepressants reduce the amount of such immobility. Though the FST is commonly used, manually scoring the test is time-consuming and involves considerable subjectivity. NEW METHOD: We developed a simple MATLAB-based motion-detection method to quantify mice's activity in videos of FST. FST trials are video-recorded from a side view. Each pixel of the video is compared between subsequent video frames; if the pixel's color difference surpasses a threshold, a motion count is recorded. RESULTS: Human-scored immobility time correlates well with total motion detected by the computer (r=-0.80) and immobility time determined by the computer (r=0.83). Our computer method successfully detects group differences in activity between genotypes and different days of testing. Furthermore, we observe heterosis for this behavior, in which (C57BL/6J×A/J) F1 hybrid mice are more active in the FST than the parental strains. COMPARISON WITH EXISTING METHODS: This computer-scoring method is much faster and more objective than human scoring. Other automatic scoring methods exist, but they require the purchase of expensive hardware and/or software. CONCLUSION: This computer-scoring method is an effective, fast, and low-cost method of quantifying the FST. It is validated by replicating statistical differences observed in traditional visual scoring. We also demonstrate a case of heterosis in the FST.
BACKGROUND: The forced swim test (FST) is used to predict the effectiveness of novel antidepressant treatments. In this test, a mouse or rat is placed in a beaker of water for several minutes, and the amount of time spent passively floating is measured; antidepressants reduce the amount of such immobility. Though the FST is commonly used, manually scoring the test is time-consuming and involves considerable subjectivity. NEW METHOD: We developed a simple MATLAB-based motion-detection method to quantify mice's activity in videos of FST. FST trials are video-recorded from a side view. Each pixel of the video is compared between subsequent video frames; if the pixel's color difference surpasses a threshold, a motion count is recorded. RESULTS:Human-scored immobility time correlates well with total motion detected by the computer (r=-0.80) and immobility time determined by the computer (r=0.83). Our computer method successfully detects group differences in activity between genotypes and different days of testing. Furthermore, we observe heterosis for this behavior, in which (C57BL/6J×A/J) F1 hybrid mice are more active in the FST than the parental strains. COMPARISON WITH EXISTING METHODS: This computer-scoring method is much faster and more objective than human scoring. Other automatic scoring methods exist, but they require the purchase of expensive hardware and/or software. CONCLUSION: This computer-scoring method is an effective, fast, and low-cost method of quantifying the FST. It is validated by replicating statistical differences observed in traditional visual scoring. We also demonstrate a case of heterosis in the FST.
Authors: Suhasa B Kodandaramaiah; Francisco J Flores; Edward S Boyden; Craig R Forest; Gregory L Holst; Annabelle C Singer; Xue Han; Emery N Brown Journal: Elife Date: 2018-01-03 Impact factor: 8.140
Authors: Joseph R Scarpa; Peng Jiang; Vance D Gao; Karrie Fitzpatrick; Joshua Millstein; Christopher Olker; Anthony Gotter; Christopher J Winrow; John J Renger; Andrew Kasarskis; Fred W Turek; Martha H Vitaterna Journal: Sci Adv Date: 2018-07-25 Impact factor: 14.136