Journal of neuroscience methods · 2014
Validation of video motion-detection scoring of forced swim test in mice.
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Gao Vance, Vitaterna Martha Hotz, Turek Fred W
Аннотация
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.
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.