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開催日 2014/9/13
時間 11:00 - 12:00
会場 Poster / Exhibition(Event Hall B)

Development and evaluation of 3-D analysis systems for mouse behaviors

  • P3-385
  • 豊田 淳 / Atsushi Toyoda:1,2,3 後藤 達彦 / Tatsuhiko Goto:1,2 岡山 毅 / Tsuyoshi Okayama:1,2,3 
  • 1:茨城大農生物生産・動物生産 / Col of Agri, Ibaraki Univ, Ibaraki, Japan 2:茨城大・農医連携 / Ibaraki Univ Coop between Agri and Med Sci 3:東京農工大院・連合農 / Unit Grad Sch of Agri Sci, Tokyo Univ of Agri and Tech, Tokyo, Japan 

Animal behaviors are studied in various fields and often evaluated by human eyes. Because the observations by humans are practically subjective and time consuming, many automated analysis systems for animal behaviors have been developed for researches. Automated systems mainly use two-dimensional (2-D) images, while evaluating 2-D information has some difficulties to follow 3-D behaviors in mice. Therefore, we have been developing the analysis system for mouse behaviors using the commercial 3-D depth sensor, and will report the results of behavioral analysis for nest building and social interaction in mice at this annual meeting.
Seven-week-old male C57BL/6J (B6) and retired ICR were used in this study. Mice were housed at room temperature (22 ± 1 °C) with exposure to light from 7:00 to 19:00 and ad libitum access to food and water. A cage was made with acrylic transparent boards which transmit near infrared light. A 3-D depth sensor (Xtion LIVE PRO, ASUS) was placed above the cage, and mouse behaviors were measured by the sensor following the data analyzes. Nest building and resident intruder tests were performed as the previous reports (Nature Protocols, 2006, Nature Neuroscience, 2006). All the experimental procedures followed the guidelines of the Animal Care and Use Committee of Ibaraki University.
In the nest building test, we could observe the building processes of the nest using the 3-D depth sensor and finally evaluate the volume and shape of the nest. In the resident intruder test, B6 were repeatedly attacked by ICR and finally showed submissive postures as described previously. Using the depth sensor, we could evaluate and analyze the counts of biting and jumping behaviors in the tests.

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