Victor A Kulikov1, Nikita V Khotskin2, Sergey V Nikitin2, Vasily S Lankin2, Alexander V Kulikov3, Oleg V Trapezov2. 1. Institute of Automation and Electrometry, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia. 2. Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia. 3. Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia. Electronic address: akulikov@ngs.ru.
Abstract
BACKGROUND: The minipig is a promising model in neurobiology and psychopharmacology. However, automated tracking of minipig behavior is still unresolved problem. NEW METHOD: The study was carried out on white, agouti and black (or spotted) minipiglets (n=108) bred in the Institute of Cytology and Genetics. New method of automated tracking of minipig behavior is based on Microsoft Kinect 3-D image sensor and the 3-D image reconstruction with EthoStudio software. The algorithms of distance run and time in the center evaluation were adapted for 3-D image data and new algorithm of vertical activity quantification was developed. RESULTS: The 3-D imaging system successfully detects white, black, spotted and agouti pigs in the open field test (OFT). No effect of sex or color on horizontal (distance run), vertical activities and time in the center was shown. Agouti pigs explored the arena more intensive than white or black animals, respectively. The OFT behavioral traits were compared with the fear reaction to experimenter. Time in the center of the OFT was positively correlated with fear reaction rank (ρ=0.21, p<0.05). Black pigs were significantly more fearful compared with white or agouti animals. COMPARISON WITH EXISTING METHOD: The 3-D imaging system has three advantages over existing automated tracking systems: it avoids perspective distortion, distinguishes animals any color from any background and automatically evaluates vertical activity. CONCLUSION: The 3-D imaging system can be successfully applied for automated measurement of minipig behavior in neurobiological and psychopharmacological experiments.
BACKGROUND: The minipig is a promising model in neurobiology and psychopharmacology. However, automated tracking of minipig behavior is still unresolved problem. NEW METHOD: The study was carried out on white, agouti and black (or spotted) minipiglets (n=108) bred in the Institute of Cytology and Genetics. New method of automated tracking of minipig behavior is based on Microsoft Kinect 3-D image sensor and the 3-D image reconstruction with EthoStudio software. The algorithms of distance run and time in the center evaluation were adapted for 3-D image data and new algorithm of vertical activity quantification was developed. RESULTS: The 3-D imaging system successfully detects white, black, spotted and agouti pigs in the open field test (OFT). No effect of sex or color on horizontal (distance run), vertical activities and time in the center was shown. Agouti pigs explored the arena more intensive than white or black animals, respectively. The OFT behavioral traits were compared with the fear reaction to experimenter. Time in the center of the OFT was positively correlated with fear reaction rank (ρ=0.21, p<0.05). Black pigs were significantly more fearful compared with white or agouti animals. COMPARISON WITH EXISTING METHOD: The 3-D imaging system has three advantages over existing automated tracking systems: it avoids perspective distortion, distinguishes animals any color from any background and automatically evaluates vertical activity. CONCLUSION: The 3-D imaging system can be successfully applied for automated measurement of minipig behavior in neurobiological and psychopharmacological experiments.
Authors: Weizhe Hong; Ann Kennedy; Xavier P Burgos-Artizzu; Moriel Zelikowsky; Santiago G Navonne; Pietro Perona; David J Anderson Journal: Proc Natl Acad Sci U S A Date: 2015-09-09 Impact factor: 11.205
Authors: Mariya A Borisova; Olga A Snytnikova; Ekaterina A Litvinova; Kseniya M Achasova; Tatiana I Babochkina; Alexey V Pindyurin; Yuri P Tsentalovich; Elena N Kozhevnikova Journal: Nutrients Date: 2020-02-11 Impact factor: 5.717
Authors: Kaitlin Wurtz; Irene Camerlink; Richard B D'Eath; Alberto Peña Fernández; Tomas Norton; Juan Steibel; Janice Siegford Journal: PLoS One Date: 2019-12-23 Impact factor: 3.240
Authors: Enrico Ullmann; George Chrousos; Seth W Perry; Ma-Li Wong; Julio Licinio; Stefan R Bornstein; Olga Tseilikman; Maria Komelkova; Maxim S Lapshin; Maryia Vasilyeva; Evgenii Zavjalov; Oleg Shevelev; Nikita Khotskin; Galina Koncevaya; Anna S Khotskina; Mikhail Moshkin; Olga Cherkasova; Alexey Sarapultsev; Roman Ibragimov; Igor Kritsky; Jörg M Fegert; Vadim Tseilikman; Rachel Yehuda Journal: Int J Mol Sci Date: 2020-10-09 Impact factor: 5.923