摘要
Understanding the connection between brain and behavior in animals requires precise monitoring of their behaviors in three-dimensional(3-D)space.However,there is no available three-dimensional behavior capture system that focuses on rodents.Here,we present MouseVenue3D,an automated and low-cost system for the efficient capture of 3-D skeleton trajectories in markerless rodents.We improved the most time-consuming step in 3-D behavior capturing by developing an automatic calibration module.Then,we validated this process in behavior recognition tasks,and showed that 3-D behavioral data achieved higher accuracy than 2-D data.Subsequently,MouseVenue3D was combined with fast high-resolution miniature two-photon microscopy for synchronous neural recording and behavioral tracking in the freely-moving mouse.Finally,we successfully decoded spontaneous neuronal activity from the 3-D behavior of mice.Our findings reveal that subtle,spontaneous behavior modules are strongly correlated with spontaneous neuronal activity patterns.
基金
the Key Area R&D Program of Guangdong Province,China(2018B030338001 and 2018B030331001)
the National Key R&D Program of China(2018YFA0701403)
the National Natural Science Foundation of China(31500861,31630031,91732304,and 31930047)
Chang Jiang Scholars Program,the International Big Science Program Cultivating Project of the Chinese Academy of Science(CAS
172644KYS820170004)
the Strategic Priority Research Program of the CAS(XDB32030100)
the Youth Innovation Promotion Association of the CAS(2017413)
the CAS Key Laboratory of Brain Connectome and Manipulation(2019DP173024)
Shenzhen Government Basic Research Grants(JCYJ20170411140807570,JCYJ20170413164535041)
the Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ20160429185235132)
a Helmholtz-CAS Joint Research grant(GJHZ1508)
Guangdong Provincial Key Laboratory of Brain Connectome and Behavior(2017B030301017)
the Ten Thousand Talent Program,the Guangdong Special Support Program,Key Laboratory of Shenzhen Institute of Advanced Technology(2019DP173024)
the Shenzhen Key Science and Technology Infrastructure Planning Project(ZDKJ20190204002).
