Deep brain electrophysiology in freely moving sheep

CURRENT BIOLOGY(2022)

引用 3|浏览8
暂无评分
摘要
Although rodents are arguably the easiest animals to use for studying brain function, relying on them as model species for translational research comes with its own set of limitations. Here, we propose sheep as a practical large animal species to use for in vivo brain function studies performed in naturalistic settings. We conducted proof-of-principle deep brain electrophysiological recording experiments using unrestrained sheep during behavioral testing. Recordings were made from cortex and hippocampus, both while sheep performed goal-directed behaviors (two-choice discrimination tasks) and across states of vigilance, including sleep. Hippocampal and cortical oscillatory rhythms were consistent with those seen in rodents and non-human primates, and included cortical alpha oscillations and hippocampal sharp wave ripple oscillations (-150 Hz) during immobility and hippocampal theta oscillations (5-6 Hz) during locomotion. Recordings were conducted over a period of many months during which time the animals participated willingly in the experiments. Over 3,000 putative neurons were identified, including examples whose activity was modulated by task, speed of locomotion, spatial position, reward and vigilance states, and one whose firing rate was potentially modulated by the sight of the investigator. Together, these experiments demonstrate that sheep are excellent experimental animals to use for longitudinal studies requiring a large-brained mammal and/or large-scale recordings across distributed neuronal networks. Sheep could be used safely for studying not only neural encoding of decision-making and spatial-mapping in naturalistic environments outside the confines of the traditional laboratory but also the neural basis of both intra-and inter-species social interactions.
更多
查看译文
关键词
cognition,learning,network dynamics,oscillations,place cells,remapping,reward,sleep,tetrodes,wakefulness
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要