Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB, Canada.
PLoS One. 2019 Aug 2;14(8):e0220751. doi: 10.1371/journal.pone.0220751. eCollection 2019.
In the current research on measuring complex behaviours/phenotyping in rodents, most of the experimental design requires the experimenter to remove the animal from its home-cage environment and place it in an unfamiliar apparatus (novel environment). This interaction may influence behaviour, general well-being, and the metabolism of the animal, affecting the phenotypic outcome even if the data collection method is automated. Most of the commercially available solutions for home-cage monitoring are expensive and usually lack the flexibility to be incorporated with existing home-cages. Here we present a low-cost solution for monitoring home-cage behaviour of rodents that can be easily incorporated to practically any available rodent home-cage. To demonstrate the use of our system, we reliably predict the sleep/wake state of mice in their home-cage using only video. We validate these results using hippocampal local field potential (LFP) and electromyography (EMG) data. Our approach provides a low-cost flexible methodology for high-throughput studies of sleep, circadian rhythm and rodent behaviour with minimal experimenter interference.
在目前对啮齿动物复杂行为/表型的研究中，大多数实验设计要求实验人员将动物从其笼内环境中取出，并将其放入不熟悉的设备（新环境）中。这种交互作用可能会影响动物的行为、整体健康和新陈代谢，即使数据采集方法是自动化的，也会影响表型结果。大多数市售的笼内监测解决方案都很昂贵，并且通常缺乏与现有笼内整合的灵活性。在这里，我们提出了一种用于监测啮齿动物笼内行为的低成本解决方案，该解决方案可以轻松地集成到几乎任何可用的啮齿动物笼内。为了演示我们系统的用途，我们仅使用视频可靠地预测了老鼠在其笼内的睡眠/觉醒状态。我们使用海马局部场电位（LFP）和肌电图（EMG）数据验证了这些结果。我们的方法提供了一种具有成本效益且灵活的方法，可用于对睡眠、昼夜节律和啮齿动物行为进行高通量研究，而对实验人员的干扰最小。
