Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, USA.
Sci Rep. 2019 Dec 27;9(1):19979. doi: 10.1038/s41598-019-56408-9.
Tracking animal behavior by video is one of the most common tasks in the life sciences. Although commercial software exists for executing this task, they often present enormous cost to the researcher and can entail purchasing hardware that is expensive and lacks adaptability. Additionally, the underlying code is often proprietary. Alternatively, available open-source options frequently require model training and can be challenging for those inexperienced with programming. Here we present an open-source and platform independent set of behavior analysis pipelines using interactive Python that researchers with no prior programming experience can use. Two modules are described. One module can be used for the positional analysis of an individual animal, amenable to a wide range of behavioral tasks. A second module is described for the analysis of freezing behavior. For both modules, a range of interactive plots and visualizations are available to confirm that chosen parameters produce the anticipated results. Moreover, batch processing tools for the fast analysis of multiple videos is provided, and frame-by-frame output makes alignment with biological recording data simple. Lastly, options for cropping video frames to mitigate the influence of fiberoptic/electrophysiology cables, analyzing specified portions of time, and defining regions of interest, are readily implemented.
通过视频跟踪动物行为是生命科学中最常见的任务之一。虽然有商业软件可用于执行此任务,但它们通常对研究人员来说成本巨大,并且需要购买昂贵且缺乏适应性的硬件。此外,底层代码通常是专有的。或者,现有的开源选项通常需要进行模型训练,并且对于没有编程经验的人来说可能具有挑战性。在这里,我们提出了一组使用交互式 Python 的开源且与平台无关的行为分析管道,即使是没有编程经验的研究人员也可以使用。描述了两个模块。一个模块可用于单个动物的位置分析,适用于广泛的行为任务。第二个模块用于分析冻结行为。对于这两个模块,都提供了一系列交互式图和可视化效果,以确认所选参数是否产生预期的结果。此外,还提供了用于快速分析多个视频的批处理工具,逐帧输出可轻松与生物记录数据对齐。最后,可以轻松实现裁剪视频帧以减轻光纤/电生理学电缆影响、分析指定时间段和定义感兴趣区域的选项。