Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America.
Section of Ecology, Behavior, and Evolution, Division of Biological Sciences, University of California San Diego, La Jolla, California, United States of America.
PLoS One. 2021 May 12;16(5):e0251053. doi: 10.1371/journal.pone.0251053. eCollection 2021.
Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterise behavioural dynamics. For the model roundworm Caenorhabditis elegans, body pose can be accurately quantified from video as coordinates in a single low-dimensional space. We focus on this well-established case as an illustrative example and propose a method to reveal subtle variations in behaviour at high time resolution. Our data-driven method, based on empirical dynamic modeling, quantifies behavioural change as prediction error with respect to a time-delay-embedded 'attractor' of behavioural dynamics. Because this attractor is constructed from a user-specified reference data set, the approach can be tailored to specific behaviours of interest at the individual or group level. We validate the approach by detecting small changes in the movement dynamics of C. elegans at the initiation and completion of delta turns. We then examine an escape response initiated by an aversive stimulus and find that the method can track return to baseline behaviour in individual worms and reveal variations in the escape response between worms. We suggest that this general approach-defining dynamic behaviours using reference attractors and quantifying dynamic changes using prediction error-may be of broad interest and relevance to behavioural researchers working with video-derived time series.
现在，视频的自动化分析可以生成在自由移动的生物中广泛的姿势和运动时间序列。这需要新的定量工具来描述行为动力学。对于模型秀丽隐杆线虫，身体姿势可以从视频中准确地量化为单个低维空间中的坐标。我们专注于这个成熟的案例作为一个说明性的例子，并提出了一种方法来以高时间分辨率揭示行为的微妙变化。我们基于经验动态建模的数据驱动方法，将行为变化量化为与行为动力学的时滞嵌入“吸引子”的预测误差。因为这个吸引子是从用户指定的参考数据集构建的，所以该方法可以针对个体或群体水平上的特定感兴趣行为进行定制。我们通过检测秀丽隐杆线虫在 delta 转弯开始和完成时的运动动力学的微小变化来验证该方法。然后，我们检查了由厌恶刺激引发的逃避反应，发现该方法可以跟踪单个蠕虫的行为恢复到基线，并揭示蠕虫之间逃避反应的变化。我们认为，这种通用方法——使用参考吸引子定义动态行为，并使用预测误差量化动态变化——可能会引起从事基于视频的时间序列的行为研究人员的广泛兴趣和关注。
