Computational Methods for Tracking, Quantitative Assessment, and Visualization of C. elegans Locomotory Behavior

The nematode Caenorhabditis elegans provides a unique opportunity to interrogate the neural basis of behavior at single neuron resolution. In C. elegans, neural circuits that control behaviors can be formulated based on its complete neural connection map, and easily assessed by applying advanced genetic tools that allow for modulation in the activity of specific neurons. Importantly, C. elegans exhibits several elaborate behaviors that can be empirically quantified and analyzed, thus providing a means to assess the contribution of specific neural circuits to behavioral output. Particularly, locomotory behavior can be recorded and analyzed with computational and mathematical tools. Here, we describe a robust single worm-tracking system, which is based on the open-source Python programming language, and an analysis system, which implements path-related algorithms. Our tracking system was designed to accommodate worms that explore a large area with frequent turns and reversals at high speeds. As a proof of principle, we used our tracker to record the movements of wild-type animals that were freshly removed from abundant bacterial food, and determined how wild-type animals change locomotory behavior over a long period of time. Consistent with previous findings, we observed that wild-type animals show a transition from area-restricted local search to global search over time. Intriguingly, we found that wild-type animals initially exhibit short, random movements interrupted by infrequent long trajectories. This movement pattern often coincides with local/global search behavior, and visually resembles Lévy flight search, a search behavior conserved across species. Our mathematical analysis showed that while most of the animals exhibited Brownian walks, approximately 20% of the animals exhibited Lévy flights, indicating that C. elegans can use Lévy flights for efficient food search. In summary, our tracker and analysis software will help analyze the neural basis of the alteration and transition of C. elegans locomotory behavior in a food-deprived condition.秀丽隐杆线虫（Caenorhabditis elegans）为在单细胞分辨率下解析行为的神经基础提供了独特的研究契机。在秀丽隐杆线虫中，调控行为的神经环路可基于其完整的神经连接图谱进行构建，并可通过使用可调控特定神经元活动的先进遗传工具进行便捷评估。尤为重要的是，秀丽隐杆线虫具备多种可通过实验量化分析的复杂行为模式，这为解析特定神经环路对行为输出的贡献提供了可行途径。具体而言，其运动行为可通过计算与数学工具进行记录与分析。本研究中，我们描述了一套基于开源Python编程语言的鲁棒单线虫追踪系统，以及一套集成路径相关算法的分析系统。本追踪系统专为适配高速下频繁转向、反转且探索大范围区域的线虫而设计。作为原理验证，我们利用该追踪系统记录了从充足细菌食物中刚分离出的野生型线虫的运动轨迹，并解析了野生型线虫在长时间尺度下的运动行为变化。与既往研究结果一致，我们观察到野生型线虫随时间推移会从区域受限的局部搜索模式转变为全局搜索模式。有趣的是，我们发现野生型线虫初始会呈现短暂的随机运动，其间穿插着不频繁的长距离轨迹。该运动模式常与局部/全局搜索行为相契合，且在视觉上类似于莱维飞行（Lévy flight）搜索——这是一种在多个物种中保守存在的搜索行为。我们的数学分析显示，尽管多数线虫呈现布朗运动（Brownian walk），但约20%的线虫表现出莱维飞行行为，这表明秀丽隐杆线虫可借助莱维飞行实现高效的食物搜寻。综上，本研究开发的追踪系统与分析软件将有助于解析秀丽隐杆线虫在食物匮乏条件下运动行为的改变与转变的神经基础。