Abstract
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.
Highlights
The nematode Caenorhabditis elegans provides many advantages for unraveling the principles underlying functional neural circuits
Our algorithm is based on the work by Reynolds et al [23] and Codling et al [24] in which the location of a turning event was defined where “the angle between two movement segments joining three successive positional fixes is less than a critical angle”. While they applied the algorithm to understand the movement behavior of fruit flies (Drosophila melanogaster) from short video tracking data and to investigate optimal search strategies for foragers based on computer simulated data, we propose to apply it to quantitatively describe the movement path of C. elegans in the presence and absence of food for longer periods of time
We determined whether tph-1 mutants, which are defective in producing serotonin and exhibit several food-related behavioral deficits, show altered food search behavior in comparison with wildtype animals
Summary
The nematode Caenorhabditis elegans provides many advantages for unraveling the principles underlying functional neural circuits. We can activate or inactivate specific neurons using optogenetic tools [2, 3], ablate specific neurons by expressing caspase-1 [4, 5], or reduce synaptic transmission of specific neurons by expressing tetanus toxin [6, 7]. It is clear from current C. elegans neural circuit studies that the basic building blocks of nervous systems (modules of neural networks) are conserved across species. The knowledge gained from C. elegans studies will be directly applicable to more complex mammalian nervous systems
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