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Behavioral phenotyping of model organisms has played an important role in unravelling the complexities of animal behavior. Techniques for classifying behavior often rely on easily identified changes in posture and motion. However, such approaches are likely to miss complex behaviors that cannot be readily distinguished by eye (e.g., behaviors produced by high dimensional dynamics). To explore this issue, we focus on the model organism Caenorhabditis elegans, where behaviors have been extensively recorded and classified. Using a dynamical systems lens, we identify high dimensional, nonlinear causal relationships between four basic shapes that describe worm motion (eigenmodes, also called “eigenworms”). We find relationships between all pairs of eigenmodes, but the timescales of the interactions vary between pairs and across individuals. Using these varying timescales, we create “interaction profiles” to represent an individual’s behavioral dynamics. As desired, these profiles are able to distinguish well-known behavioral states: i.e., the profiles for foraging individuals are distinct from those of individuals exhibiting an escape response. More importantly, we find that interaction profiles can distinguish high dimensional behaviors among divergent mutant strains that were previously classified as phenotypically similar. Specifically, we find it is able to detect phenotypic behavioral differences not previously identified in strains related to dysfunction of hermaphrodite-specific neurons., Author summary A primary reason for studying C. elegans is the possibility of achieving an end-to-end understanding of animal behaviour—one that mechanistically links genes to behaviour through neurobiology. With the genome and connectome fully mapped, techniques for measuring the behavior of C. elegans have traditionally relied on easily identified changes in posture and motion. Such approaches, however, can miss complex and more subtle behaviors that cannot be readily distinguished by eye. To explore this issue, we develop an attractor-based network approach to show that the four basic shapes classically used to describe worm position can be related to each other in a network that represents how the shapes interact with each other over time. That is, we study not just worm poses, but how the poses interact. Importantly, we find that these interaction networks can distinguish subtle high dimensional behaviors among mutant strains previously classified as phenotypically similar, thereby making genetic distinctions not previously possible. Thus, the value of this attractor-based network approach is its ability to detect subtle differences that can discriminate macroscopic behavior at the gene-mutation level. We anticipate these methods will be of interest to behaviorists, neuroscientists, and geneticists studying the connection between behavior and genes.

Automated analysis of video can now generate extensive time series of pose and motion in freely-moving organisms. This requires new quantitative tools to characterize 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.