Tracking changes in behavioural dynamics using prediction error

Abstract

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.

Recent developments in empirical dynamic modelling

“…Although attractor reconstruction applies formally only to stationary systems, there are extensions of EDM that can deal with non‐stationarity. At its simplest, EDM can provide an equation‐free test for non‐stationarity by evaluating prediction accuracy across libraries constructed from different time periods (Schreiber, 1997) or different modes of dynamic behaviour (Lorimer et al, 2021)—in effect, for anomaly detection. When the relevant environmental drivers are known, incorporating these into the embedding may be sufficient for prediction in non‐stationary systems (e.g.…”

“…The number of datasets, tools for analysis and range of questions that can be addressed with EDM have expanded dramatically since (Schreiber, 1997) or different modes of dynamic behaviour (Lorimer et al, 2021)-in effect, for anomaly detection.…”

Caenorhabditis elegans Multi-Tracker Based on a Modified Skeleton Algorithm

“…These assays, like others, are visualized manually, due to the complexity of solving the identification problem during an overlapping or body contact of these worms. Currently, the automatic [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] or semi-automatic applications discard the data from tracks where there are these particular cases (overlapping and bodies contacts) [ 22 , 23 , 24 ]. Overlapping can take place among worms or may also be due to plate noise.…”

Desarrollo de técnicas avanzadas de seguimiento de posturas para reconocimiento de comportamientos de C. elegans

“…These assays, like others, are visualized manually, due to the complexity of solving the identification problem during an overlapping or body contact of these worms. Currently, the automatic [74,89,70,17,83,32,6,18,50] or semi-automatic applications discard the data from tracks where there are these particular cases (overlapping and bodies contacts) [33,38,45]. Overlapping can take place among worms or may also be due to plate noise.…”