Pose analysis data

IntroductionMovement requires maneuvers that generate thrust to either make turns or move the body forward in physical space. The computational space for perpetually controlling the relative position of every point on the body surface can be vast. We hypothesize the evolution of efficient design for movement that minimizes active (neural) control by leveraging the passive (reactive) forces between the body and the surrounding medium at play. To test our hypothesis, we investigate the presence of stereotypical postures during free-swimming in adult zebrafish, Danio rerio.
MethodsWe perform markerless tracking using DeepLabCut (DLC), a deep learning pose estimation toolkit, to track geometric relationships between body parts. We identify putative clusters of postural configurations from twelve freely behaving zebrafish, using unsupervised multivariate time-series analysis (B-SOiD machine learning software) and of distances and angles between body segments extracted from DLC data.
ResultsWhen applied to single individuals, DLC data reveal a best-fit for 36 to 50 clusters in contrast to 86 clusters for data pooled from all 12 animals. The centroids of each cluster obtained over 14,000 sequential frames represent an apriori classification into relatively stable “target body postures”. We use multidimensional scaling of mean parameter values for each cluster to map cluster-centroids within two dimensions of postural space. From a posteriori visual analysis, we condense neighboring postural variants into 15 superclusters or core body configurations. We develop a nomenclature specifying the antero-posterior level/s (upper, mid and lower) and degree of bending
ConclusionOur results suggest that constraining bends to mainly three antero-posterior levels in fish paved the way for the evolution of a neck, fore- and hind-limb design for maneuverability in land vertebrates.