Quantitative analysis of animal behaviour in model organisms is becoming an increasingly essential approach for tackling the great challenge of understanding how activity in the brain gives rise to behaviour. Here we used automated image-based tracking to extract behavioural features from an organism of great importance in understanding the evolution of chordates, the free-swimming larval form of the tunicate Ciona intestinalis, which has a compact and fully mapped nervous system composed of only 231 neurons. We analysed hundreds of videos of larvae and we extracted basic geometric and physical descriptors of larval behaviour. Importantly, we used machine learning methods to create an objective ontology of behaviours for C. intestinalis larvae. We identified eleven behavioural modes using agglomerative clustering. Using our pipeline for quantitative behavioural analysis, we demonstrate that C. intestinalis larvae exhibit sensory arousal and thigmotaxis. Notably, the anxiotropic drug modafinil modulates thigmotactic behaviour. Furthermore, we tested the robustness of the larval behavioural repertoire by comparing different rearing conditions, ages and group sizes. This study shows that C. intestinalis larval behaviour can be broken down to a set of stereotyped behaviours that are used to different extents in a context-dependent manner.

Quantitative analysis of animal behaviour in model organisms is becoming an increasingly essential approach for tackling the great challenge of understanding how activity in the brain gives rise to behaviour. In addition, behavioural analysis can provide insight on the molecular basis of nervous system development and function as demonstrated by genetic screens focused on behavioural phenotyping in some genetically tractable model organisms. The progress in building low-cost automated tracking setups, together with advances in computer vision machine learning have expanded the repertoire of organisms which are amenable to quantitative behavioural analysis. Here we used automated image-based tracking to extract behavioural features from an organism of great importance in understanding the evolution of chordates, the free swimming larval form of the tunicate Ciona intestinalis which has a compact and fully mapped nervous system composed of only 231 neurons. We analysed hundreds of videos of larvae and we extracted basic geometric and physical descriptors of larval behaviour. Most importantly, we used machine learning methods to create an objective ontology of behaviours for C. intestinalis larvae. We identified eleven behavioural modes using agglomerative clustering. This approach enabled us to produce a quantitative description of the basic larval behavioural repertoire. Furthermore, we tested the robustness of this repertoire by comparing different rearing conditions and ages. Using our pipeline for quantitative behavioural analysis, we successfully reproduced the known photoresponsive behaviour and the first demonstration to our knowledge that C. intestinalis larvae exhibit sensory arousal and thigmotaxis, both of which can be modulated by the anxiotropic drug modafinil. Remarkably, by comparing the behaviour between animals assayed individually or in small groups, we found that crowd size influences larval behaviour. This study shows that C. intestinalis larval behaviour can be broken down to a set of stereotyped behaviours that are used to different extents in a context-dependent manner. Furthermore, it raises exciting possibilities such as mapping behaviour to specific neurons of this compact chordate nervous system and it paves the way for comparative quantitative behavioural studies as a means to reconstruct the evolution of behaviour, especially in the chordate lineage.