Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models.

Background: Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD.

Methods: We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait.

Results: Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy.

Limitations: Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability.

Conclusions: Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.