Though the foundational nature of social skill disruptions in autism spectrum disorder (ASD) is widely accepted, no studies have investigated infant-caregiver interactions in dyads with infants with ASD in early infancy. Using advances in computer vision analysis and deep learning for dynamic behavior prediction, Sarah Shultz and Gordon Berman aim to identify behaviors produced during infant-caregiver dyadic interactions, and the extent to which caregiver and infant behaviors predict each other. This project will identify objective markers of interactional dynamics that signify risk for social disability and targets for when and how to optimize early social interventions.

Maja Bucan and her collaborator Edward S. Brodkin will combine deep phenotyping with genetic analysis to better understand sleep traits in autism spectrum disorder (ASD). They plan to recontact families participating in SPARK and perform an actimetry-based study of sleep traits. Families with both idiopathic ASD and families of SPARK participants with deleterious variants in known ASD risk genes and synaptic genes linked to sleep disruptions will be prioritized. Findings from this study are expected to improve our understanding of genetic and clinical contributors to sleep in ASD.

Atypical perceptual integration, in which sensory information is combined over time, is widely observed in individuals with ASD. In this project, Benjamin Scott aims to use online computer games to measure meaningful differences in perceptual integration across a range of children and adolescents with and without ASD. This approach will enable rigorous, quantitative assessment of perception across the population and will provide important insights into cognitive mechanisms that underly ASD.