In this study, Elena Tenenbaum, Shafali Spurling Jeste and a team of researchers and clinicians, plan to capitalize on recent advances in remote administration of cognitive development tasks and computer vision software to pilot the Remote Infant Studies of Early Learning (RISE) Battery. This project represents a critical first step to increased understanding of early cognitive development in autism and has the potential to elucidate the foundational underpinnings of clinical heterogeneity in autism. This in turn will inform more targeted, developmentally relevant early intervention strategies for infants with atypical developmental trajectories.

Despite significant advances in modern communication technologies, there exists a huge gap in their application to help individuals who are minimally verbal with autism spectrum disorder speak. In the current project, Christopher McDougle, Pattie Maes and Thomas Quatieri plan to draw on advanced signal sensing and analysis technologies to characterize both vocal and behavioral means of expression for use in the development of clinical assessment tools and personalized treatments.

Understanding brain functional atypicalities in ASD and knowing when they emerge is important in planning personalized early interventions. In this project, Margot Taylor and her collaborators will study brain function (using new technology: optically pumped magnetometry magnetoencephalography) in children one to four years old, some of whom who will be diagnosed with ASD and some who will not, providing unique data on neural markers of risk and resilience and their relations to emerging ASD features and behavior.

A nascent literature documents abnormal sleep rhythms in people with ASD, which is consistent with other evidence of altered thalamocortical communication in ASD. Because large studies are needed to characterize abnormal sleep rhythms and to understand its clinical relevance, Dara Manoach and her team plan to validate and use a wearable EEG device to measure sleep at home in children with ASD and typically developing peers. This project will lay the groundwork for large-scale studies to illuminate the genetics and mechanisms of abnormal sleep rhythms in ASD and identify novel treatment targets.

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.

In the current project, Sophie Molholm and colleagues plan to test the potential of neuro-oscillations, measured under a range of different task conditions, to serve as a sensitive and reliable measure of alterations in brain function in autism spectrum disorder (ASD). The approach will involve collecting scalp-recordings of EEG, as well as clinical and cognitive data (from children with ASD, unaffected siblings of children with ASD and age and sex matched typically developing children). Machine learning analyses will be used to assess which combinations of neural measures provide the most robust and reliable biomarkers of clinical phenotype.