Motor impairments are prevalent in individuals with ASD without a known genetic syndrome (nonsyndromic ASD) and with a known genetic syndrome (syndromic ASD) and can negatively impact environmental exploration, social engagement and language development. In the current project, Rujuta Wilson aims to use quantitative measurement to develop improved methods to identify motor impairments in individuals with ASD and chromatin modifying disorders that can serve as targets for treatment and be implemented into clinical trials.

There is a considerable shortage of psychometrically sound, sensitive and objective measures of early social communication behaviors associated with autism. In the current project, Rachel Reetzke and Rebecca Landa aim to fill this gap and develop accessible mobile technology to improve the objectivity, granularity and scalability of remote automated measurement of core social communication behaviors in young children on the autism spectrum.

Personalized medicine in autism requires stratification biomarkers because of substantial biological heterogeneity. The identification of such biomarkers requires direct, sensitive and individualizable measures of variation in key neurobiological systems. Emily Jones and Sarah Lippé plan to capitalize on new developments in artificial intelligence that allow individualized stratification biomarkers across a broad task and analytic space to be identified by harnessing the power of large-scale datasets but without exhaustively accessing data.