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.

Understanding how motor development interacts with emerging social and communication abilities during infancy is critical to establishing early ASD screening, diagnosis and intervention. In the current project, Gillian Forrester plans to employ high-frequency, longitudinal motor behavior sampling and cognitive evaluations to test for discernible differences in the developmental trajectories in infants at low and high risk for ASD.

Developing scalable objective measures is important for better understanding the strengths and weaknesses of people with autism and can help researchers to identify biological factors that influence cognitive, emotional and behavioral features associated with the condition. In the current project, Thomas Frazier plans to evaluate a recently developed online tool that uses webcam-collected measures of gaze and facial expressions that can be completed at home and can supplement traditional measures, providing a careful look at attention, language, and several other cognitive and emotional processes.

In the current project, Gabriela Rosenblau and colleagues aim to use various data-driven clustering approaches to identify subgroups of autistic individuals based on core dimensions of social learning strategies and rigidity/flexibility of self-preferences. The ultimate goal is to constrain the heterogeneity of ASD by identifying subtypes; such findings will inform future diagnoses, treatments and phenotype-to genotype associations.

Mutations in FMR1 cause fragile X syndrome (FXS). Loss of FMRP (the protein product of FMR1) leads to altered big-conductance voltage-activated potassium (BK) channel function, which can be measured in the eye using an electroretinogram (ERG). This non-invasive tool can thus provide a direct measure of visual/central nervous system (CNS) function in FXS. In the current project, Randi and Paul Hagerman aim to link ERG activity to molecular (peripheral FMRP levels) and neurobehavioral alterations across the spectrum of fragile X mutations. The findings are expected to lead to the identification of a biomarker that may be valuable for assessing targeted treatments for FXS.