Early Detection of Autism with Automated Social Cognition & Imitation Screener (AScIS)

Funding Source: UL1TR001876 from the NIH National Center for Advancing Translational Sciences, CTSI-CN Discovery Pilot Award Program
Principal Investigator: Francys Subiaul, Darcy Mahoney (Co-PIs)
We propose adapting existing technology to develop a novel automated social cognition and imitation screener (AScIS) that identifies robust developmental benchmarks in infants (6-12 months) associated with a heightened risk of ASD. Most experts agree that early detection, often leads to early intervention {Landa, 2018 #5147}. And early intervention is one of the most important factors predicting long-term success for children with autism spectrum disorder or ASD {Landa, 2018 #5147}. However, there are various barriers to early identification in ASD.  Infant screeners must address the problem that infants have significant communicative, socioemotional, motor and sensory limitations. In testing environments, they are easily distracted and fatigued. Together these features result in substantial variation between and within individuals {Karmiloff-Smith, 2012 #6224}. Such limitations, coupled with the fact that different neurodevelopmental disorders (AD/HD, ASD, SCD, DD, ID, etc.) are likely to share common phenotypic expressions {Doernberg, 2016 #6454}, means that a differential diagnosis in infancy must--ideally--include both passive and active measures at multiple levels of analysis {Krueger, 2018 #6366}.

Aims 1-2 of this proposal addresses most of these challenges by using a personalized automated system--the Automated Social Cognition & Imitation Screener (AScIS)--for individual infants and that includes more active responses across different social constructs.