Autism Spectrum Disorders (autism) has significant and long term effects on the lives of children and their families. Reliable diagnosis of autism is not possible until 2 years of age or later, and there are no currently available methods to screen for autism in early infancy. Preliminary evidence suggests that children with autism may be characterized by atypical features of cry and neurobehavior during early infancy. Previous research has identified atypicalities in vocal production in older children with autism, and unusual acoustic features of infant cry would be consistent with such findings from early childhood. One barrier to the acoustic analysis of newborn cry has been the lack of a computerized cry analysis system based on modern technology. Our lab has developed a new cry analysis system based on state-of-the-art signal processing that can be used to study acoustic characteristics of cry that could relate to risk for autism. Our group has also developed the NNNS, a widely used and well validated neurobehavioral exam for infants in the newborn period. Preliminary findings point to a set of cry and neurobehavioral features from these measures that was able to differentiate a sample of infants later diagnosed with autism from non-autistic infants included in a longitudinal, heterogeneous, child development study. Other preliminary findings support the hypothesis that infant cry and neurobehavior are affected in infants with later autism diagnoses. In the proposed project, we will utilize cry and neurobehavioral assessments that are being collected as part of unique pregnancy and birth cohort at a large regional hospital. It is anticipated that up to up to 5,000 infants will be enrolled and followed longitudinally. A 2-stage screening and evaluation process will identify children with autism by 24 to 36 months of age. Ongoing analyses will be conducted to identify neonatal cry and neurobehavioral characteristics that are associated with risk for autism using signal detection methods and complex conditional statistical models. This project is of high significance and has the potential to have substantial impact on public health by identifying potential indicators of risk for autism. The long term impact of this research would be on the development of early screening and early interventions for infants at risk for autism.
Autism affects up to 1.6% of the pediatric population, and can result in significant long term impairments. Autism is typically diagnosed after 2 to 3 years of age and there is a great need for improved early screening. This research is highly relevant to both early identification and intervention for children with autism. By utilizing state of the art methods for the assessment of infant cry and neurobehavior, this project seeks to develop a screening tool/process that can be used in the first year of life. This research work has the potential to open new doors for early intervention and will lead to an improved understanding of the early developmental course of autism.
