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A Longitudinal EEG Study of Infants at Risk for Autism: Network Capacity Building (Phase I)

Chapel Hill
State/Province Full: 
North Carolina
United States

This project aims to develop capacity for the collection of electroencephalographic (EEG) data in infants to complement an existing multi-site NIH Autism Center of Excellence (ACE) Network that is examining the longitudinal brain and behavior development of infants at high familial risk (HR) for autism. Data from this Network has already demonstrated widespread aberrant white matter tract development by 6 months of age in infants who are later classified as having autism (referred to as HR+) on the Autism Diagnostic Observation Schedule (ADOS). Dramatic changes in Diffusion Tensor Imaging (DTI) metrics in HR+ infants are noted throughout the 6 to 24 month interval in comparison to HR- infants (who are not classified as having ‘autism’ at 24 months). These brain changes occur at the time when the defining symptoms of autism appear to be unfolding in affected individuals. A prominent idea in the neurobiology literature is that autism is a disorder of neural synchrony, which has its origins in the functional connections within and among regions of the brain. Cortical connectivity can be assessed by measuring EEG coherence, which represents the consistency of the phase difference between two EEG signals when compared over time. EEG is a relatively inexpensive measure of brain characteristics and preliminary data suggest it may play a role in informing early prediction models. The research project aim to build the capacity for a large-scale, multi-site EEG study in infants in an 8 month demonstration project (Phase I), to be following by a Phase II application in which we will propose to add EEG measurements to all scheduled visits during the first year (3, 6, 9, 12 months) for the remaining length of the ACE network grant (years 2-5). With the eventual addition of EEG to the existing ACE network study, this application aims to: (a) evaluate EEG as a cost-effective marker that could be integrated into a prediction model for early detection of autism; and (b) more fully understand brain-behavior relationships during the critically important period preceding and coinciding with the emergence of the defining features of autism. The addition of EEG measurements leverages the tremendous resources already in place and planned over the next 5 years in this ACE network, builds on exciting new findings just emerging in the EEG field, and substantially expands the capacity of this project to provide insights into early prediction and brain-behavior relationships.