Autism, a neurodevelopmental disorder characterized by impairment in communication, social impairment andrepetitive and stereotyped behaviors, is a life-long condition with an undetermined etiology and currently is notcurable. While autism has a heterogeneous and complex genetic underpinning there is evidence thatenvironmental factors also play a role. Despite the variety of factors that can lead to autism, the phenotype isremarkable uniform, raising the possibility of a “final common pathway” in this disorder. Functional MRI andelectrophysiology studies suggest this “final common pathway” may be through aberrant neural connectivityduring development. In this proposal we wish to evaluate previously recorded EEGs obtained from a largecohort of children with autism, developmental delay and neurotypically developing children extensivelyevaluated at the NIMH, to determine if there are neural networks characteristics that distinguish children withclassic autism from typically developing children and children with developmental disabilities without autism.Ascertaining such changes may provide insight into the pathophysiological mechanisms responsible for thesymptoms in autism. In specific aim 1 we will determine whether children with classic autism have neuralnetworks which distinguish them from typically developing children and children with neurodevelopmentaldisorders but without autism. We will examine awake and sleep recordings for coherence and developfunctional connectivity maps using Pearson correlations and partial correlations in all three groups. In specificaim 2 we will determine whether coherence and connectivity maps change over time in children with autismand whether such changes correlate with outcome and in specific aim 3 we will determine whether epileptiformactivity is more common in the EEGs of the children with autism than in typically developing children andchildren with neurodevelopmental disorders but without autism and whether such activity is related to outcome.By taking advantage of this rich data set we wish to better characterize neural connectivity in autism usingpowerful electrophysiological techniques. Understanding how the brain of a child with autism is “wired” willplay an important role in developing therapeutic interventions.
Interagency Autism Coordinating Committee (IACC)
Autism Research Database (AFD)
