Graph theoretical approaches towards understanding differences in frontoparietal and default mode networks in Autism

摘要

Autism Spectrum Disorder (ASD) is a complex developmental disorder affecting 1 in 68 children in the United States. While the prevalence may be on the rise, we currently lack a firm understanding of the etiology of the disease, and diagnosis is made purely on behavioral observation and informant report. As one potential method for improving our understanding of ASD, the current study took a network-level approach by assessing the causal interactions among the frontoparietal and default mode networks using volumetric structural covariance of a large Autism dataset. Although preliminary, we report diffuse yet subtle changes throughout these networks when comparing age and sex matched controls to ASD patients.