| Literature DB >> 29201281 |
Brandalyn C Riedel1, Neda Jahanshad1, Paul M Thompson1.
Abstract
Autism Spectrum Disorder 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 method to improve our understanding of the disease, the current study took a systems-level approach by assessing the causal interactions among the frontoparietal and default mode networks using 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.Entities:
Keywords: Autism; MRI; Machine learning; Networks
Year: 2017 PMID: 29201281 PMCID: PMC5705086 DOI: 10.1109/ISBI.2017.7950560
Source DB: PubMed Journal: Proc IEEE Int Symp Biomed Imaging ISSN: 1945-7928