Autism spectrum disorder (ASD) is a heritable, heterogeneous neurodevelopmental disorder that isdiagnosed on the basis of impairments in social communication, as well as the presence of repetitive behaviorsand restricted interests. Although the disorder has a rising prevalence last estimated at 1 in 68 children, thereis still a paucity of broadly effective treatments. This is in part because there is still little known about theunderlying neurobiological basis of ASD. Furthermore, the considerable heterogeneity present in ASD makes itdifficult to draw conclusions about how the brains of individuals with ASD differ from those of typicallydeveloping (TD) persons. This project will take an interdisciplinary approach to improve our understanding ofthe mechanisms underlying ASD and to parse the heterogeneity present in ASD by analyzing how commongenetic variants may affect the brain. This project will use magnetic resonance imaging (MRI) to relate functional and structural connectivityto cumulative genetic risk in 80 children and adolescents with ASD (age 8-16) and 80 matched TD controls(age 8-16). Functional connectivity will be calculated from resting-state functional MRI scans by correlatingactivity time courses across different brain regions. Structural connectivity will be quantified through diffusiontensor imaging (DTI), which measures white matter microstructure based on the directional movement of watermolecules. Cumulative genetic risk will be based on participants' genotypes in several common geneticvariants across three genes which have been consistently linked to ASD (CNTNAP2, MET, OXTR). This studywill also examine whether genetic risk influences the brains of individuals with ASD differently than those of TDcontrols; for instance, youth with ASD may be more vulnerable to the effect of such risk variants. To betterunderstand the impact of genetic risk and the neural variation related to such vulnerability, genetic risk andassociated measures of brain connectivity will be related to individual differences in ASD symptomatology.Lastly, a classifier analysis based on brain connectivity measures will be used to determine how includinggenetic information may improve the ability to predict diagnosis from brain measures. By examining how cumulative genetic risk impacts brain connectivity, this research may suggest onepotential mechanism through which risk genes contribute to ASD and ultimately lead to more targetedtreatments. This approach will also begin to characterize individual variation within ASD, which may eventuallyaid with the development of more personalized interventions. Lastly, current attempts to diagnose ASD usingbrain-based measures do not yet have a high enough accuracy for clinical use. Accounting for some of theheterogeneity in ASD may improve prediction abilities and thereby aid with quantitative early diagnosis andsubsequent early interventions in younger populations.
Interagency Autism Coordinating Committee (IACC)
Autism Research Database (AFD)
