Despite the tremendous variability observed across individuals diagnosed with Autism Spectrum Disorder (ASD), most research to date has treated ASD as a unitary condition, comparing individuals with ASD to neurotypical controls. This approach has hindered our progress in unraveling the neurobiological mechanisms that give rise to ASD symptomatology and it also undermines the potential of translational research to contribute to `precision medicine' in ASD. In this project, we take a critical step toward dissecting the significant heterogeneity observed in ASD by combining state-of-the-art imaging methods, novel approaches to account for genetic susceptibility, and a deep phenotypic characterization of a large and unique sample of males and females with ASD that we curated as part of our recently renewed ACE Network (MH100028). Capitalizing on the comprehensive assays already collected in this cohort (i.e., phenotyping, genotyping, MRI, EEG) and our involvement in the Human Connectome Project - Development (MH109589), here we will collect and analyze a rich dataset of brain-based measures of unparalleled resolution and quality in order to characterize individual differences in brain network properties and examine how these relate to a vast phenotypic battery of measures tapping into key domains of interest. Using resting-state fMRI and innovative fMRI activation tasks as neural assays of social and sensory responsivity, we will examine how functional connectivity and brain responses in associated neural circuits co-vary within and between individuals in order to determine how atypical reactivity to sensory stimuli impacts neural processing of socially relevant stimuli, and assess how distinct neural endophenotypes of social and sensory responsivity relate to altered functional connectivity and behavioral phenotypes. Building upon our prior imaging-genetics work, we will also examine how polygenic risk, and risk genetic variants on ASD-associated polymorphisms, influence brain function, connectivity, as well as core ASD symptoms. Our overarching hypothesis is that both distinct and shared neuroendophenotypes will be identified across our sample based on different brain function and connectivity metrics and that these will map onto varying dimensions of social and sensory atypicalities manifested at the neural and behavioral level. We further expect that higher polygenic risk will predict increasingly aberrant patterns of brain activity, connectivity, and overall symptom severity, whereas genetic variance on ASD-associated polymorphisms will selectively modulate brain function and connectivity in brain circuits where these ASD risk genes are expressed. By employing (a) cutting-edge imaging methods to examine brain function and connectivity, (b) innovative paradigms to relate social attention and motivation to sensory processing atypicalities, (c) novel approaches to integrate genetic risk with neural and behavioral phenotypes, and (d) sophisticated data-analytic strategies to sensibly stratify our ASD sample, this research will further our understanding of the neural mechanisms underlying heterogeneity in ASD and ultimately inform more personalized and efficacious interventions.
The proposed research will take a critical first step toward parsing the significant heterogeneity observed in Autism Spectrum Disorder (ASD) by combining state-of-the-art imaging and data-analytic methods to examine brain function, novel approaches to account for genetic susceptibility, and an unprecedentedly deep phenotypic characterization of a large, gender-balanced sample of youth with ASD. Capitalizing on the availability of EEG data previously collected on this sample as part of a different project, we will also explore whether any ASD subgroups identified by combining imaging, genetic, and phenotypic measures may be linked to distinct EEG profiles in order to develop easily deployable biomarkers of ASD that are predictive of meaningful differences in symptomatology and developmental outcomes. A better understanding of the neural mechanisms underlying heterogeneity in ASD will ultimately contribute to the development of more personalized and efficacious interventions.
