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Despite extensive progress in evidence-based behavioral interventions, most people affected by autism spectrum disorder (ASD) struggle with significant challenges and decreased quality of life during adulthood. For instance, in addition to broad impairments in adaptive functioning (AF), anxiety and depression affect many adults with autism. In addition, past research on ASD biomarkers has not proven predictive of these challenges.

In accordance with the National Institutes of Mental Health Research Domain Criteria, we will focus upon negative valence (NV) systems and adaptive functioning as outcome variables related to quality of life in autism – seeing this as a more promising approach than the identification of specific biomarkers for categorical classification of ASD.

Neuroimaging predictors of negative valence and adaptive function in autism have been understudied during the transition from adolescence into adulthood. We will obtain behavioral follow-up data from 80 adolescents (40 ASD, 40 TD) who participated in the mentor’s NIH-funded grants and provided excellent quality fMRI and DTI neuroimaging data in recent years (recruited from a dataset of >180 individuals).

Our objectives are to identify neural predictors of risk and resilience for NV and AF in ASD using a combination of hypothesis-driven approaches and innovative data-driven methods such a machine-learning. By identifying specific neural and behavioral mechanisms that conspire to shape outcome in ASD, we hope to inform interventions that may help adults who remain affected by autism achieve higher Quality of Life.