Autism spectrum disorders (ASDs) are characterized by vast neural and behavioral phenotypic heterogeneity.However, little is yet known about how variability in these processes impacts functional outcomes; this, in turn,hampers development of highly effective and targeted interventions. As children encounter increasing socialdemands through school, these challenges become especially urgent. The current project aims to addressthese concerns through improved multi-level characterization of the processes contributing to “real world”social deficits in ASD. The principal investigator has previously shown that simultaneous study of socially-sensitive EEG/ERP and behaviorally-indexed cognitive variables can predict more than 50% of variance in keysocial outcomes (e.g. diagnostic severity). The current proposal extends this work by focusing on predictors of“real world” social functioning (e.g. friendship-making and prosocial behavior) that have been characterized intypically-developing (TD) children (e.g. emotion recognition, Theory of Mind), but whose relations have neverbeen firmly established in ASD. These variables will be examined at both electrophysiological (perceptual andearly cognitive) and behavioral levels. 160 youth with ASD and 100 TD youth, ages 11 – 17, will be assessed.They will complete lab-based batteries of EEG and behavioral tasks; parent and teacher report of socialfunctioning, sociometrics in school, and observed prosocial behavior data will be collected as “real world”outcome indices. Predictors and “real world” outcomes will be correlated to see whether these long-presumed,yet rarely-tested relations are indeed evident. Then, advanced predictive modeling will be used to assessspecific, optimal patterns of factors that best characterize the variance in social outcomes in ASD. Thesepatterns will be cross-validated to maximize generalizability. Third, subgroups of individuals with ASD for whomthese patterns are especially salient will be identified. This innovative approach will test bedrock assumptionsof the field (i.e. the importance of neural and behavioral measures in functional outcomes) to uncover basicbiopsychosocial variables underlying social deficits in ASD. It will also use contemporary statistical modelingapproaches to – for the first time – identify much more precisely how putative predictors combine to affectsocial functioning, creating a more realistic and ecologically-valid picture of how internal factors “scale up” toaffect the social world, and deriving more useful and specific treatment targets . Finally, this approach will helpachieve the goal of identifying functionally meaningful subgroups within ASD, which will yield profound insightsto guide investigations into discrete developmental processes by which social deficits (and capabilities) arise inASD, and to whom precision intervention approaches may be matched. The proposed research addressesObjective 1 of the NIMH Strategic Plan by integrating behavioral and biological markers and examining howthe complex interplay of these process – specifically behavioral and EEG indices of social cognition andperception – translates into “real world” impairment in ASD.
Interagency Autism Coordinating Committee (IACC)
Autism Research Database (AFD)
