# Optimizing Prediction of Social Deficits in Autism Spectrum Disorders

> **NIH NIH R01** · STATE UNIVERSITY NEW YORK STONY BROOK · 2020 · $465,219

## Abstract

Project Summary
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 social
demands through school, these challenges become especially urgent. The current project aims to address
these 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 key
social 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 in
typically-developing (TD) children (e.g. emotion recognition, Theory of Mind), but whose relations have never
been firmly established in ASD. These variables will be examined at both electrophysiological (perceptual and
early 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 social
functioning, 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 assess
specific, optimal patterns of factors that best characterize the variance in social outcomes in ASD. These
patterns will be cross-validated to maximize generalizability. Third, subgroups of individuals with ASD for whom
these patterns are especially salient will be identified. This innovative approach will test bedrock assumptions
of the field (i.e. the importance of neural and behavioral measures in functional outcomes) to uncover basic
biopsychosocial variables underlying social deficits in ASD. It will also use contemporary statistical modeling
approaches to – for the first time – identify much more precisely how putative predictors combine to affect
social functioning, creating a more realistic and ecologically-valid picture of how internal factors “scale up” to
affect the social world, and deriving more useful and specific treatment targets . Finally, this approach will help
achieve the goal of identifying functionally meaningful subgroups within ASD, which will yield profound insights
to guide investigations into discrete developmental processes by which social deficits (and capabilities) arise in
ASD, and to whom precision intervention approaches may be matched. The proposed research addresses
Objective 1 of the NIMH Stra...

## Key facts

- **NIH application ID:** 9919634
- **Project number:** 5R01MH110585-05
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Matthew Daniel Lerner
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $465,219
- **Award type:** 5
- **Project period:** 2016-07-18 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9919634

## Citation

> US National Institutes of Health, RePORTER application 9919634, Optimizing Prediction of Social Deficits in Autism Spectrum Disorders (5R01MH110585-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9919634. Licensed CC0.

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