# Discovering Neural Biomarkers of Language and Social Development in ASD Toddlers

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $586,836

## Abstract

Project Summary/Abstract
Despite the annual $268 billion cost of ASD in the U.S. and the tens of millions spent annually on research,
“precision” medicine does not exist in any meaningful way for ASD infants and toddlers. The heterogeneity
of early neural and behavioral developmental trajectories in ASD has stymied the search for explanations,
and the identification of clinically useful biomarkers of prognosis as well as the discovery of biotargets that
could be used to develop maximally effective treatments. In our proposed studies, 175 ASD, typical, language
delayed (LD) and global developmental delayed (GDD) toddlers will participate in a series of language-
relevant (nursery rhymes vs music) and social emotion fMRI paradigms (own mother’s voice vs stranger’s)
as well as resting state connectivity paradigms to begin to address this major gap in the field. Toddlers will
be recruited using our novel general population based screening approach that provides unique and
complementary data to those from baby sibling studies. In order to generate a rich clinical profile of each
toddler, multiple language and social measures will be taken, including CELF-R, Mullen and Vineland. In
order to examine change and leverage powerful longitudinal modeling approaches, toddlers will be clinically
assessed and imaged at both 1-2 and 3-4 years. State-of-the-field MEMB and ME-ICA denoising approaches
will be utilized that yield highly reliable high signal-to-noise functional imaging that outperforms previous fMRI
approaches and enhances effect size estimates and statistical power; this greatly benefits robustness in our
analyses, reliability, split sample feasibility, and exploratory prognostic biomarker modeling. Multiple analytic
methods (e.g., PLS, seed-PLS, ICA, spectral DCM, PPI) will be applied to identify brain-language and brain-
social emotion relationships; model neural and clinical trajectories from 1-2 to 3-4 years; reveal language-
and social emotion-relevant fMRI activation and connectivity patterns at 1-2 years that are predictive of
language and social outcomes; discover underlying neural-clinical subtypes; model continuous variation in
still other neural measures that predict continuous language and social measures; identify fMRI-MRI
relationships; define how language and social emotion neural deficits tap into shared neural network
resources in early development; and examine similarities and differences in brain-behavioral relationships
across multiple groups which then allows for sensitive tests of whether brain-behavioral patterns are common
across diagnostic boundaries (e.g., LD, GDD and ASD poor language toddlers in an RDoC fashion) or
specific to a subgroup of individuals. Our studies will identify clinically meaningful early-age neural biomarkers
that predict which ASD children will go on to have good language outcomes and which poor ones, and others
that predict social outcome. Compelling ASD language and social biotargets will be found th...

## Key facts

- **NIH application ID:** 9994972
- **Project number:** 5R01DC016385-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** ERIC COURCHESNE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $586,836
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994972

## Citation

> US National Institutes of Health, RePORTER application 9994972, Discovering Neural Biomarkers of Language and Social Development in ASD Toddlers (5R01DC016385-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9994972. Licensed CC0.

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