# Toward Scalable Biomarker-Based Prediction of ASD in High-Risk Infants

> **NIH NIH R01** · CHILDREN'S HOSPITAL OF LOS ANGELES · 2023 · $700,223

## Abstract

ABSTRACT
Despite tremendous effort by parents, researchers, clinicians, and educators, autism spectrum disorder (ASD)
continues to present a significant, lifelong challenge to most affected individuals and their families. Studies of
early development in infants at risk for ASD (such as infants with older siblings with ASD: “HR infant siblings” –
who have a ~20% chance of developing ASD) can identify early, presymptomatic predictors of ASD that can
then improve early screening and promote presymptomatic intervention. Behavioral studies of these HR infant
siblings have identified atypical behaviors in the second year of life in the social domain, with some evidence of
motor delays and differences in social attention within the first year. However, in part because of the limited
behavioral repertoire of infants, investigators have struggled to identify consistent first-year-of-life behaviors
that predict later ASD in a clinically actionable manner. We propose that the earliest measures of atypical
development should directly assay brain function. The Infant Brain Imaging Study (IBIS) Network has used
MRI methods to reveal functional and structural brain changes in the first year of life in HR infant siblings.
These brain changes are present prior to the emergence of behavioral features of ASD and accurately predict
ASD at 24 months of age (positive predictive value >= 80%). While scientifically promising, MRI's cost and
reduced availability limit its potential scalability for use in HR infants to use as a general population screener in
clinical settings. Electroencephalography (EEG) and eye tracking (ET) represent two scalable methods that
can measure brain function and can help to identify predictive biomarkers of ASD in early infancy. EEG and ET
are developmentally sensitive and accessible in community, real-world settings. In spring 2019, the Infant Brain
Imaging Study (IBIS) Network will launch a new study of 250 HR infants designed to replicate and extend its
predictive MRI findings. Here, we propose to add EEG and ET measures of brain function to this study, testing
HR infants from IBIS at 6 and 12 months of age, and assessing clinical outcomes at 24 months of age with
clinical outcomes assessed at 24 months of age. We will examine brain network function at rest, during low
level sensory processing, and during social information processing. Our hypothesis is that these scalable
EEG/ET biomarkers will (Aim 1) accurately identify infants with a later diagnosis of ASD and will (Aim 2) relate
to ASD-associated behaviors at 24 months of age. Capitalizing on this unprecedented opportunity to integrate
EEG/ET with neuroimaging in the same cohort of infants, in Aim 3 we also propose to explore the predictive
power of these combined measures, and the association between EEG/ET and MRI features. The overarching
goal of this proposal is to lower the age of detection of autism to early infancy, making intervention before the
emergence of ASD-specific beha...

## Key facts

- **NIH application ID:** 10691868
- **Project number:** 5R01MH121462-05
- **Recipient organization:** CHILDREN'S HOSPITAL OF LOS ANGELES
- **Principal Investigator:** Shafali Spurling Jeste
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $700,223
- **Award type:** 5
- **Project period:** 2021-07-16 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10691868

## Citation

> US National Institutes of Health, RePORTER application 10691868, Toward Scalable Biomarker-Based Prediction of ASD in High-Risk Infants (5R01MH121462-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10691868. Licensed CC0.

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