# MRI Based Presymptomatic Prediction of ASD

> **NIH NIH R01** · WASHINGTON UNIVERSITY · 2024 · $687,270

## Abstract

ABSTRACT
(verbatim, original text)
The overarching goal of this proposal is to lower the age of detection in autism to early infancy, making
presymptomatic (i.e., before the emergence of ASD-specific behavioral features) intervention feasible. Infants
with an older autistic sibling have up to a 20% risk of developing autism spectrum disorder (ASD). Prospective
high familial risk (HR) infant sibling studies have shown that the defining behaviors of ASD do not emerge until
the latter part of the first year and into the second year of life. Therefore, the vast majority of affected children
are diagnosed after age 2. No behavioral markers in the first year of life have yet been identified that can predict
later ASD diagnosis with sufficient accuracy (i.e., positive predictive value: PPV ≥ 80%) to justify presymptomatic
intervention. We recently published two independent approaches that use brain imaging in the first year of life to
predict which HR infants will be diagnosed with ASD at 2 years of age. Specifically, structural MRI (sMRI) at 6
and 12 months of age, and resting state functional connectivity MRI (fcMRI) at 6 months of age independently
predicted later ASD diagnosis in HR infants with over 80% PPV. Our preliminary data show that a third MRI
approach, using regions of CSF volume and cortical shape at 6 months of age can also accurately predict later
ASD diagnosis. If we replicate and extend these findings, we will be able to identify individual infants at “ultra-
high risk” (80% chance) of developing ASD, rather than being limited to group-level risk (20% chance), where
we do not know who will later be affected. This R01 application aims to move our initial findings toward a clinical
test for ASD in HR infants in the first year of life. Aim 1 will validate our previous findings in a new, independent
sample of HR infants, extend our methods to a new MRI platform, and examine whether fcMRI and/or sMRI, with
and without behavioral information, during the presymptomatic period in infancy, accurately predict ASD
diagnosis at 24 months of age. Aim 2 will move beyond predicting categorical diagnosis to predicting
dimensional, clinically-relevant characteristics for individual infants. Specific dimensional targets include
expressive language level, social responsiveness, initiation of joint attention, and repetitive behavior. Validating
and extending our findings on presymptomatic prediction of ASD in a new sample, on a different MRI scanner,
and with dimensional developmental characteristics are critical next steps for moving the field forward toward (a)
the development of a clinically-useful, presymptomatic test for identifying ultra-high risk infants who would benefit
from very early intervention in infancy, (b) efficient studies of presymptomatic intervention strategies in individuals
at ultra-high risk, and (c) the development of future presymptomatic tests for use in the general (not just HR)
population.

## Key facts

- **NIH application ID:** 11057230
- **Project number:** 3R01MH118362-05S1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Joseph Piven
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $687,270
- **Award type:** 3
- **Project period:** 2024-03-16 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11057230

## Citation

> US National Institutes of Health, RePORTER application 11057230, MRI Based Presymptomatic Prediction of ASD (3R01MH118362-05S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/11057230. Licensed CC0.

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