# Discovering novel predictors of minimally verbal outcomes in autism through computational modeling

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2023 · $532,265

## Abstract

The proposed project will create novel multidimensional models to characterize the prelinguistic developmental
pathways leading to verbal and minimally verbal (MV) outcomes in children with autism spectrum disorder
(ASD). Children with ASD experience significant delays in the development of prelinguistic communication
(PLC) skills that are important indicators of progress along a path towards language. PLC skills include
vocalizations, gestures, joint attention, and comprehension. As many as 30% of children with ASD remain MV,
producing very few if any spoken words by the time they reach kindergarten. Significant gaps remain in our
knowledge of early risk factors that predispose children to remain MV. In particular, further research is needed
to identify specific inflection points that are indicative of risk for not progressing to spoken language. The
proposed innovative modeling framework will assess whether transitions between specific prelinguistic stages
and the timing of these transitions represent risk for MV outcomes. Crucially, the project will develop a novel
method for quantifying a child’s risk of a MV outcome at age 5 given their PLC progressions at earlier points in
development. Such information could guide and focus early intervention efforts, such as intensifying therapies
at certain points in development and/or deciding when to introduce augmentative or alternative communication.
The key innovation in this proposal is leveraging Continuous-Time Hidden Markov Models to delineate
progressions of PLC across dimensions of attention, vocalizations, gestures, and comprehension from 18-36
months in children with ASD, and to identify unique multidimensional trajectories that predict which children
remain MV at age 5. The proposal will test the hypothesis that children who are transitioning between PLC
stages more slowly or following atypical patterns of progression are at higher risk for MV outcomes. Aim 1 will
develop and validate state-based models of development of attention, vocalizations, gestures, and
comprehension in a well-characterized sample of typically developing children and separately, children with
ASD (Activity 1a), and then construct a multidimensional model that unifies the individual models to
simultaneously examine progressions across the four dimensions (Activity 1b). Models will be first validated on
a sample of 50 typically developing children observed every 3 months from 6 to 18 months of age, and then
separately validated on a sample of 100 children with ASD observed every 3 months from diagnosis at 18-24
through 36 months of age. Aim 2 will utilize the ASD model from 1b to identify predictors for MV status at 5
years in children with ASD (Activity 2a) and apply a survival analysis approach to turn these predictors into a
quantifiable risk score for MV outcome (Activity 2b).
The models along with the underlying training data will be released to the research community, enabling ASD
and developmental researchers wit...

## Key facts

- **NIH application ID:** 10676845
- **Project number:** 5R01DC020048-02
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** NANCY CAROLINE BRADY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $532,265
- **Award type:** 5
- **Project period:** 2022-08-05 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10676845

## Citation

> US National Institutes of Health, RePORTER application 10676845, Discovering novel predictors of minimally verbal outcomes in autism through computational modeling (5R01DC020048-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10676845. Licensed CC0.

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