# Early Predictors of Late Talking: EEG Trajectories and Psychosocial Profiles

> **NIH NIH R21** · BOSTON CHILDREN'S HOSPITAL · 2024 · $489,500

## Abstract

PROJECT SUMMARY
Primary language impairment (PLI) affects 6-8% of children in the U.S. and the longer it goes undetected or
untreated, the greater the negative cascading effect on children’s functioning. Late talking (LT) in toddlerhood
(not meeting expressive language milestones between 18-24m) is an often-used marker of PLI risk
(approximately 40% of LT have persistent delays and go on to receive a diagnosis of PLI). To date, research
on LT has identified several factors that are associated with poorer language in the second year, including
demographic, family history, and child neurocognitive variables. However, there is little extant research
focusing on predictors of delayed expressive language in the first year, before the onset of major language
milestones. For the proposed secondary data analysis, we will analyze data from three existing longitudinal
studies conducted in our lab. These projects collectively provide a rich, densely sampled longitudinal dataset
from 2-36m encompassing a diverse sample of children (n>300) with distinct factors (e.g., socioeconomic,
familial history of language disorder or autism) that put them at elevated likelihood of developmental language
delay. From these projects, we have access to a suite of demographic and environmental data, language
measures, resting-state electroencephalogram (EEG) data, and videos of parent-child interactions (PCX). We
plan to process EEG data, code PCX videos for language and parenting behaviors, and harmonize variables
across studies to establish a unified dataset to retrospectively investigate the biopsychosocial factors that
contribute to late talking and predict risk for PLI in early infancy. We have identified approximately n=64 late
talkers in this sample and plan to select a group of matched peers who met typical expressive language
milestones at 24m. The specific aims of the project are: 1. To trace longitudinal trajectories of EEG features
from 2-24 months that are associated with late talking. 2. To identify features of the psychosocial environment
with the greatest explanatory power for individual differences in EEG developmental trajectories and language
outcomes at 24 and 36m. Our goal for this project is to establish a comprehensive foundation for identifying the
neural mechanisms and environmental factors associated with language development that may, in
combination, improve predictive models of delay and impairment. In addition, we plan to make this dataset
available on NIH and public databases to facilitate future secondary analyses that may uncover nuanced
patterns in the neurodevelopmental trajectories and psychosocial environments of late talkers.

## Key facts

- **NIH application ID:** 11030506
- **Project number:** 1R21DC022337-01
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Kali L Woodruff Carr
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $489,500
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11030506

## Citation

> US National Institutes of Health, RePORTER application 11030506, Early Predictors of Late Talking: EEG Trajectories and Psychosocial Profiles (1R21DC022337-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/11030506. Licensed CC0.

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