# A machine learning computational approach for developing synchronized EEG and behavior biomarkers in young autistic children

> **NIH NIH P50** · DUKE UNIVERSITY · 2024 · $718,192

## Abstract

ABSTRACT – Project 3
The overall goal of the Duke Autism Center of Excellence is to use a translational digital health and computational
approach to address the critical need for more effective autism screening tools, objective outcome measures,
and brain-based biomarkers that can be used in clinical trials with young autistic children. Despite significant
advances in understanding the biological basis of autism, clinical trials continue to rely on subjective clinical
observation and caregiver report measures. Objective, biologically based biomarkers are needed for use in
clinical trials that can parse heterogeneity, assess target engagement, and monitor outcomes. Autism biomarker
studies have utilized electroencephalography (EEG) and eye-tracking measures, which have found differences
between autistic and neurotypical individuals in neural and attentional processing of social stimuli. However, to
date, the majority of autism biomarker studies have used independent experimental paradigms and separate
analyses of EEG and gaze. Technical and computational advances, including machine learning and computer
vision analysis, now allow for synchronized measurement and analysis of EEG and behavior, including eye-
tracking, each of which provides distinct sources of information that can be integrated to improve biomarker
performance. Project 3 will use an innovative machine learning computational method to develop a multimodal
biomarker that combines features of EEG activity and synchronized measures of children’s behavior (e.g., social
attention) automatically coded via computer vision analysis. We will test the hypothesis that a multimodal
biomarker will show enhanced discrimination between autistic and neurotypical children compared to biomarkers
based on EEG alone. Standard and novel methods will be used to combine synchronized behavior (digital
phenotypes) and EEG features, with a focus on neural connectivity measured via traditional methods
(coherence, phase-lag index) and new network analysis methods (discriminative cross-spectral factor analysis)
developed by our team. This multimodal approach will be evaluated in 3–6-year-old autistic children without
intellectual disability (ID), age- and sex-matched neurotypical children, and autistic children with ID (IQ <= 70).
Multimodal biomarkers will be compared to three commonly used EEG biomarkers. Our goal is to develop robust,
brain-based biomarkers that can be used in clinical trials to evaluate early interventions for young autistic children
designed to improve outcomes and quality of life.

## Key facts

- **NIH application ID:** 10909166
- **Project number:** 5P50HD093074-08
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Kimberly L H Carpenter
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $718,192
- **Award type:** 5
- **Project period:** 2017-09-07 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10909166

## Citation

> US National Institutes of Health, RePORTER application 10909166, A machine learning computational approach for developing synchronized EEG and behavior biomarkers in young autistic children (5P50HD093074-08). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10909166. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*