Synchronized EEG and behavior markers for young children with autism
A machine learning computational approach for developing synchronized EEG and behavior biomarkers in young autistic children
It uses synchronized brainwave (EEG) and behavior data like eye-tracking plus machine learning to create objective markers that could help identify and track autism in young children.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Duke University NIH-funded |
| Lab location | 1 site (Durham, United States) |
| Project ID | NIH-11178416 on NIH RePORTER |
What this research studies
If your child joins, technicians will record EEG (brainwave) and eye-tracking while your child watches simple tasks or stimuli and behaves naturally. The project combines those synchronized recordings with computer vision and machine-learning analysis to find patterns that separate autistic from neurotypical responses. Researchers hope to develop a multimodal biomarker that is more reliable than caregiver reports alone and that could be used in future clinical trials or screening. Most visits are conducted at Duke or affiliated sites and involve noninvasive sensors and short testing sessions.
Who could benefit from this research
Good fit: Ideal candidates are young children (infancy through early school age, roughly 0–11 years) who have autism or are being evaluated for autism and who can tolerate EEG and eye-tracking sessions.
Not a fit: Older adolescents, adults, people outside the target age range, or children unable to tolerate EEG/eye-tracking procedures are unlikely to benefit directly from participation.
Why it matters
Potential benefit: If successful, this could give clinicians a more objective way to detect and monitor autism and help match children to the right treatments or clinical trials.
How similar studies have performed: Previous research has shown EEG and eye-tracking differences in autistic versus neurotypical children, but combining synchronized multimodal recordings with machine learning is relatively new.
Where this research is happening
Durham, United States
- Duke University — Durham, United States (Active)
Researchers
- Principal investigator: Carpenter, Kimberly L H — Duke University
- Study coordinator: Carpenter, Kimberly L H
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.