NIH Reporter #5P50HD093074-10

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

This award funds a project at Duke University to develop a machine learning-based multimodal biomarker combining EEG and behavioral data to improve autism screening and clinical trial outcome measures in young children.

Description

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. This project aims to develop a multimodal biomarker combining EEG features and synchronized behavioral measures automatically coded via computer vision analysis, using machine learning techniques to better discriminate between autistic and neurotypical children than EEG alone.

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