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

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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.