Discovering Eye Tracking Biomarkers of ASD with Diagnostic and Prognostic Power

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Children with ASD, on average, are not identified and treated until around age 4‐years, several years beyond the first signs and symptoms. Even when toddlers are diagnosed as ASD, parents and clinicians have little information to guide treatment decisions or predict the early course of that child's next few years. Research is needed to discover objective biomarkers that detect ASD at early ages with high accuracy, indicate disorder subtypes linked to definable clinical profiles, and convey prognostic information. The discovery of such biomarkers have been elusive, in part, because most studies utilize small sample sizes and fail to include non‐ASD delay contrast groups which are essential to enhance specificity. Our proposal plans to fill this gap by leveraging eye tracking technology to determine if visual fixation patterns in a large sample of very young (12‐36 months) ASD and non‐ASD toddlers (n=225) from the general population can be used to discover an eye tracking biomarker profile of ASD. The prognostic power of our eye tracking biomarkers will be determined by linking initial eye tracking scores to clinical profiles 1‐2 years later. Given the heterogeneity in ASD, it is unlikely that a single eye tracking test would detect all toddlers. Here we plan to remedy this by testing the utility of a battery of 9 developmentally appropriate, short (~1‐minute each), eye tracking tests that each tap into a foundational domain in ASD symptoms including: visual social attention, gaze shifting, and auditory social attention. Our tests will objectively quantify key metrics such as overall fixation levels within social versus non‐social images, the frequency of gaze alterations during joint attention and gaze following tests and, using unique gaze contingent technology, the degree to which a toddler prefers to listen to prosodic, emotionally valent, motherese speech. Our preliminary findings suggest that several of our proposed eye tracking tests have extremely high diagnostic accuracy. Findings, however, are sometimes not replicated in science, and we proactively address this by proposing concurrent, independent and exact (equipment, software, paradigms, procedures) replication testing of each of our eye tracking tests within an independent cohort of toddlers at U. Washington (n=90 toddlers). Thus, in AIM 1 we will discover an eye tracking biomarker that detects ASD at early ages (12‐36 months) with high accuracy using artificial neural networks at UCSD, and then we will independently test replication of its performance at U Washington. To enhance interpretability, our approach will incorporate patterns of metrics from each eye tracking test to produce an Autism Risk Score (ARS) scaled from 0‐100 that represents the level of ASD risk for a toddler. In AIM 2, using a rich clinical battery that captures each toddler's social, language, cognitive and symptom severity profile derived from a combination of standardized, parent report, and free‐play testing, we will identify clinically meaningful eye‐tracking based subtypes of ASD using unbiased network clustering approaches. In AIM 3 we will examine the prognostic utility of our eye tracking test battery by identifying the degree to which eye tracking‐based profiles at ages 12‐36 months can predict social, language, cognitive and attention abilities 1‐2 years later.