Development and Validation of Objective and Quantitative Eye Tracking-Based Measures of Autism Risk and Symptom Levels. Journal of the American Academy of Child and Adolescent Psychiatry Frazier, T. W., Klingemier, E. W., Parikh, S., Speer, L., Strauss, M. S., Eng, C., Hardan, A. Y., Youngstrom, E. A. 2018; 57 (11): 858–66


OBJECTIVE: The primary aim of this study was to develop and validate eye tracking-based measures for estimating autism spectrum disorder (ASD) risk and quantifying autism symptom levels.METHOD: Eye tracking data were collected from youth during an initial evaluation visit, with administrators blinded to all clinical information. Consensus diagnoses were given by the multidisciplinary team. Participants viewed a 5-minute video that included 44 dynamic stimuli from 7 distinct paradigms while gaze was recorded. Gaze metrics were computed for temporally defined regions of interest. Autism risk and symptom indices aggregated gaze measures showing significant bivariate relationships with ASD diagnosis and Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) symptom severity levels in a training sample (75%, n= 150). Receiver operating characteristic curve analysis and nonparametric correlations were used to cross-validate findings in a test sample (25%; n= 51).RESULTS: Most children (n= 201, 92%) completed a valid eye tracking assessment (ages 1.6-17.6; 80% male; ASD n= 91, non-ASD n= 110). In the test subsample, the autism risk index had high accuracy for ASD diagnosis (area under the curve [AUC]= 0.86, 95% CI=0.75-0.95), whereas the autism symptom index was strongly associated with ADOS-2 total severity scores (r= 0.41, p< .001). Validity was not substantively attenuated after adjustment for language, nonverbal cognitive ability, or other psychopathology symptoms (r= 0.40-0.67, p > .001).CONCLUSION: Eye tracking measures appear to be useful quantitative, objective measures of ASD risk and autism symptom levels. If independently replicated and scaled for clinical use, eye tracking-based measures could be used to inform clinical judgment regarding ASD identification and to track autism symptom levels.

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