Development and Validation of Objective, Eye Tracking-Based Risk and Symptom Measures for Autism

Poster Presentation
Thursday, May 10, 2018: 5:30 PM-7:00 PM
Hall Grote Zaal (de Doelen ICC Rotterdam)
T. W. Frazier1,2, E. W. Klingemier3, A. Y. Hardan4, C. Eng5, M. S. Strauss6, L. Speer2, S. Parikh2 and E. Youngstrom7, (1)Autism Speaks, New York, NY, (2)Cleveland Clinic, Cleveland, OH, (3)Cleveland Clinic Center for Autism, Cleveland, OH, (4)Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, (5)Genomic Medicine, Cleveland Clinic, Cleveland, OH, (6)Psychology, University of Pittsburgh, Pittsburgh, PA, (7)University of North Carolina at Chapel Hill, Chapel Hill, NC
Background: At present, there are no validated, objective, quantitative, scalable assessment tools for autism spectrum disorder (ASD).

Objectives: To develop and validate eye tracking-based measures for estimating ASD risk and quantifying autism symptom levels.

Methods: Patients were recruited into this cross-sectional, case-control study from a tertiary care, multi-disciplinary, autism diagnostic evaluation clinic. Eye tracking data were collected during a single evaluation visit with administrators blinded to all clinical information. Participants included 201 children referred for evaluation of possible developmental disorder who completed a valid eye tracking assessment (ages 1.6-17.6; 80% male; ASD n=91, non-ASD n=110). Consensus clinical diagnoses were given by the multidisciplinary team based on Autism Diagnostic Observation Schedule-2 (ADOS-2) results, developmental history, physician evaluation, and additional clinical measures. Participants viewed a 5-minute video that included 44 dynamic stimuli from 7 distinct paradigms while gaze was recorded. Five gaze metrics (glances, fixation count, fixation duration percent, first fixation duration, and average fixation duration) were computed for temporally-defined regions-of-interest within each stimulus. Autism risk and symptom indices were created by aggregating across gaze measures showing significant bivariate relationships with ASD diagnosis and ADOS-2 symptom severity levels in a training sample (75%, n=150). Receiver operating characteristic curve analysis and non-parametric correlations were used to cross-validate identification of ASD diagnosis and autism symptom severity in a test sample (25%; n=51).

Results: The autism risk index had excellent accuracy for identifying ASD diagnosis in the training sample (AUC=.92, 95%CI=.88-.96) and maintained high accuracy in the test sub-sample (AUC=.86, 95%CIs=.75-.95; Figure 1). Autism symptom indices were strongly associated with ADOS-2 total, social affect, and restricted/repetitive behavior severity scores (smallest r=.26, p=.040). The autism risk index had high internal consistency reliability (α=.92) and wide quantitative range, with 95% of non-ASD cases falling from z=-2.3 to 1.6 and 95% of ASD cases falling from z=-0.1 to 5.0. The majority of missed cases (68%) fell within +/-0.75 SD of the optimal cut point z=0.74. Autism symptom indices had high internal consistency reliability (α>=.93). Validity of autism risk and symptom indices was not substantively attenuated after adjustment for language, non-verbal cognitive ability, or other psychopathology symptoms (r=.40-.67, p>.001), indicating that the eye-tracking based measures were highly specific to autism.

Conclusions: Eye tracking measures may be useful quantitative, objective measures of ASD risk and autism symptom levels. Future studies with large, multi-site samples are needed to replicate these findings and determine their generalizability, including resistance to sampling differences and minor procedural variations. If replicated and scaled for clinical use, eye tracking-based risk and symptom measures could be used to inform clinical judgment regarding ASD identification and to track autism symptom levels in clinical trials and longitudinal studies.