Different Eye Tracking Patterns in Autism Spectrum Disorder in Toddler and Preschool Children

• Published in: Frontiers in psychiatry Vol. 13; p. 899521
• Main Authors: Kong, Xue-Jun, Wei, Zhen, Sun, Binbin, Tu, Yiheng, Huang, Yiting, Cheng, Ming, Yu, Siyi, Wilson, Georgia, Park, Joel, Feng, Zhe, Vangel, Mark, Kong, Jian, Wan, Guobin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 09.06.2022
• Subjects: autism; eye tracking; machine learning; percentage of fixation time; preschool; Psychiatry; toddler; support vector machine; toddler; percentage of fixation time; eye tracking; preschool; machine learning; autism
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2022.899521

Children with autism spectrum disorder (ASD) have been observed to be associated with fixation abnormality as measured eye tracking, but the dynamics behind fixation patterns across age remain unclear. In this study, we investigated gaze patterns between toddlers and preschoolers with and without ASD while they viewed video clips and still images (i.e., mouth-moving face, biological motion, mouthing face vs. moving object, still face picture vs. objects, and moving toys). We found that the fixation time percentage of children with ASD showed significant decrease compared with that of TD children in almost all areas of interest (AOI) except for moving toy (helicopter). We also observed a diagnostic group (ASD vs. TD) and chronological age (Toddlers vs. preschooler) interaction for the eye AOI during the mouth-moving video clip. Support vector machine analysis showed that the classifier could discriminate ASD from TD in toddlers with an accuracy of 80% and could discriminate ASD from TD in preschoolers with an accuracy of 71%. Our results suggest that toddlers and preschoolers may be associated with both common and distinct fixation patterns. A combination of eye tracking and machine learning methods has the potential to shed light on the development of new early screening/diagnosis methods for ASD.