In a research project aimed at developing an objective index to identify autism in patients, subjects were recorded with Tobii Pro eye trackers while viewing video clips. By analyzing gaze patterns, researchers developed a quantitative method to help diagnose autism.
The prevalence of autism, a developmental disorder characterized by impairments in social interaction and communication, has increased tenfold in the last 30 years. The diagnosis depends on correct judgments as to whether each symptom listed in the diagnostic criteria is met or not. This means an accurate diagnosis of autism calls for an expert in the field, but there are too few of these to meet the increasing needs of the population. Consequently, there is a demand for a system that can be used by non-experts with an accuracy comparable to that of someone trained in this disorder.
Gaze analysis can be used as an autism diagnosis system. In recent years, unobtrusive eye tracking systems without a chin rest or headgear have become increasingly accurate. This new technology has made eye tracking infants and young children as easy as testing adult participants.
The objective for the researchers at Osaka University was to develop a quantitative scale for identifying individuals with autism based on gaze measurement data that could be applied to adults, as well as children.
By using eye tracking, even a non-expert is able to draw ample information from non-verbal children. The method has a wide range of applications such as screening of developmental disorders in children.
The team at the Graduate School of Frontier Biosciences at Osaka University in Japan examined 25 young children (average age of 3) with autism, 25 age-matched children with typical development (also known as TD or neurotypical children), 27 adults with autism, and 27 neurotypical adults. The individuals viewed the same brief video clips taken from films and TV programs for young children.
The video stimulus featured several characters talking, with varying degrees of social context and distracters in each scene. Gaze positions of both eyes were measured with a Tobii X50 screen-based eye tracker. The researchers then analyzed the gaze patterns for all of the participants, focusing on the subjects' attention to eyes and mouths during the video clips.
Taking all of the subjects' gaze patterns into account, the team summarized the data by using multidimensional scaling (MDS). If the temporospatial gaze trajectories are similar in a pair of subjects, they would be plotted very near to each other. Thus, a group of subjects with a similar gaze behavior would form a cluster, and those with atypical gaze behaviors would be plotted in the periphery, far from the others.
When the data was plotted in this way, most neurotypical control group participants were distributed near the center, while the participants with autism were distributed along the periphery (see the plot in the top right figure; the '+' mark in the center of the plot indicates the median, which can be thought of as corresponding to the most neurotypical way of looking at the video stimulus). The distance from the 'center' (MDS distance) was thus smaller in the neurotypical groups than in the participants with autism. The results show that neurotypical control groups share similar gaze patterns, whereas those with autism show atypical gaze behaviors that differ from one subject to another.
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