Basic oculomotor function is similar in young children with ASD and typically developing controls

Oculomotor randomness is higher in autistic children and increases with the severity of symptoms

A variety of studies have suggested that at least some children with autism spectrum disorder (ASD) view the world differently. Differences in gaze patterns as measured by eye tracking have been demonstrated during visual exploration of images and natural viewing of movies with social content. Here we analyzed the temporal randomness of saccades and blinks during natural viewing of movies, inspired by a recent measure of "randomness" applied to micro-movements of the hand and head in ASD (Torres et al., 2013; Torres & Denisova, 2016). We analyzed a large eye-tracking dataset of 189 ASD and 41 typically developing (TD) children (1-11 years old) who watched three movie clips with social content, each repeated twice. We found that oculomotor measures of randomness, obtained from gamma parameters of inter-saccade intervals (ISI) and blink duration distributions, were significantly higher in the ASD group compared with the TD group and were correlated with the ADOS comparison score, reflecting increased "randomness" in more severe cases. Moreover, these measures of randomness decreased with age, as well as with higher cognitive scores in both groups and were consistent across repeated viewing of each movie clip. Highly "random" eye movements in ASD children could be associated with high "neural variability" or noise, poor sensory-motor control, or weak engagement with the movies. These findings could contribute to the future development of oculomotor biomarkers as part of an integrative diagnostic tool for ASD.

The effect of background elements of pictures on the visual attention among ASD children with intellectual disabilities, children with intellectual disabilities and typical development: Evidence from eye-tracking and fMRI

Traditional picture books for children come with colourful images and a multitude of elements to attract attention and increase the reading interest of typical-developing (TD) children. However, children with Autism Spectrum Disorder (ASD) are less capable of filtering out unimportant elements in pictures and focusing on social items (e.g., human faces). This study proposed that the removal of background and less important elements in the pictures of children's storybooks could facilitate better attention and enhance children with ASD's focus on the main object and thus the intended meaning of the storybook. We adopted pictures from a well-known children's book and modified them by removing the inessential background elements. Then, ASD children with intellectual disabilities (ASD+ID) (n = 40), children with ID (n = 38) and TD (n = 40) were asked to view the original and modified pictures in an eye-tracking experiment, respectively. Additionally, brain activation of ASD+ID participants (n = 10) was recorded as they were viewing those pictures in an fMRI scan. Eye-tracking found that ASD+ID children viewed the modified pictures with significantly longer average fixations, fewer fixations, fewer saccades, and higher fixation/saccade duration ratio. Contrary to the original pictures, no significant differences were found among ASD+ID, ID only and TD. Especially, ASD+ID group showed highly similar visual patterns to the TD participants when viewing the modified pictures and particularly focusing on the main character in the pictures. Additional fMRI evidence on ASD+ID group also revealed that modified pictures were associated with enhanced activation in bilateral fusiform gyri as compared to those from original pictures, which might suggest increased visual attention. Theoretical and practical implications were discussed in light of our findings.

Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach

This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child's 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD.