The Cognitive Neuroscience of Autism Spectrum Disorders

National Institute Of Mental Health

Investigators

Linked publications, trials & patents

Abstract

This past year we have continued to evaluate the neural and behavioral characteristics of high-functioning individuals with an Autism Spectrum Disorder (ASD). We have been particularly interested in understanding aberrant eye movement patterns in ASD when confronted with dynamic social stimuli. Extensive study of TD and ASD participants has identified a large number of brain regions associated with our ability to navigate the social world. Although it is widely appreciated that this so-called social brain is composed of distinct, interacting systems, these component parts have yet to be clearly elucidated. In this study we used measures of eye movement and neural typicality (ie., the extent to which each individual TD and ASD participant differed from the norm) while they watched a battery of short movie clips depicting social interactions. Our findings provided evidence for dysfunction in the autistic individuals in two components of the social brain; one underpinning the ability to orient attention to social stimuli, the other underpinning the ability to infer the mental state of others (Ramot et al., Communications Biology, 2020). This study added to a growing body of work indicating that measures of eye tracking provide insights into social processing deficits in autism spectrum disorder (ASD), especially in conjunction with dynamic, naturalistic, freely-viewed stimuli. However, the question remains as to whether eye-gaze characteristics, such as preference for viewing specific facial features, can be considered a stable individual trait, particularly in those with ASD. If so, how much data are needed for consistent estimations? Following up on the study described above (Ramot et al., Communications Biology, 2020), we assessed the stability and robustness of gaze preference for facial features as incremental amounts of movie data were introduced for analysis. To accomplish this we trained an artificial neural network to create an object-based segmentation of naturalistic movie clips (14 s each, 7410 frames total). In the study, 33 high-functioning individuals with ASD and 36 typically developing individuals of similar IQ and age (age range: 12-30 years) viewed 22 Hollywood movie clips, each depicting a social interaction. As we evaluated combinations of one, three, five, eight, and 11 movie clips, gaze times on core facial features became increasingly stable at within-subject, within-group, and between-group levels. Using a number of movie clips deemed sufficient by our analysis, we found that individuals with ASD displayed significantly less face-centered gaze (centralized on the nose) but that viewing times did not significantly differ from typically developing participants when focusing on eyes or mouths. Our findings validate gaze preference for specific facial features as a stable individual trait and highlight the possibility of misinterpretation with insufficient data. Additionally, we propose the use of a machine learning approach to stimuli segmentation to quickly and flexibly prepare dynamic stimuli for analysis (Reimann et al., Autism Research, 2021).