Social interaction deficits are evident in many psychiatric conditions and specifically in autism spectrum disorder (ASD), but hard to assess objectively. We present a digital tool to automatically quantify biomarkers of social interaction deficits: the simulated interaction task (SIT), which entails a standardized 7-min simulated dialog via video and the automated analysis of facial expressions, gaze behavior, and voice characteristics. In a study with 37 adults with ASD without intellectual disability and 43 healthy controls, we show the potential of the tool as a diagnostic instrument and for better description of ASD-associated social phenotypes. Using machine-learning tools, we detected individuals with ASD with an accuracy of 73%, sensitivity of 67%, and specificity of 79%, based on their facial expressions and vocal characteristics alone. Especially reduced social smiling and facial mimicry as well as a higher voice fundamental frequency and harmony-to-noise-ratio were characteristic for individuals with ASD. The time-effective and cost-effective computer-based analysis outperformed a majority vote and performed equal to clinical expert ratings.npj Digital Medicine (2020) 3:25 ; https://doi.
Individuals with Autism Spectrum Disorder (ASD) experience a variety of symptoms sometimes including atypicalities in language use. The study explored differences in semantic network organisation of adults with ASD without intellectual impairment. We assessed clusters and switches in verbal fluency tasks ('animals', 'human feature', 'verbs', 'r-words') via curve fitting in combination with corpus-driven analysis of semantic relatedness and evaluated socio-emotional and motor action related content. Compared to participants without ASD (n = 39), participants with ASD (n = 32) tended to produce smaller clusters, longer switches, and fewer words in semantic conditions (no p values survived Bonferroni-correction), whereas relatedness and content were similar. In ASD, semantic networks underlying cluster formation appeared comparably small without affecting strength of associations or content.
Background Imitation of facial expressions plays an important role in social functioning. However, little is known about the quality of facial imitation in individuals with autism and its relationship with defining difficulties in emotion recognition. Methods We investigated imitation and recognition of facial expressions in 37 individuals with autism spectrum conditions and 43 neurotypical controls. Using a novel computer-based face analysis, we measured instructed imitation of facial emotional expressions and related it to emotion recognition abilities. Results Individuals with autism imitated facial expressions if instructed to do so, but their imitation was both slower and less precise than that of neurotypical individuals. In both groups, a more precise imitation scaled positively with participants’ accuracy of emotion recognition. Limitations Given the study’s focus on adults with autism without intellectual impairment, it is unclear whether the results generalize to children with autism or individuals with intellectual disability. Further, the new automated facial analysis, despite being less intrusive than electromyography, might be less sensitive. Conclusions Group differences in emotion recognition, imitation and their interrelationships highlight potential for treatment of social interaction problems in individuals with autism.
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