1. Alcaniz Raya M, Marin-Morales J, Minissi ME, Teruel Garcia G, Abad L, Chicchi Giglioli IA. {{Machine Learning and Virtual Reality on Body Movements’ Behaviors to Classify Children with Autism Spectrum Disorder}}. {J Clin Med}. 2020; 9(5).
Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements’ frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients’ subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements’ biomarkers that could contribute to improving ASD diagnosis.
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2. Lopata C, Donnelly JP, Thomeer ML, Rodgers JD, Lodi-Smith J, Booth AJ, Volker MA. {{Moderators of School Intervention Outcomes for Children with Autism Spectrum Disorder}}. {Journal of abnormal child psychology}. 2020.
A prior cluster randomized controlled trial (RCT) compared outcomes for a comprehensive school intervention (schoolMAX) to typical educational programming (services-as-usual [SAU]) for 103 children with autism spectrum disorder (ASD) without intellectual disability. The schoolMAX intervention was superior to SAU in improving social-cognitive understanding (emotion-recognition), social/social-communication skills, and ASD-related impairment (symptoms). In the current study, a range of demographic, clinical, and school variables were tested as potential moderators of treatment outcomes from the prior RCT. Moderation effects were not evident in demographics, child IQ, language, or ASD diagnostic symptoms, or school SES. Baseline externalizing symptoms moderated the outcome of social-cognitive understanding and adaptive skills moderated the outcome of ASD-related symptoms (no other comorbid symptoms or adaptive skills ratings moderated outcomes on the three measures). Overall, findings suggest that the main effects of treatment were, with two exceptions, unaffected by third variables.
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3. Noel JP, Lakshminarasimhan KJ, Park H, Angelaki DE. {{Increased variability but intact integration during visual navigation in Autism Spectrum Disorder}}. {Proceedings of the National Academy of Sciences of the United States of America}. 2020.
Autism Spectrum Disorder (ASD) is a common neurodevelopmental disturbance afflicting a variety of functions. The recent computational focus suggesting aberrant Bayesian inference in ASD has yielded promising but conflicting results in attempting to explain a wide variety of phenotypes by canonical computations. Here, we used a naturalistic visual path integration task that combines continuous action with active sensing and allows tracking of subjects’ dynamic belief states. Both groups showed a previously documented bias pattern by overshooting the radial distance and angular eccentricity of targets. For both control and ASD groups, these errors were driven by misestimated velocity signals due to a nonuniform speed prior rather than imperfect integration. We tracked participants’ beliefs and found no difference in the speed prior, but there was heightened variability in the ASD group. Both end point variance and trajectory irregularities correlated with ASD symptom severity. With feedback, variance was reduced, and ASD performance approached that of controls. These findings highlight the need for both more naturalistic tasks and a broader computational perspective to understand the ASD phenotype and pathology.
