Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences.

Social communication differences are seen in autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), but the brain mechanisms contributing to these differences remain largely unknown. To address this gap, we used a data-driven and diagnosis-agnostic approach to discover brain correlates of social communication differences in ASD, ADHD, and OCD, and subgroups of individuals who share similar patterns of brain-behavior associations. A machine learning pipeline (regression clustering) was used to discover the pattern of association between structural brain measures (volume, surface area, and cortical thickness) and social communication abilities. Participants (n = 416) included children with a diagnosis of ASD (n = 192, age = 12.0[5.6], 19% female), ADHD (n = 109, age = 11.1[4.1], 18% female), or OCD (n = 50, age = 12.3[4.2], 42% female), and typically developing controls (n = 65, age = 11.6[7.1], 48% female). The analyses revealed (1) associations with social communication abilities in distributed cortical and subcortical networks implicated in social behaviors, language, attention, memory, and executive functions, and (2) three data-driven, diagnosis-agnostic subgroups based on the patterns of association in the above networks. Our results suggest that different brain networks may contribute to social communication differences in subgroups that are not diagnosis-specific.

Data-Driven Discovery of Predictors of Virtual Reality Safety and Sense of Presence for Children With Autism Spectrum Disorder: A Pilot Study.

Virtual reality (VR) offers children with autism spectrum disorder (ASD) an inexpensive and motivating medium to learn and practice skills in a personalized, controlled, and safe setting; however, outcomes of VR interventions can vary widely. In particular, there is a need to understand the predictors of VR experience in children with ASD to inform the design of these interventions. To address this gap, a sample of children with ASD (n=35, mean age: 13.0 ± 2.6 years; 10 female) participated in a pilot study involving an immersive VR experience delivered through a head-mounted display. A data-driven approach was used to discover predictors of VR safety and sense of presence among a range of demographic and phenotypic user characteristics. Our results suggest that IQ may be a key predictor of VR sense of presence and that anxiety may modify the association between IQ and sense of presence. In particular, in low-anxiety participants, IQ was linearly related to experienced spatial presence and engagement, whereas, in high-anxiety participants, this association followed a quadratic form. The results of this pilot study, when replicated in larger samples, will inform the design of future studies on VR interventions for children with ASD.

Short report: Evaluating the safety and usability of head-mounted virtual reality compared to monitor-displayed video for children with autism spectrum disorder.

LAY ABSTRACT: This study investigated the safety and usability of a virtual reality experience for children with autism spectrum disorder in a laboratory setting. In our study, the negative effects of head-mounted display-virtual reality were similar to monitor-displayed video watching. At the same time, the participants indicated that the head-mounted display-virtual reality experience provided improved realism and sense of presence. This study is a first step in understanding the impact of head-mounted display on children with autism spectrum disorder.

Heart Rate monitoring during physical exercise using wrist-type photoplethysmographic (PPG) signals.

Heart rate monitoring using wrist-type using photoplethysmographic (PPG) signals during subjects' intensive exercises is a challenging problem, since signals are strongly affected by motion artifacts caused by unexpected movements. This paper presents a method that uses both time and frequency characteristics of signals; using sparse signal reconstruction for high-resolution spectrum estimation. Experimental results on type data sets recorded from 12 subjects during fast running at peak speed of 15 km/hour. The results have a performance with the average absolute error being 1.80 beat per minute.