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In the last decade, there has been an increase in publications on technology-based interventions for autism spectrum disorder (ASD). Virtual reality based assessments and intervention tools are promising and have shown to be acceptable amongst individuals with ASD. This scoping review reports on 49 studies utilizing virtual reality and augmented reality technology in social skills interventions for individuals with ASD. The included studies mostly targeted children and adolescents, but few targeted very young children or adults. Our findings show that the mode number of participants with ASD is low, and that female participants are underrepresented. Our review suggests that there is need for studies that apply virtual and augmented realty with more rigorous designs involving established and evidenced-based intervention strategies.

Background Social attention differences, expressed through gaze patterns, have been documented in autism spectrum disorder (ASD), with subtle differences also reported among first-degree relatives, suggesting a shared genetic link. Findings have mostly been derived from standard eye-tracking methods (total fixation count or total fixation duration). Given the dynamics of visual attention, these standard methods may obscure subtle, yet core, differences in visual attention mechanisms, particularly those presenting sub-clinically. This study applied a constellation of eye-tracking analyses to gaze data from individuals with ASD and their parents. Methods This study included n  = 156 participants across groups, including ASD ( n  = 24) and control ( n  = 32) groups, and parents of individuals with ASD ( n  = 61) and control parents ( n  = 39). A complex scene with social/non-social elements was displayed and gaze tracked via an eye tracker. Eleven analytic methods from the following categories were analyzed: (1) standard variables, (2) temporal dynamics (e.g., gaze over time), (3) fixation patterns (e.g., perseverative or regressive fixations), (4) first fixations, and (5) distribution patterns. MANOVAs, growth curve analyses, and Chi-squared tests were applied to examine group differences. Finally, group differences were examined on component scores derived from a principal component analysis (PCA) that reduced variables to distinct dimensions. Results No group differences emerged among standard, first fixation, and distribution pattern variables. Both the ASD and ASD parent groups demonstrated on average reduced social attention over time and atypical perseverative fixations. Lower social attention factor scores derived from PCA strongly differentiated the ASD and ASD parent groups from controls, with parent findings driven by the subset of parents demonstrating the broad autism phenotype. Limitations To generalize these findings, larger sample sizes, extended viewing contexts (e.g., dynamic stimuli), and even more eye-tracking analytical methods are needed. Conclusions Fixations over time and perseverative fixations differentiated ASD and the ASD parent groups from controls, with the PCA most robustly capturing social attention differences. Findings highlight their methodological utility in studies of the (broad) autism spectrum to capture nuanced visual attention differences that may relate to clinical symptoms in ASD, and reflect genetic liability in clinically unaffected relatives. This proof-of-concept study may inform future studies using eye tracking across populations where social attention is impacted.

Clinical research in neurodevelopmental disorders remains reliant upon clinician and caregiver measures. Limitations of these approaches indicate a need for objective, quantitative, and reliable biomarkers to advance clinical research. Extant research suggests the potential utility of multiple candidate biomarkers; however, effective application of these markers in trials requires additional understanding of replicability, individual differences, and intra-individual stability over time. The Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is a multi-site study designed to investigate a battery of electrophysiological (EEG) and eye-tracking (ET) indices as candidate biomarkers for autism spectrum disorder (ASD). The study complements published biomarker research through: inclusion of large, deeply phenotyped cohorts of children with ASD and typical development; a longitudinal design; a focus on well-evidenced candidate biomarkers harmonized with an independent sample; high levels of clinical, regulatory, technical, and statistical rigor; adoption of a governance structure incorporating diverse expertise in the ASD biomarker discovery and qualification process; prioritization of open science, including creation of a repository containing biomarker, clinical, and genetic data; and use of economical and scalable technologies that are applicable in developmental populations and those with special needs. The ABC-CT approach has yielded encouraging results, with one measure accepted into the FDA's Biomarker Qualification Program to date. Through these advances, the ABC-CT and other biomarker studies in progress hold promise to deliver novel tools to improve clinical trials research in ASD.

Social attention, including shared attention and social orienting, is essential for positive social interactions. Although early visual social attention is often quantified using eye tracking, these indices may not consistently reflect cognitive engagement. Heart rate defined sustained attention (HRDSA) is a physiological measure that can index cognitive engagement alongside visual attention, leading to more comprehensive assessments of attentional processes that are particularly important in young, neurodiverse children with high support needs, including those with autism and fragile X syndrome (FXS). The present study examined visual and heart-defined measures of social attention to the Selective Social Attention task, a video-based assay of social attention, in children with autism, FXS, and neurotypical development. Linear mixed models examined group and condition effects in multiple cardiac indices and overall looking at the scene. Findings suggest that, overall, children across all groups engaged similarly across the experiment in most dimensions of HRDSA, and consistent with previous work, autistic children spent less time visually attending to the scene than either other group. HRDSA was positively associated with visual social attention. Combining physiological and visual attention measures may elucidate the complex nature of social attention and be especially valuable for neurodiverse children when typical assessments are inaccessible.

Participants with autism spectrum disorder (ASD) (n = 121, mean [SD] age: 14.6 [8.0] years) and typically developing (TD) controls (n = 40, 16.4 [13.3] years) were presented with a series of videos representing biological motion on one side of a computer monitor screen and non-biological motion on the other, while their eye movements were recorded. As predicted, participants with ASD spent less overall time looking at presented stimuli than TD participants ( P  < 10 –3 ) and showed less preference for biological motion ( P  < 10 –5 ). Participants with ASD also had greater average latencies than TD participants of the first fixation on both biological ( P  < 0.01) and non-biological motion ( P  < 0.02). Findings suggest that individuals with ASD differ from TD individuals on multiple properties of eye movements and biological motion preference.

Background Eye tracking (ET) is a powerful methodology for studying attentional processes through quantification of eye movements. The precision, usability, and cost-effectiveness of ET render it a promising platform for developing biomarkers for use in clinical trials for autism spectrum disorder (ASD). Methods The Autism Biomarkers Consortium for Clinical Trials conducted a multisite, observational study of 6–11-year-old children with ASD ( n = 280) and typical development (TD, n = 119). The ET battery included: Activity Monitoring, Social Interactive, Static Social Scenes, Biological Motion Preference, and Pupillary Light Reflex tasks. A priori, gaze to faces in Activity Monitoring, Social Interactive, and Static Social Scenes tasks were aggregated into an Oculomotor Index of Gaze to Human Faces (OMI) as the primary outcome measure. This work reports on fundamental biomarker properties (data acquisition rates, construct validity, six-week stability, group discrimination, and clinical relationships) derived from these assays that serve as a base for subsequent development of clinical trial biomarker applications. Results All tasks exhibited excellent acquisition rates, met expectations for construct validity, had moderate or high six-week stabilities, and highlighted subsets of the ASD group with distinct biomarker performance. Within ASD, higher OMI was associated with increased memory for faces, decreased autism symptom severity, and higher verbal IQ and pragmatic communication skills. Limitations No specific interventions were administered in this study, limiting information about how ET biomarkers track or predict outcomes in response to treatment. This study did not consider co-occurrence of psychiatric conditions nor specificity in comparison with non-ASD special populations, therefore limiting our understanding of the applicability of outcomes to specific clinical contexts-of-use. Research-grade protocols and equipment were used; further studies are needed to explore deployment in less standardized contexts. Conclusions All ET tasks met expectations regarding biomarker properties, with strongest performance for tasks associated with attention to human faces and weakest performance associated with biological motion preference. Based on these data, the OMI has been accepted to the FDA’s Biomarker Qualification program, providing a path for advancing efforts to develop biomarkers for use in clinical trials.

The relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD. Caregivers of ASD ( = 144) and typically developing (TD) ( = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety. Prevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly ( < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly ( < 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety. Caregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD.

Biomarker development is currently a high priority in neurodevelopmental disorder research. For many types of biomarkers (particularly biomarkers of diagnosis), reliability over short periods is critically important. In the field of autism spectrum disorder (ASD), resting electroencephalography (EEG) power spectral densities (PSD) are well-studied for their potential as biomarkers. Classically, such data have been decomposed into pre-specified frequency bands (e.g., delta, theta, alpha, beta, and gamma). Recent technical advances, such as the Fitting Oscillations and One-Over-F (FOOOF) algorithm, allow for targeted characterization of the features that naturally emerge within an EEG PSD, permitting a more detailed characterization of the frequency band-agnostic shape of each individual's EEG PSD. Here, using two resting EEGs collected a median of 6 days apart from 22 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the Autism Biomarkers Consortium for Clinical Trials, we estimate test-retest reliability based on the characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set of basis functions that characterize individual power spectrum shapes. We show that individuals exhibit idiosyncratic PSD signatures that are stable over recording sessions using both characterizations. Our data show that EEG activity from a brief 2-min recording provides an efficient window into characterizing brain activity at the single-subject level with desirable psychometric characteristics that persist across different analytical decomposition methods. This is a necessary step towards analytical validation of biomarkers based on the EEG PSD and provides insights into parameters of the PSD that offer short-term reliability (and thus promise as potential biomarkers of trait or diagnosis) vs. those that are more variable over the short term (and thus may index state or other rapidly dynamic measures of brain function). Future research should address the longer-term stability of the PSD, for purposes such as monitoring development or response to treatment.

Early identification of autism spectrum disorder (ASD) is regarded as crucial for swift access to early intervention and, subsequently, better outcomes later in life. However, current instruments miss large proportions of children who later go on to be diagnosed with ASD, raising a question of what these instruments measure. The present study utilized data from the Norwegian Mother, Father, and Child Cohort Study and the Autism Birth Cohort study to explore the subsequent developmental and diagnostic characteristics of children raising developmental concern on the six-critical discriminative item criterion of the M-CHAT (DFA6) at 18 months of age (N = 834). The DFA6 identified 28.8% of children diagnosed with ASD (N = 163), but 4.4% with language disorder (N = 188) and 81.3% with intellectual disability (N = 32) without ASD. Scoring in the «at-risk» range was associated with lower IQ, impaired functional language, and greater severity of autism symptoms whether children had ASD or not.

Biomarkers for autism spectrum disorder (ASD) are lacking but would facilitate drug development for the core deficits of the disorder. We evaluated markers proposed for characterization of differences in social communication and interaction in adults with ASD versus healthy controls (HC) for utility as biomarkers. Data pooled from an observational study and baseline data from a placebo-controlled study were analyzed. Between-group differences were observed in eye-tracking tasks for activity monitoring, biomotion, human activity preference, composite score (p = 0.0001–0.037) and pupillometry (various tasks, p = 0.017–0.05). Impaired olfaction was more common in the ASD sample versus HC (p = 0.018). Our preliminary results suggest the potential use for stratification and response sub-analyses outcome-prediction of specific eye-tracking tasks, pupillometry and olfaction tests in ASD trials