Explore the vast range of titles available.

Although it has often been argued that clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorder (ASD), relatively few investigations have indexed the impact of intervention and feedback approaches. This pilot study investigated the application of a novel robotic interaction system capable of administering and adjusting joint attention prompts to a small group (n = 6) of children with ASD. Across a series of four sessions, children improved in their ability to orient to prompts administered by the robotic system and continued to display strong attention toward the humanoid robot over time. The results highlight both potential benefits of robotic systems for directed intervention approaches as well as potent limitations of existing humanoid robotic platforms.

Neuropsychiatric disorders vary substantially in age of onset but are best understood within the context of neurodevelopment. Here, we review opportunities for intervention at critical points in developmental trajectories. We begin by discussing potential opportunities to prevent neuropsychiatric disorders. Once symptoms begin to emerge, a number of interventions have been studied either before a diagnosis can be made or shortly after diagnosis. Although some of these interventions are helpful, few are based upon an understanding of pathophysiology, and most ameliorate rather than resolve symptoms. As such, in the next portion of the review, we turn our discussion to genetic syndromes that are rare phenocopies of common diagnoses such as autism spectrum disorder or schizophrenia. Cellular or animal models of these syndromes point to specific regulatory or signaling pathways. As examples, findings from the mouse models of Fragile X and Rett syndromes point to potential treatments now being tested in randomized clinical trials. Paralleling oncology, we can hope that our treatments will move from nonspecific, like chemotherapies thrown at a wide range of tumor types, to specific, like the protein kinase inhibitors that target molecularly defined tumors. Some of these targeted treatments later show benefit for a broader, yet specific, array of cancers. We can hope that medications developed within rare neurodevelopmental syndromes will similarly help subgroups of patients with disruptions in overlapping signaling pathways. The insights gleaned from treatment development in rare phenocopy syndromes may also teach us how to test treatments based upon emerging common genetic or environmental risk factors.

Existing literature regarding social communication outcomes of interventions in autism spectrum disorder (ASD) depends upon human raters, with limited generalizability to real world settings. Technological innovation, particularly virtual reality (VR) and collaborative virtual environments (CVE), could offer a replicable, low cost measurement platform when endowed with intelligent agent technology and peer-based interactions. We developed and piloted a novel collaborative virtual environment and intelligent agent (CRETA) for the assessment of social communication and collaboration within system and peer interactions. The system classified user statements with moderate to high accuracies. We found moderate to high agreement in displayed communication and collaboration skills between human–human and human–agent interactions. CRETA offers a promising avenue for future development of autonomous measurement systems for ASD research.

The current study examined depressive symptoms, concerning the week following autism spectrum diagnosis and an average of 1.4 years later, in mothers ( n  = 75) of young children diagnosed with an autism spectrum disorder (ASD). Over three-quarters of mothers (78.7%) provided retrospective reports of clinically significant depressive symptoms regarding the week following their child’s ASD diagnosis, with some 37.3% continuing to report clinically significant levels of depressive symptoms at follow-up. Depressive symptoms immediately following diagnosis were not related to initial global characteristics of child functioning, but were related to reported child problem behaviors and financial barriers at follow-up. Results of this study underscore the importance of attention to caregiver distress and depression within models of autism detection and intervention.

Proposed DSM-5 revisions to the diagnosis of autism spectrum disorder (ASD) include a “severity” marker based on degree of impairment. Although qualitative differences between support levels are described, quantitative methods or practice recommendations for differentiating between levels remain undetermined. This leaves the field vulnerable to potential discrepancies between severity categorizations that may have inadvertent service implications. We examined overlap between mild, moderate, and severe impairment classifications based on autism symptoms, cognitive skills, and adaptive functioning in 726 participants (15 months—17 years) with ASD. Participants with mild, moderate, and severe autism symptoms demonstrated varying levels of adaptive and cognitive impairment. These discrepancies highlight the need for a clearly elucidated method of classifying level of support in ASD diagnosis.

Most humans believe in a god, but many do not. Differences in belief have profound societal impacts. Anthropological accounts implicate bottom-up perceptual processes in shaping religious belief, suggesting that individual differences in these processes may help explain variation in belief. Here, in findings replicated across socio-religiously disparate samples studied in the U.S. and Afghanistan, implicit learning of patterns/order within visuospatial sequences (IL-pat) in a strongly bottom-up paradigm predict 1) stronger belief in an intervening/ordering god, and 2) increased strength-of-belief from childhood to adulthood, controlling for explicit learning and parental belief. Consistent with research implicating IL-pat as a basis of intuition, and intuition as a basis of belief, mediation models support a hypothesized effect pathway whereby IL-pat leads to intuitions of order which, in turn, lead to belief in ordering gods. The universality and variability of human IL-pat may thus contribute to the global presence and variability of religious belief. Beliefs about gods are theorized to develop from bottom-up neurocognitive processes. Here, in the U.S. and Afghanistan, the authors show that superior implicit learning of patterns in visuo-spatial stimuli predicts stronger belief in intervening gods and greater increase in belief since childhood.

Although there has been growing interest in utilizing robots for intervention in autism spectrum disorder (ASD), there have been very few controlled trials to assess the actual impacts of such systems on social communication vulnerabilities. This study reports a randomized controlled trial to investigate a robot-mediated response to joint attention intervention in a small (23 recruited; 20 completed) group of young children (average age = 2.54 years) with ASD. Small and nonsignificant group differences were observed regarding improvements in response to joint attention skills within and beyond the intervention. The sample showed tremendous individual variability in response to the system. Results highlight the current challenges related to developing pragmatic, beneficial, and generalizable robotic intervention systems for the targeted population.

Autism spectrum disorder (ASD). 2018. The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts active surveillance of ASD. This report focuses on the prevalence and characteristics of ASD among children aged 8 years in 2018 whose parents or guardians lived in 11 ADDM Network sites in the United States (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin). To ascertain ASD among children aged 8 years, ADDM Network staff review and abstract developmental evaluations and records from community medical and educational service providers. In 2018, children met the case definition if their records documented 1) an ASD diagnostic statement in an evaluation (diagnosis), 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code. For 2018, across all 11 ADDM sites, ASD prevalence per 1,000 children aged 8 years ranged from 16.5 in Missouri to 38.9 in California. The overall ASD prevalence was 23.0 per 1,000 (one in 44) children aged 8 years, and ASD was 4.2 times as prevalent among boys as among girls. Overall ASD prevalence was similar across racial and ethnic groups, except American Indian/Alaska Native children had higher ASD prevalence than non-Hispanic White (White) children (29.0 versus 21.2 per 1,000 children aged 8 years). At multiple sites, Hispanic children had lower ASD prevalence than White children (Arizona, Arkansas, Georgia, and Utah), and non-Hispanic Black (Black) children (Georgia and Minnesota). The associations between ASD prevalence and neighborhood-level median household income varied by site. Among the 5,058 children who met the ASD case definition, 75.8% had a diagnostic statement of ASD in an evaluation, 18.8% had an ASD special education classification or eligibility and no ASD diagnostic statement, and 5.4% had an ASD ICD code only. ASD prevalence per 1,000 children aged 8 years that was based exclusively on documented ASD diagnostic statements was 17.4 overall (range: 11.2 in Maryland to 29.9 in California). The median age of earliest known ASD diagnosis ranged from 36 months in California to 63 months in Minnesota. Among the 3,007 children with ASD and data on cognitive ability, 35.2% were classified as having an intelligence quotient (IQ) score ≤70. The percentages of children with ASD with IQ scores ≤70 were 49.8%, 33.1%, and 29.7% among Black, Hispanic, and White children, respectively. Overall, children with ASD and IQ scores ≤70 had earlier median ages of ASD diagnosis than children with ASD and IQ scores >70 (44 versus 53 months). In 2018, one in 44 children aged 8 years was estimated to have ASD, and prevalence and median age of identification varied widely across sites. Whereas overall ASD prevalence was similar by race and ethnicity, at certain sites Hispanic children were less likely to be identified as having ASD than White or Black children. The higher proportion of Black children compared with White and Hispanic children classified as having intellectual disability was consistent with previous findings. The variability in ASD prevalence and community ASD identification practices among children with different racial, ethnic, and geographical characteristics highlights the importance of research into the causes of that variability and strategies to provide equitable access to developmental evaluations and services. These findings also underscore the need for enhanced infrastructure for diagnostic, treatment, and support services to meet the needs of all children.

Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.

Autism spectrum disorder (ASD). 2016. The Autism and Developmental Disabilities Monitoring (ADDM) Network is an active surveillance program that provides estimates of the prevalence of ASD among children aged 8 years whose parents or guardians live in 11 ADDM Network sites in the United States (Arizona, Arkansas, Colorado, Georgia, Maryland, Minnesota, Missouri, New Jersey, North Carolina, Tennessee, and Wisconsin). Surveillance is conducted in two phases. The first phase involves review and abstraction of comprehensive evaluations that were completed by medical and educational service providers in the community. In the second phase, experienced clinicians who systematically review all abstracted information determine ASD case status. The case definition is based on ASD criteria described in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. For 2016, across all 11 sites, ASD prevalence was 18.5 per 1,000 (one in 54) children aged 8 years, and ASD was 4.3 times as prevalent among boys as among girls. ASD prevalence varied by site, ranging from 13.1 (Colorado) to 31.4 (New Jersey). Prevalence estimates were approximately identical for non-Hispanic white (white), non-Hispanic black (black), and Asian/Pacific Islander children (18.5, 18.3, and 17.9, respectively) but lower for Hispanic children (15.4). Among children with ASD for whom data on intellectual or cognitive functioning were available, 33% were classified as having intellectual disability (intelligence quotient [IQ] ≤70); this percentage was higher among girls than boys (39% versus 32%) and among black and Hispanic than white children (47%, 36%, and 27%, respectively) [corrected]. Black children with ASD were less likely to have a first evaluation by age 36 months than were white children with ASD (40% versus 45%). The overall median age at earliest known ASD diagnosis (51 months) was similar by sex and racial and ethnic groups; however, black children with IQ ≤70 had a later median age at ASD diagnosis than white children with IQ ≤70 (48 months versus 42 months). The prevalence of ASD varied considerably across sites and was higher than previous estimates since 2014. Although no overall difference in ASD prevalence between black and white children aged 8 years was observed, the disparities for black children persisted in early evaluation and diagnosis of ASD. Hispanic children also continue to be identified as having ASD less frequently than white or black children. These findings highlight the variability in the evaluation and detection of ASD across communities and between sociodemographic groups. Continued efforts are needed for early and equitable identification of ASD and timely enrollment in services.