Current Autism Spectrum Disorder Research Articles

LAMP1 as a novel molecular biomarker to predict the prognosis of the children with autism spectrum disorder using bioinformatics approaches

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that usually manifests in childhood and is thought to be caused by a complex interaction of genetic, environmental, and immune factors. The majority of current ASD diagnostic methods rely on subjective behavioral observation and scale assessment, making early detection difficult. In this study, we confirmed that lysosomal-associated membrane protein 1 (LAMP1), a functional marker of immune cell activation and cytotoxic degranulation, was upregulated in ASD blood, brain cortex, and various genetic animal models or cells using bioinformatics approaches. The prognostic value of LAMP1 was investigated by correlating its expression with clinical ASD rating scales, and the receiver operating characteristic (ROC) curve analysis in ASD also revealed that it has a favorable diagnostic ability in distinguishing ASD from control cohort. According to gene set enrichment analysis (GSEA) results, LAMP1 correlated with genes that were enriched in natural kill and T cell immune function. Taking all of the evidence into account, we discovered that abnormal elevations of LAMP1 mRNA and protein in the blood of ASD children, may influence the development of ASD through its involvement in immune cell activity regulation. This report highlights a novel marker for ASD early detection as well as potential therapeutic targets.

Identification of Autism spectrum disorder based on a novel feature selection method and Variational Autoencoder

The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI. In the framework, we proposed a novel filter feature selection method based on the difference between step distribution curves (DSDC) to select remarkable functional connectivities (FCs) and utilized a multilayer perceptron (MLP) which was pretrained by a simplified Variational Autoencoder (VAE) for classification. We also designed a pipeline consisting of a normalization procedure and a modified hyperbolic tangent (tanh) activation function to replace the classical tanh function, further improving the model accuracy. Our model was evaluated by 10 times 10-fold cross-validation and achieved an average accuracy of 78.12%, outperforming the state-of-the-art methods reported on the same dataset. Given the importance of sensitivity and specificity in disease diagnosis, two constraints were designed in our model which can improve the model’s sensitivity and specificity by up to 9.32% and 10.21%, respectively. The added constraints allow our model to handle different application scenarios and can be used broadly.

Exploring Autism, Culture, and Immigrant Experiences: Lessons from Sri Lankan Tamil Mothers.

Background: Canada is home to a mosaic of cultures with immigrant communities from a wide range of countries, but there are significant variations in how autism spectrum disorder (ASD) may be understood across different immigrant groups, including Sri Lankan Tamils. Such gaps in knowledge may present challenges for immigrant families that are trying to access appropriate care for their child, including occupational therapy services. Purpose: This descriptive qualitative study aimed to better understand the experiences of immigrant Sri Lankan Tamil parents of children diagnosed with ASD in Southern Ontario, Canada. Method: Interviews were analyzed using an in-depth content analysis. Findings: Results demonstrated parents’ perceived supports and barriers towards ASD intervention planning, and indicated that parents were generally satisfied by the level of cultural competence in current ASD systems. However, families may still experience significant immigrant-related barriers that are not fully addressed. Implications: Recommendations to improve cultural awareness among occupational therapists utilizing ASD interventions are suggested.

Jointly Composite Feature Learning and Autism Spectrum Disorder Classification Using Deep Multi-Output Takagi-Sugeno-Kang Fuzzy Inference Systems.

Autism spectrum disorder (ASD) is characterized by poor social communication abilities and repetitive behaviors or restrictive interests, which has brought a heavy burden to families and society. In many attempts to understand ASD neurobiology, resting-state functional magnetic resonance imaging (rs-fMRI) has been an effective tool. However, current ASD diagnosis methods based on rs-fMRI have two major defects. First, the instability of rs-fMRI leads to functional connectivity (FC) uncertainty, affecting the performance of ASD diagnosis. Second, many FCs are involved in brain activity, making it difficult to determine effective features in ASD classification. In this study, we propose an interpretable ASD classifier DeepTSK, which combines a multi-output Takagi-Sugeno-Kang (MO-TSK) fuzzy inference system (FIS) for composite feature learning and a deep belief network (DBN) for ASD classification in a unified network. To avoid the suboptimal solution of DeepTSK, a joint optimization procedure is employed to simultaneously learn the parameters of MO-TSK and DBN. The proposed DeepTSK was evaluated on datasets collected from three sites of the Autism Brain Imaging Data Exchange (ABIDE) database. The experimental results showed the effectiveness of the proposed method, and the discriminant FCs are presented by analyzing the consequent parameters of Deep MO-TSK.

The Role of Intelligent Technologies in Early Detection of Autism Spectrum Disorder (ASD): A Scoping Review

Background: Two-year delay is reported between the first developmental concern raised by the parents and the diagnosis of ASD (Autism Spectrum Disorder), delaying the start of early intervention programs most beneficial within the first three years. Aim: Evaluate the role of technology in ASD detection by answering four research questions analyzing 1) evolution of technology, 2) use of various bio-behavioral data sources, 3) demographic categories, databases, controls, comparators, and assessment instruments, and 4) data collection, processing, and outcomes of the technology-based methods in ASD detection. Methods: Scoping review included behavioral-based ASD screening and diagnostic studies, published between 1st January 2011 to 31st December 2021 in PUBMED, SCOPUS, and IEEE Xplore databases for children under six years. The studies were assessed using the Critical Appraisal Skills Programm (CASP) and the PRISMA scoping review checklist (PRISMA-ScR). Results: The shortlisted 35 studies were categorized into seven bio-behavioral categories. The review suggested extensive use of machine learning (ML) and Deep Learning (DL) technologies with multimodal structured and unstructured data to detect infants at risk of ASD and Other developmental delays (ODD) as early as 9 to 12 months. However, the review reported various internal and external validity threats. Conclusion: Technology can significantly improve the current ASD detection process. The validation and adoption of technology can be fast-tracked by 1) designing robust study protocols, 2) executing multi-cultural field trials, 3) standardizing datasets, data quality and feature engineering methods, 4) recruiting statistically significant participants from ASD, typically developing (TD) and other developmental disorders (ODD) groups to ensure technological generalization, validation, and adoption outside laboratory settings.

The Brainstem-Informed Autism Framework: Early Life Neurobehavioral Markers.

Autism spectrum disorders (ASD) have long-term implications on functioning at multiple levels. In this perspective, we offer a brainstem-informed autism framework (BIAF) that traces the protracted neurobehavioral manifestations of ASD to early life brainstem dysfunctions. Early life brainstem-mediated markers involving functions of autonomic/arousal regulation, sleep-wake homeostasis, and sensorimotor integration are delineated. Their possible contributions to the early identification of susceptible infants are discussed. We suggest that the BIAF expands our multidimensional understanding of ASD by focusing on the early involvement of brainstem systems. Importantly, we propose an integrated BIAF screener that brings about the prospect of a sensitive and reliable early life diagnostic scheme for weighing the risk for ASD. The BIAF screener could provide clinicians substantial gains in the future and may carve customized interventions long before the current DSM ASD phenotype is manifested using dyadic co-regulation of brainstem-informed autism markers.

Understanding autism spectrum disorders with animal models: applications, insights, and perspectives.

Autism spectrum disorder (ASD) is typically characterized by common deficits in social skills and repetitive/stereotyped behaviors. It is widely accepted that genetic and environmental factors solely or in combination cause ASD. However, the underlying pathogenic mechanism is unclear due to its highly heterogeneous nature. To better understand the pathogenesis of ASD, various animal models have been generated, which can be generally divided into genetic, environment-induced, and idiopathic animal models. In this review, we summarize the common animals used for ASD study and then discuss the applications, clinical insights, as well as challenges and prospects of current ASD animal models.

De Novo Variants in Chinese Asd Trios Reveal Distinct Genetic Basis Underlying Autism with and Without Developmental Delay

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder causing social communication and stereotypical behavioral impairments, often with developmental delay or intellectual disabilities (DD/ID). Here, we performed whole-exome sequencing on 772 Chinese ASD trios, combining with a previous 369 ASD trios, to identify de novo variants in 1141 ASD trios. We found that ASD without DD/ID carried less disruptive de novo variants than ASD with DD/ID. Surprisingly, we found that expression of genes with de novo variants in ASD without DD/ID were enriched in a subtype of human neural progenitor cells. Importantly, some ASD risk genes identified in this study are not present in the current ASD gene database, suggesting that there are ubique genetic contributors to ASD in East Asian populations. Together, this work nominates novel ASD risk genes and indicates that ASD genetic studies in different geographic populations are essential to reveal the comprehensive genetic architecture of ASD.

Perceptual inference is impaired in individuals with ASD and intact in individuals who have lost the autism diagnosis

Beyond the symptoms which characterize their diagnoses, individuals with autism spectrum disorder (ASD) show enhanced performance in simple perceptual discrimination tasks. Often attributed to superior sensory sensitivities, enhanced performance may also reflect a weaker bias towards previously perceived stimuli. This study probes perceptual inference in a group of individuals who have lost the autism diagnosis (LAD); that is, they were diagnosed with ASD in early childhood but have no current ASD symptoms. Groups of LAD, current ASD, and typically developing (TD) participants completed an auditory discrimination task. Individuals with TD showed a bias towards previously perceived stimuli—a perceptual process called “contraction bias”; that is, their representation of a given tone was contracted towards the preceding trial stimulus in a manner that is Bayesian optimal. Similarly, individuals in the LAD group showed a contraction bias. In contrast, individuals with current ASD showed a weaker contraction bias, suggesting reduced perceptual inferencing. These findings suggest that changes that characterize LAD extend beyond the social and communicative symptoms of ASD, impacting perceptual domains. Measuring perceptual processing earlier in development in ASD will tap the causality between changes in perceptual and symptomatological domains. Further, the characterization of perceptual inference could reveal meaningful individual differences in complex high-level behaviors.

6.7 AUTISM SPECTRUM DISORDER–FOCUSED AUGMENTED REALITY SMARTGLASSES SYSTEM FOR ENHANCING ENGAGEMENT AND PARENT-CHILD INTERACTION

Autism spectrum disorder (ASD) is a prevalent childhood-onset neurodevelopmental disorder. The current ASD interventions are primarily based on in-person behavioral therapies that are both costly and difficult to access, and are targeting ASD deficits in social communication, and the presence of stereotypies and other ASD-related behaviors. This secondary analysis evaluates the impact of ASD-focused augmented-reality Brain Power Smartglasses (BPS) system on engagement and parent-child interaction of children with higher-functioning ASD.

From Kanner Austim to Asperger Syndromes, the Difficult Task to Predict Where ASD People Look at

Modelling the visual attention of people with autism spectrum disorder (ASD) is attracting more and more interest. This consists in determining where ASD people look and in inferring the important visual features contributing to the gaze deployment. In this article, we investigate whether or not existing neurotypical as well as ASD saliency models perform well over the whole spectrum of autism. For this purpose, we propose two new eye-tracking datasets of ASD people in order to cover a large part of the autism spectrum, going from high-level functioning (e.g. Asperger) to low-level functioning (e.g. Kanner) autism. We demonstrate that current neurotypical and ASD models do not generalize well and perform well only on a small part of the spectrum. Our objective is to raise the awareness of computer scientists to the difficult task we are facing up when it comes to simulate the gaze deployment of ASD people.

Pre- and Paralinguistic Vocal Production in ASD: Birth Through School Age.

We review what is known about how pre-linguistic vocal differences in autism spectrum disorder (ASD) unfold across development and consider whether vocalization features can serve as useful diagnostic indicators. Differences in the frequency and acoustic quality of several vocalization types (e.g., babbles and cries) during the first year of life are associated with later ASD diagnosis. Paralinguistic features (e.g., prosody) measured during early and middle childhood can accurately classify current ASD diagnosis using cross-validated machine learning approaches. Pre-linguistic vocalization differences in infants are promising behavioral markers of later ASD diagnosis. In older children, paralinguistic features hold promise as diagnostic indicators as well as clinical targets. Future research efforts should focus on (1) bridging the gap between basic research and practical implementations of early vocalization-based risk assessment tools, and (2) demonstrating the clinical impact of targeting atypical vocalization features during social skill interventions for older children.

Racial and Ethnic Disparities in Autism-Related Health and Educational Services.

The objective of this study is to measure racial and ethnic disparities in autism-related services among U.S. children with parent-reported autism spectrum disorder (ASD). Using the 2011 Survey of Pathways to Diagnosis and Services, we analyzed parent-reported data on 1420 children with current ASD in the nationally representative sample. An estimation method consistent with the Institute of Medicine's definition of health care disparities is used to measure racial and ethnic disparities. The findings revealed Latino-white disparities in the percentage of children currently using school-based occupational and physical therapy and Latino-white and "other race"-white disparities in the percentage of children using physical therapy outside of school. There were no statistically significant black-white disparities. Instead, the study found that the percentage of black children with ASD receiving school-based services was 8 points higher than that of white children (p < 0.04). Factors unrelated to the need for autism services were largely unassociated with the receipt of services. The findings provide a partial baseline and identify a need for further examination of the source of existing disparities and the lack of disparities found for specific services and minority groups.

Missing Components in Current Management of Autism Spectrum Disorder (ASD): Nutrition, Dental Care, and House-Call Programs

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder and systemic disease with multiple comorbidities and largely fragmented clinical care. In this article, we discuss three important components frequently missing from the current ASD standard care routine: oral health, nutrition, and house-call programs. Both ASD centers associated with tertiary-care hospitals and community ASD providers do not regularly offer these services. In this review, we address the benefits of and rationale behind incorporating dental care, nutrition, and house-call programs into ASD management. We also explain why these three services are closely intertwined, with potentially synergistic effects to improve health care outcomes for patients with ASD. Finally, we discuss strategies for service implementation and envision ways in which these three branches of ASD care can be best integrated into a primary care routine.

Predictors of Caregivers' Satisfaction with the Management of Children with Autism Spectrum Disorder: A Study at Multiple Levels of Health Care.

Background: Caregivers are the initial gatekeepers in the health care management of children with autism spectrum disorder (ASD). Methods: This cross-sectional study aimed to determine the factors associated with caregivers’ satisfaction with different levels of health care services in managing children with ASD in Kelantan. The satisfaction scores of 227 main caregivers of confirmed ASD children were assessed with a modified Parent Satisfaction Scale (PSS) questionnaire. Results: The analysis showed that caregivers who waited longer for a doctor’s consultation in primary care had a reduced PSS score, whereas caregivers who were satisfied with the waiting time in primary care had higher PSS scores. At the secondary care level, caregivers who possessed at least a diploma had reduced PSS scores, whereas caregivers who were satisfied with both doctors’ consultation times and occupational therapy appointments had higher PSS scores. At the tertiary care level, caregivers with an underlying medical problem and who had children undergoing occupational therapy for two months or more had reduced PSS scores. Nevertheless, the analysis showed that caregivers who were concerned with their children’s sleeping problems, who had been informed about parental support, who were satisfied with speech and occupational therapy appointments, who were satisfied with waiting times at tertiary care clinics, and who were satisfied with their doctor’s knowledge and experience had higher PSS scores. Conclusions: This study elucidated the importance of understanding caregivers’ satisfaction in attaining care for their ASD children and highlighted the need to promote factors that would increase caregivers’ satisfaction with current ASD services.

Caregiver Daily Reporting of Symptoms in Autism Spectrum Disorder: Observational Study Using Web and Mobile Apps.

BackgroundCurrently, no medications are approved to treat core symptoms of autism spectrum disorder (ASD). One barrier to ASD medication development is the lack of validated outcome measures able to detect symptom change. Current ASD interventions are often evaluated using retrospective caregiver reports that describe general clinical presentation but often require recall of specific behaviors weeks after they occur, potentially reducing accuracy of the ratings. My JAKE, a mobile and Web-based mobile health (mHealth) app that is part of the Janssen Autism Knowledge Engine—a dynamically updated clinical research system—was designed to help caregivers of individuals with ASD to continuously log symptoms, record treatments, and track progress, to mitigate difficulties associated with retrospective reporting.ObjectiveMy JAKE was deployed in an exploratory, noninterventional clinical trial to evaluate its utility and acceptability to monitor clinical outcomes in ASD. Hypotheses regarding relationships among daily tracking of symptoms, behavior, and retrospective caregiver reports were tested.MethodsCaregivers of individuals with ASD aged 6 years to adults (N=144) used the My JAKE app to make daily reports on their child’s sleep quality, affect, and other self-selected specific behaviors across the 8- to 10-week observational study. The results were compared with commonly used paper-and-pencil scales acquired over a concurrent period at regular 4-week intervals.ResultsCaregiver reporting of behaviors in real time was successfully captured by My JAKE. On average, caregivers made reports 2-3 days per week across the study period. Caregivers were positive about their use of the system, with over 50% indicating that they would like to use My JAKE to track behavior outside of a clinical trial. More positive average daily reporting of overall type of day was correlated with 4 weekly reports of lower caregiver burden made at 4-week intervals (r=–0.27, P=.006, n=88) and with ASD symptoms (r=–0.42, P<.001, n=112).ConclusionsMy JAKE reporting aligned with retrospective Web-based or paper-and-pencil scales. Use of mHealth apps, such as My JAKE, has the potential to increase the validity and accuracy of caregiver-reported outcomes and could be a useful way of identifying early changes in response to intervention. Such systems may also assist caregivers in tracking symptoms and behavior outside of a clinical trial, help with personalized goal setting, and monitoring of progress, which could collectively improve understanding of and quality of life for individuals with ASD and their families.Trial RegistrationClinicalTrials.gov NCT02668991; https://clinicaltrials.gov/ct2/show/NCT02668991

Prevalence and Treatment Patterns of Autism Spectrum Disorder in the United States, 2016

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Previous surveys have reported a steady increase in ASD prevalence in US children over the past decades. Several behavioral therapies and medications have been developed to treat the symptoms of ASD; however, little is known about the current status of treatment usage for children diagnosed as having ASD. To estimate the prevalence and treatment patterns of ASD among US children using nationally representative data. This study used data from the 2016 National Survey of Children's Health, a nationwide, population-based, cross-sectional survey. We included 43 032 children aged 3 to 17 years. Data were collected through questionnaires completed by a parent or guardian. Data were analyzed from February 2018 to March 2018. Outcome variables included ASD diagnosed by a physician or health professional and the use of behavioral treatment or medication treatment among children with ASD. Of the 43 032 included participants, 22 072 (51.3%) were male, and the mean (SD) age was 10.7 (4.4) years. The weighted prevalence of ever-diagnosed ASD and current ASD were 2.79% (95% CI, 2.46-3.12) and 2.50% (95% CI, 2.21-2.79), respectively. The state-level prevalence of ever-diagnosed ASD varied from 1.54% (95% CI, 0.60-2.48) in Texas to 4.88% (95% CI, 2.72-7.05) in Florida. Nationally, about 70% of children with current ASD (70.5%; 95% CI, 65.1-75.8) were treated; 43.3% (95% CI, 37.4-49.2) received behavioral treatment only, 6.9% (95% CI, 3.7-10.1) received medication treatment only, and 20.3% (95% CI, 16.5-24.1) received both behavioral and medication treatments. The remaining 29.5% (95% CI, 24.2-34.9) of children with current ASD did not receive either behavioral or medication treatment. This study showed that the prevalence of ASD in the United States was relatively high, and it varied substantially across US states. Almost 30% of US children with ASD did not receive behavioral or medication treatment, which calls for a critical need to understand and address the barriers for those children to receive appropriate treatments.

The Prevalence of Parent-Reported Autism Spectrum Disorder Among US Children.

The 2016 NSCH is a nationally representative survey of 50 212 children focused on the health and well-being of children aged 0 to 17 years. The NSCH collected parent-reported information on whether children ever received an ASD diagnosis by a care provider, current ASD status, health care use, access and challenges, and methods of treatment. We calculated weighted prevalence estimates of ASD, compared health care experiences of children with ASD to other children, and examined factors associated with increased likelihood of medication and behavioral treatment. Parents of an estimated 1.5 million US children aged 3 to 17 years (2.50%) reported that their child had ever received an ASD diagnosis and currently had the condition. Children with parent-reported ASD diagnosis were more likely to have greater health care needs and difficulties accessing health care than children with other emotional or behavioral disorders (attention-deficit/hyperactivity disorder, anxiety, behavioral or conduct problems, depression, developmental delay, Down syndrome, intellectual disability, learning disability, Tourette syndrome) and children without these conditions. Of children with current ASD, 27% were taking medication for ASD-related symptoms, whereas 64% received behavioral treatments in the last 12 months, with variations by sociodemographic characteristics and co-occurring conditions. The estimated prevalence of US children with a parent-reported ASD diagnosis is now 1 in 40, with rates of ASD-specific treatment usage varying by children's sociodemographic and co-occurring conditions.

Atypical audiovisual temporal function in autism and schizophrenia: similar phenotype, different cause.

Binding across sensory modalities yields substantial perceptual benefits, including enhanced speech intelligibility. The coincidence of sensory inputs across time is a fundamental cue for this integration process. Recent work has suggested that individuals with diagnoses of schizophrenia (SZ) and autism spectrum disorder (ASD) will characterize auditory and visual events as synchronous over larger temporal disparities than their neurotypical counterparts. Namely, these clinical populations possess an enlarged temporal binding window (TBW). Although patients with SZ and ASD share aspects of their symptomatology, phenotypic similarities may result from distinct etiologies. To examine similarities and variances in audiovisual temporal function in these two populations, individuals diagnosed with ASD (n=46; controls n=40) and SZ (n=16, controls=16) completed an audiovisual simultaneity judgment task. In addition to standard psychometric analyses, synchrony judgments were assessed using Bayesian causal inference modeling. This approach permits distinguishing between distinct causes of an enlarged TBW: an a priori bias to bind sensory information and poor fidelity in the sensory representation. Findings indicate that both ASD and SZ populations show deficits in multisensory temporal acuity. Importantly, results suggest that while the wider TBWs in ASD most prominently results from atypical priors, the wider TBWs in SZ results from a trend toward changes in prior and weaknesses in the sensory representations. Results are discussed in light of current ASD and SZ theories and highlight that different perceptual training paradigms focused on improving multisensory integration may be most effective in these two clinical populations and emphasize that similar phenotypes may emanate from distinct mechanistic causes.

Secondhand smoke exposure is associated with autism spectrum disorder in US males but not in females: Results from the National Survey on Children's Health

Background: Secondhand smoke (SHS) exposure is linked with neurobehavioral disorders in children. This study evaluated the SHS exposure and autism spectrum disorders (ASD) in children in the United States. Materials and Methods: Parent-reported postnatal SHS exposure and ASD diagnosis were examined in children age 2 to 12 years using the 2011–2012 National Survey on Children's Health. The physician diagnosis of current ASD as reported by the parents was used as the outcome. Logistic regression analysis was used to evaluate the association of ASD with SHS after adjusting for risk factors. Results: Of the 56,710 children, 24% had SHS exposure, 2% had ASD, and the mean age was 7 years. SHS exposure was associated with 47% greater odds of ASD in male children (adjusted odds ratio [OR] = 1.47; 95% confidence interval [CI] =1.05, 2.07; P = 0.025). In contrast, SHS was not significantly associated with ASD in female children (adjusted OR = 0.72; 95% CI = 0.40, 1.29; P = 0.266). Other significant factors associated with ASD diagnosis in male children were age, income, mother's education, and mental health status. Conclusions: SHS is significantly associated with ASD in male children. Sociodemographic factors, natal and prenatal characteristics are important etiologic influences for ASD. Targeted efforts to change the smoking behavior of parents and caregivers of children could reduce ASD.