Autism spectrum: parents' perspectives reflecting the different needs of different families.

BACKGROUND: Parents of children on the autism spectrum often face great challenges in the care of their child. Early support tailored to families' individual needs is therefore crucial for the development and quality of life of both children on the autism spectrum and their families. However, to date it is unclear whether the support available meets the parents' needs. STUDY AIM: To investigate how the system of care, support, and therapies for children on the autism spectrum is perceived by their parents. METHOD: A total of 57 parents of Swiss children on the autism spectrum participated in an online survey, and 20 of them participated in additional semi-structured interviews. RESULTS: We found that parents of children on the autism spectrum may face substantial challenges and that social support is essential. Two thirds of the participating parents reported a long and difficult diagnostic process as challenging, and 60% expressed their need for closer follow-up after diagnosis and more support. Only one third of the parents stated that they manage their everyday lives well, whereas 17.5% felt exhausted, and more than half of the parents responded that they felt challenged. One fifth indicated that they had poor family support, and half reported substantial financial challenges. At the same time, most families also emphasize how important their neurodivergent children are to the family`s life together. CONCLUSION: It is important that primary pediatricians not only initiate the diagnostic process, but also assess the different needs of the different family independent of the diagnosis and, if necessary, initiate adequate measures or guide parents to institutions in charge. Parents who do not actively express their individual needs should nevertheless be advised about support services, including financial counseling. The positive aspects mentioned by families can be emphasized and used as resources to improve their quality of life.

Urine manganese, cadmium, lead, arsenic, and selenium among autism spectrum disorder children in Kuala Lumpur.

Background: The development of autism spectrum disorder (ASD) may stem from exposure to environmental pollutants such as heavy metals. The primary objective of this study is to determine the role of heavy metals of concern such as manganese (Mn), cadmium (Cd), lead (Pb), arsenic (As), and essential trace element selenium (Se) among ASD children in Kuala Lumpur, Malaysia. Method: A total of 155 preschoolers in Kuala Lumpur between the ages 3 to 6 participated in an unmatched case-control study, comprising ASD children (n = 81) recruited from an early intervention program for autism, and 74 children without autism who were recruited from public preschools. Urine samples were collected at home, delivered to the study site, and transported to the environmental lab within 24 hours. Inductively coupled plasma mass spectrometry (ICP-MS) was applied to measure the concentration of heavy metals in the samples. Data were analysed using bivariate statistical tests (Chi-square and T-test) and logistic regression models. Result: This study demonstrated that Cd, Pb, and As urine levels were significantly greater in children without autism relative to those affected with ASD (p < 0.05). No significant difference was in the levels of Se (p = 0.659) and Mn (p = 0.875) between children with ASD and the control group. The majority of children in both groups have urine As, Pb, and Cd values lower than 15.1 µg/dL, 1.0 µg/dL, and 1.0 µg/dL, respectively which are the minimal risk values for noncarcinogenic detrimental human health effect due to the heavy metal's exposure . Factors associated with having an ASD child included being a firstborn, male, and higher parental education levels (adjusted odds ratios (aOR) > 1, p < 0.05). Conclusion: Preschoolers in this study demonstrated low levels of heavy metals in their urine samples, which was relatively lower in ASD children compared to the healthy matched controls. These findings may arise from the diminished capacity to excrete heavy metals, especially among ASD children, thereby causing further accumulation of heavy metals in the body. These findings, including the factors associated with having an ASD child, may be considered by healthcare professionals involved in child development care, for early ASD detection. Further assessment of heavy metals among ASD children in the country and interventional studies to develop effective methods of addressing exposure to heavy metals will be beneficial for future reference.

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.

The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.

Autism spectrum disorders detection based on multi-task transformer neural network.

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice.

Measuring self and informant perspectives of Restricted and Repetitive Behaviours (RRBs): psychometric evaluation of the Repetitive Behaviours Questionnaire-3 (RBQ-3) in adult clinical practice and research settgs.

BACKGROUND: Brief questionnaires that comprehensively capture key restricted and repetitive behaviours (RRBs) across different informants have potential to support autism diagnostic services. We tested the psychometric properties of the 20-item Repetitive Behaviours Questionnaire-3 (RBQ-3), a questionnaire that includes self-report and informant-report versions enabling use across the lifespan. METHOD: In Study 1, adults referred to a specialised adult autism diagnostic service (N = 110) completed the RBQ-3 self-report version, and a relative or long-term friend completed the RBQ-3 informant-report version. Clinicians completed the abbreviated version of the Diagnostic Interview for Social and Communication Disorders (DISCO-Abbreviated) with the same adults as part of the diagnostic process. For half of the assessments, clinicians were blind to the RBQ-3 ratings. We tested internal consistency, cross-informant reliability and convergent validity of the RBQ-3. In Study 2, a follow-up online study with autistic (N = 151) and non-autistic (N = 151) adults, we further tested internal consistency of the RBQ-3 self-report version. We also tested group differences and response patterns in this sample. RESULTS: Study 1 showed good to excellent internal consistency for both self- and informant-report versions of the RBQ-3 (total score, α = 0.90, ω = 0.90, subscales, α = 0.76-0.89, ω = 0.77-0.88). Study 1 also showed cross-informant reliability as the RBQ-3 self-report scores significantly correlated with RBQ-3 informant-report scores for the total score (rs = 0.71) and subscales (rs= 0.69-0.72). Convergent validity was found for both self and informant versions of the RBQ-3, which significantly correlated with DISCO-Abbreviated RRB domain scores (rs = 0.45-0.54). Moreover, the RBQ-3 scores showed significantly weaker association with DISCO -Abbreviated scores for the Social Communication domain, demonstrating divergent validity. Importantly, these patterns of validity were found even when clinicians were blind to RBQ-3 items. In Study 2, for both autistic and non-autistic groups, internal consistency was found for the total score (α = 0.82-0.89, ω = 0.81-0.81) and for subscales (α = 0.68-0.85, ω = 0.69-0.85). A group difference was found between groups. LIMITATIONS: Due to the characteristics and scope of the specialist autism diagnostic service, further testing is needed to include representative samples of age (including children) and intellectual ability, and those with a non-autistic diagnostic outcome. CONCLUSIONS: The RBQ-3 is a questionnaire of RRBs that can be used across the lifespan. The current study tested its psychometric properties with autistic adults without intellectual disability and supported its utility for both clinical diagnostic and research settings.

New advances in the diagnosis and treatment of autism spectrum disorders.

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders that affect individuals' social interactions, communication skills, and behavioral patterns, with significant individual differences and complex etiology. This article reviews the definition and characteristics of ASD, epidemiological profile, early research and diagnostic history, etiological studies, advances in diagnostic methods, therapeutic approaches and intervention strategies, social and educational integration, and future research directions. The highly heritable nature of ASD, the role of environmental factors, genetic-environmental interactions, and the need for individualized, integrated, and technology-driven treatment strategies are emphasized. Also discussed is the interaction of social policy with ASD research and the outlook for future research and treatment, including the promise of precision medicine and emerging biotechnology applications. The paper points out that despite the remarkable progress that has been made, there are still many challenges to the comprehensive understanding and effective treatment of ASD, and interdisciplinary and cross-cultural research and global collaboration are needed to further deepen the understanding of ASD and improve the quality of life of patients.

Microcephaly type 22 and autism spectrum disorder: A case report and review of literature.

INTRODUCTION: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with a multifaceted etiology. This case report explores the ischemic cryptogenic vascular dissection as a potential underlying cause of ASD. METHODS: A 9-year-old child presented with symptoms of ASD, including social interaction difficulties, repetitive behaviors, and cognitive challenges. Despite conventional ASD treatments, significant improvement was only observed after addressing an underlying ischemic cryptogenic vascular dissection identified through DCE-CT. RESULTS: Following a reconstructive treatment approach to the vascular dissection, the patient showed marked improvement in cognitive functions, social abilities, and a reduction in ASD-related symptoms whether during the perioperative period or during approximately 5-month follow-up. CONCLUSION: This case suggests that ischemic cryptogenic vascular dissection may contribute to the symptoms of ASD. Identifying and treating underlying vascular anomalies may offer a new avenue for mitigating ASD symptoms, emphasizing the need for comprehensive diagnostic estimations in ASD management.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Social communication and restricted, repetitive behavior as assessed with a diagnostic tool for autism (ADOS-2) in women with anorexia nervosa.

OBJECTIVE: In anorexia nervosa (AN), the traits of autism spectrum disorder (ASD) are associated with poor outcomes. However, the subtle nature of these characteristics remains poorly understood. We investigated the in-depth patterns of ASD traits using Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) in women with AN. METHODS: Of 28 women with ICD-10 AN, 16 (age 19-30 years) participated in the ADOS-2, a video-recorded, semistructured diagnostic assessment for social communication and interaction and restricted, repetitive behaviors and interests related to ASD. None of the participants had previously been diagnosed with ASD. Other measurements included the Eating Disorder Examination Questionnaire and the Wechsler Abbreviated Scale of Intelligence-IV. RESULTS: Five individuals (18% of all, 31% of those assessed) scored above the cutoff for autism in ADOS-2. They had challenges in social communication and interaction, manifesting as sustained difficulties in social relationships and deficits in conversation skills. Few described being frequently misunderstood by others, including in the eating disorder treatment settings. Three individuals showed prominent restricted and repetitive behaviors such as ritual seeking, eating-related routines, sensory sensitivity related to food texture and selective eating, and intense interest in specific topics. The mean duration of AN in women above the cutoff was twice as long compared with those below (12.3 vs. 6.2 years). DISCUSSION: The ASD-related characteristics and behavior appear to contribute to the manifestation and duration of AN in a subgroup of women. Among these women, the traits of ASD appear to be mixed with eating disorder symptoms, which should be taken into account in the treatment.

A study of the effects of screen exposure on the neuropsychological development in children with autism spectrum disorders based on ScreenQ.

PURPOSE: To investigate the relationship between multi-dimensional aspects of screen exposure and autistic symptoms, as well as neuropsychological development in children with ASD. METHODS: We compared the ScreenQ and Griffiths Development Scales-Chinese Language Edition (GDS-C) of 636 ASD children (40.79 ± 11.45 months) and 43 typically developing (TD) children (42.44 ± 9.61 months). Then, we analyzed the correlations between ScreenQ and Childhood Autism Rating Scale (CARS), and GDS-C. We further used linear regression model to analyze the risk factors associated with high CARS total scores and low development quotients (DQs) in children with ASD. RESULTS: The CARS of children with ASD was positively correlated with the ScreenQ total scores and "access, frequency, co-viewing" items of ScreenQ. The personal social skills DQ was negatively correlated with the "access, frequency, content, co-viewing and total scores" of ScreenQ. The hearing-speech DQ was negatively correlated with the "frequency, content, co-viewing and total scores" of ScreenQ. The eye-hand coordination DQ was negatively correlated with the "frequency and total scores" of ScreenQ. The performance DQ was negatively correlated with the "frequency" item of ScreenQ. CONCLUSION: ScreenQ can be used in the study of screen exposure in children with ASD. The higher the ScreenQ scores, the more severe the autistic symptoms tend to be, and the more delayed the development of children with ASD in the domains of personal-social, hearing-speech and eye-hand coordination. In addition, "frequency" has the greatest impact on the domains of personal social skills, hearing-speech, eye-hand coordination and performance of children with ASD.

A hybrid CNN-SVM model for enhanced autism diagnosis.

Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an individual's behavior and performance, potentially co-occurring with other mental illnesses. Consequently, an effective diagnostic approach proves to be invaluable in both therapeutic interventions and the timely provision of medical support. Currently, most scholars' research primarily relies on neuroimaging techniques for auxiliary diagnosis and does not take into account the distinctive features of autism's social impediments. In order to address this deficiency, this paper introduces a novel convolutional neural network-support vector machine model that integrates resting state functional magnetic resonance imaging data with the social responsiveness scale metrics for the diagnostic assessment of autism. We selected 821 subjects containing the social responsiveness scale measure from the publicly available Autism Brain Imaging Data Exchange dataset, including 379 subjects with autism spectrum disorder and 442 typical controls. After preprocessing of fMRI data, we compute the static and dynamic functional connectivity for each subject. Subsequently, convolutional neural networks and attention mechanisms are utilized to extracts their respective features. The extracted features, combined with the social responsiveness scale features, are then employed as novel inputs for the support vector machine to categorize autistic patients and typical controls. The proposed model identifies salient features within the static and dynamic functional connectivity, offering a possible biological foundation for clinical diagnosis. By incorporating the behavioral assessments, the model achieves a remarkable classification accuracy of 94.30%, providing a more reliable support for auxiliary diagnosis.

Diagnosis of Autism Spectrum Disorder by Dynamic Local Graph-Theory Indicators Based on Electroencephalogram.

BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopment disease characterized by impaired social and cognitive abilities. Despite its prevalence, reliable biomarkers for identifying individuals with ASD are lacking. Recent studies have suggested that alterations in the functional connectivity of the brain in ASD patients could serve as potential indicators. However, previous research focused on static functional-connectivity analysis, neglecting temporal dynamics and spatial interactions. To address this gap, our study integrated dynamic functional connectivity, local graph-theory indicators, and a feature-selection and ranking approach to identify biomarkers for ASD diagnosis. METHODS: The demographic information, as well as resting and sleeping electroencephalography (EEG) data, were collected from 20 ASD patients and 25 controls. EEG data were pre-processed and segmented into five sub-bands (Delta, Theta, Alpha-1, Alpha-2, and Beta). Functional-connection matrices were created by calculating coherence, and static-node-strength indicators were determined for each channel. A sliding-window approach, with varying widths and moving steps, was used to scan the EEG series; dynamic local graph-theory indicators were computed, including mean, standard deviation, median, inter-quartile range, kurtosis, and skewness of the node strength. This resulted in 95 features (5 sub-bands × 19 channels) for each indicator. A support-vector-machine recurrence-feature-elimination method was used to identify the most discriminative feature subset. RESULTS: The dynamic graph-theory indicators with a 3-s window width and 50% moving step achieved the highest classification performance, with an average accuracy of 95.2%. Notably, mean, median, and inter-quartile-range indicators in this condition reached 100% accuracy, with the least number of selected features. The distribution of selected features showed a preference for the frontal region and the Beta sub-band. CONCLUSIONS: A window width of 3 s and a 50% moving step emerged as optimal parameters for dynamic graph-theory analysis. Anomalies in dynamic local graph-theory indicators in the frontal lobe and Beta sub-band may serve as valuable biomarkers for diagnosing autism spectrum disorders.

Interactions-based classification of a single microbial sample.

To address the limitation of overlooking crucial ecological interactions due to relying on single time point samples, we developed a computational approach that analyzes individual samples based on the interspecific microbial relationships. We verify, using both numerical simulations as well as real and shuffled microbial profiles from the human oral cavity, that the method can classify single samples based on their interspecific interactions. By analyzing the gut microbiome of people with autistic spectrum disorder, we found that our interaction-based method can improve the classification of individual subjects based on a single microbial sample. These results demonstrate that the underlying ecological interactions can be practically utilized to facilitate microbiome-based diagnosis and precision medicine.

Autism Spectrum Disorder Diagnoses and Congenital Cytomegalovirus.

OBJECTIVE: To examine the association between congenital cytomegalovirus (cCMV) and autism spectrum disorder (ASD) administrative diagnoses in US children. METHODS: Cohort study using 2014 to 2020 Medicaid claims data. We used diagnosis codes to identify cCMV (exposure), ASD (outcome), and covariates among children enrolled from birth through ≥4 to <7 years. Covariates include central nervous system (CNS) anomaly or injury diagnosis codes, including brain anomaly, microcephaly within 45 days of birth, cerebral palsy, epilepsy, or chorioretinitis. We used Cox proportional hazards regression models to estimate hazard ratios and 95% confidence intervals, overall and stratified by sex, birth weight and gestational age outcome (low birth weight or preterm birth), and presence of CNS anomaly or injury. RESULTS: Among 2 989 659 children, we identified 1044 (3.5 per 10 000) children with cCMV and 74 872 (25.0 per 1000) children with ASD. Of those with cCMV, 49% also had CNS anomaly or injury diagnosis codes. Children with cCMV were more likely to have ASD diagnoses (hazard ratio: 2.5; 95% confidence interval: 2.0-3.2, adjusting for birth year, sex, and region). This association differed by sex and absence of CNS anomaly or injury but not birth outcome. CONCLUSIONS: Children with (versus without) cCMV diagnoses in Medicaid claims data, most of whom likely had symptomatic cCMV, were more likely to have ASD diagnoses. Future research investigating ASD risk among cohorts identified through universal cCMV screening may help elucidate these observed associations.

The emerging role of extracellular vesicles in the diagnosis and treatment of autism spectrum disorders.

Autism spectrum disorders (ASD) are neurodevelopmental conditions characterized by restricted, repetitive behavioral patterns and deficits in social interactions. The prevalence of ASD has continued to rise in recent years. However, the etiology and pathophysiology of ASD remain largely unknown. Currently, the diagnosis of ASD relies on behavior measures, and there is a lack of reliable and objective biomarkers. In addition, there are still no effective pharmacologic therapies for the core symptoms of ASD. Extracellular vesicles (EVs) are lipid bilayer nanovesicles secreted by almost all types of cells. EVs play a vital role in cell-cell communications and are known to bear various biological functions. Emerging evidence demonstrated that EVs are involved in many physiological and pathological processes throughout the body and the content in EVs can reflect the status of the originating cells. EVs have demonstrated the potential of broad applications for the diagnosis and treatment of various brain diseases, suggesting that EVs may have also played a role in the pathological process of ASD. Besides, EVs can be utilized as therapeutic agents for their endogenous substances and biological functions. Additionally, EVs can serve as drug delivery tools as nano-sized vesicles with inherent targeting ability. Here, we discuss the potential of EVs to be considered as promising diagnostic biomarkers and their potential therapeutic applications for ASD.

Objective measurement of movement variability using wearable sensors predicts ASD outcomes in infants at high likelihood for ASD and ADHD.

Early motor delays and differences are common among children with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). Yet, little work has shown whether there are early atypical motor signs that differentiate these groups. Quantitative measures of movement variability hold promise for improving the identification of subtle and specific differences in motor function among infants and toddlers at high likelihood for ASD and ADHD. To this end, we created a novel quantitative measure of movement variability (movement curvature) and conducted a preliminary investigation as to whether this measure improves outcome predictions. We used a wearable triaxial accelerometer to evaluate continuous motion-based activity in infants at high and low likelihood for ASD and ADHD at 12, 18, 24, and 36 months of age. At 36 months, participants were categorized into three outcome groups: ASD (n = 19), ADHD concerns (n = 17), and a comparison group (n = 82). We examined group differences in movement curvature and whether movement curvature is predictive of a later ASD or ADHD concerns classification. We found that movement curvature was significantly lower in infants with later ASD diagnosis at 18, 24, and 36 months of age compared to infants with either ADHD concerns or those in the comparison group. Movement curvature was also a significant predictor of ASD at 18, 24, and 36 months (AUC 0.66-0.71; p = 0.005-0.039) and when adjusting for high ASD likelihood at 18 and 24 months (AUC 0.90, p = 0.05-0.019). These results indicate that lower movement curvature may be a feature of early motor differences in infants with later ASD diagnosis as early as 18 months of age.