Search Results (57)

Background/Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows for timely intervention, which significantly improves developmental, behavioral, and communicative outcomes in children. However, traditional diagnostic procedures for identifying autism spectrum disorder (ASD) typically involve lengthy clinical examinations, which can be both time-consuming and costly. This research proposes leveraging automated machine learning (AUTOML) to streamline the diagnostic process and enhance its accuracy. Methods: In this study, by collecting data from various rehabilitation centers across Pakistan, we applied a specific AUTOML tool known as Tree-based Pipeline Optimization Tool (TPOT) for ASD detection. Notably, this study marks one of the initial explorations into utilizing AUTOML for ASD detection. The experimentations indicate that the TPOT provided the best pipeline for the dataset, which was verified using a manual machine learning method. Results: The study contributes to the field of ASD diagnosis by using AUTOML to determine the likelihood of ASD in children at prompt stages of evolution. The study also provides an evaluation of precision, recall, and F1-score metrics to confirm the correctness of the diagnosis. The propose TPOT-based AUTOML framework attained an overall accuracy 78%, with a precision of 83%, a recall of 90%, and an F1-score of 86% for the autistic class. Conclusions: In summary, this research offers an encouraging approach to improve the detection of autism spectrum disorders (ASD) in children, which could lead to better results for affected individuals and their families. Full article

Background: Neurodevelopmental disorders, including autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), are increasingly recognized as conditions arising from multifaceted interactions among genetic predisposition, environmental exposures, and epigenetic modifications. Among epigenetic mechanisms, non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and PIWI-interacting RNAs (piRNAs), have gained attention as pivotal regulators of gene expression during neurodevelopment. These RNA species do not encode proteins but modulate gene expression at transcriptional and post-transcriptional levels, thereby influencing neuronal differentiation, synaptogenesis, and plasticity. Objectives: This systematic review critically examines and synthesizes the most recent findings, particularly in the post-COVID transcriptomic research era, regarding the role of ncRNAs in the pathogenesis, diagnosis, and potential treatment of neurodevelopmental disorders. Methods: A comprehensive literature search was conducted to identify studies reporting on the expression profiles, functional implications, and clinical relevance of ncRNAs in neurodevelopmental disorders, across both human and animal models. Results: Here, we highlight that multiple classes of ncRNAs are differentially expressed in individuals with ASD and ADHD. Notably, specific miRNAs and lncRNAs demonstrate potential as diagnostic biomarkers with high sensitivity and specificity. Functional studies further reveal that ncRNAs actively contribute to pathogenic mechanisms by modulating neuronal gene networks. Conclusions: Emerging experimental data indicate that the exogenous administration of certain ncRNAs may reverse molecular and behavioral phenotypes, supporting their therapeutic promise. These findings broaden our understanding of neurodevelopmental regulation and open new avenues for personalized diagnostics and targeted interventions in clinical neuropsychiatry. Full article

Background/Objectives: Repetitive behaviors such as hand flapping, body rocking, and head shaking characterize Autism Spectrum Disorder (ASD) while functioning as early signs of neurodevelopmental variations. Traditional diagnostic procedures require extensive manual observation, which takes significant time, produces subjective results, and remains unavailable to many regions. The research introduces a real-time system for the detection of ASD-typical behaviors by analyzing body movements through the You Only Look Once (YOLOv11) deep learning model. Methods: The system’s multi-layered design integrates monitoring, network, cloud, and typical ASD behavior detection layers to facilitate real-time video acquisition, wireless data transfer, and cloud analysis along with ASD-typical behavior classification. We gathered and annotated our own dataset comprising 72 videos, yielding a total of 13,640 images representing four behavior classes that include hand flapping, body rocking, head shaking, and non_autistic. Results: YOLOv11 demonstrates superior performance compared to baseline models like the sub-sampling (CNN) (MobileNet-SSD) and Long Short-Term Memory (LSTM) by achieving 99% accuracy along with 96% precision and 97% in recall and the F1-score. Conclusions: The results indicate that our system provides a scalable solution for real-time ASD screening, which might help clinicians, educators, and caregivers with early intervention, as well as ongoing behavioral monitoring. Full article

Training people interested in implementing Applied Behavior Analysis (ABA) interventions to children with autism spectrum disorder (ASD) is important to promote skill gains. A recommended training package is called behavioral skills training (BST), which involves four components (didactic instruction, modeling, role-play, and performance feedback). Background/Objectives: The purpose was to assess the effects of BST on the accurate teaching of multiple skills via DTT by six psychology university students to a confederate and six children diagnosed with ASD. Generalization and maintenance assessments were conducted. Results: Through the research conditions, all university participants were able to teach ten different skills (sitting still, motor imitation, making requests, vocal imitation, receptive identification of non-verbal stimuli, making eye contact, following instructions, intraverbal, labeling, receptive identification of non-verbal stimuli by function, feature and class) with a high integrity level to the children. In addition, across four months after training, all participants maintained high teaching integrity levels while teaching skills to the children related to their individualized curriculum goals. Each child accumulated over 1000 correct responses across several sessions. The university participants rated their training with the highest possible score in a social validity assessment. Conclusions: BST successfully trained psychology university students to accurately teach multiple skills via DTT to children with ASD and involved long lasting effects. Limitations and new avenues for research were discussed. Full article

Typically developing peers are the key factor for children with disabilities to participate in inclusive settings. Good peer relationships can improve the social communication and language expression of children with disabilities, and typically developing children play a role as “gatekeepers” in the social activities of children with disabilities in the schools. In this study, 36 primary school students from grades 1 to 3 received online picture book teaching for 3 weeks, 6 units, 12 class hours, and 40 min per class hour with six volumes of disability picture books (including physical disability, deaf and hard of hearing, visual impairment, intellectual disability, learning disability and autism spectrum disorder) selected by experts in summer vocation. The attitudes of typically developing children toward peers with disabilities of participants were tested before and after attending the online picture book course. The teaching of disability-themed picture books online has significantly improved the attitudes of typically developing children in lower grades toward peers with disabilities. Specifically, there are significant differences in the sub-dimensions of emotion and positive behavior and negative behavior before and after the intervention. The results showed that online picture book teaching activities with disability themes can effectively improve the attitudes of typically developing children in primary schools toward children with disabilities in terms of cognition, emotion, and behavior, and they can be used in schools to create an inclusive climate for students with disabilities. Full article

Autism spectrum disorder (ASD) is a genetically heterogeneous syndrome characterized by repetitive, restricted, and stereotyped behaviors, along with persistent difficulties with social interaction and communication. Despite its increasing prevalence globally, the underlying pathogenic mechanisms of this complex neurodevelopmental disorder remain poorly understood. Therefore, the identification of reliable biomarkers could play a crucial role in enabling early screening and more precise classification of ASD subtypes, offering valuable insights into its physiopathology and aiding the customization of treatment or early interventions. Biogenic amines, including serotonin, histamine, dopamine, epinephrine, norepinephrine, and polyamines, are a class of organic compounds mainly produced by the decarboxylation of amino acids. A substantial portion of the genetic variation observed in ASD has been linked to genes that are either directly or indirectly involved in the metabolism of biogenic amines. Their potential involvement in ASD has become an area of growing interest due to their pleiotropic activities in the central nervous system, where they act as both neurotransmitters and neuromodulators or hormones. This review examines the role of biogenic amines in ASD, with a particular focus on genetic alterations in the enzymes responsible for their synthesis and degradation. Full article

With advances in digital technology, including deep learning and big data analytics, new methods have been developed for autism diagnosis and intervention. Emotion recognition and the detection of autism in children are prominent subjects in autism research. Typically using single-modal data to analyze the emotional states of children with autism, previous research has found that the accuracy of recognition algorithms must be improved. Our study creates datasets on the facial and speech emotions of children with autism in their natural states. A convolutional vision transformer-based emotion recognition model is constructed for the two distinct datasets. The findings indicate that the model achieves accuracies of 79.12% and 83.47% for facial expression recognition and Mel spectrogram recognition, respectively. Consequently, we propose a multimodal data fusion strategy for emotion recognition and construct a feature fusion model based on an attention mechanism, which attains a recognition accuracy of 90.73%. Ultimately, by using gradient-weighted class activation mapping, a prediction heat map is produced to visualize facial expressions and speech features under four emotional states. This study offers a technical direction for the use of intelligent perception technology in the realm of special education and enriches the theory of emotional intelligence perception of children with autism. Full article

Electroretinograms (ERGs) show differences between typically developing populations and those with a diagnosis of autism spectrum disorder (ASD) or attention deficit/hyperactivity disorder (ADHD). In a series of ERGs collected in ASD (n = 77), ADHD (n = 43), ASD + ADHD (n = 21), and control (n = 137) groups, this analysis explores the use of machine learning and feature selection techniques to improve the classification between these clinically defined groups. Standard time domain and signal analysis features were evaluated in different machine learning models. For ASD classification, a balanced accuracy (BA) of 0.87 was achieved for male participants. For ADHD, a BA of 0.84 was achieved for female participants. When a three-group model (ASD, ADHD, and control) the BA was lower, at 0.70, and fell further to 0.53 when all groups were included (ASD, ADHD, ASD + ADHD, and control). The findings support a role for the ERG in establishing a broad two-group classification of ASD or ADHD, but the model’s performance depends upon sex and is limited when multiple classes are included in machine learning modeling. Full article

Autism Spectrum disorders (ASD) are diagnosed more often in males than in females, by a ratio of about 3:1; this is likely to be due to a difference in risk burden between the sexes and/or to “compensatory skills” in females, that may delay the diagnosis of ASD. Identifying specific risk factors for ASD in females may be important in facilitating early diagnosis. We investigated whether HLA- class I: -A, -B, -C and class II -DRB1 alleles, which have been suggested to play a role in the development of ASD, can be considered as sex-related risk/protective markers towards the ASD. We performed HLA allele genotyping in 178 Italian children with ASD, 94 healthy siblings, and their parents. HLA allele distribution was compared between children with ASD, sex-matched healthy siblings, and a cohort of healthy controls (HC) enrolled in the Italian bone marrow donor registry. Allele transmission from parents to children with ASD and their siblings was also assessed. Our findings suggest that HLA-A*02, B*38, and C*12 alleles are more frequently carried by females with ASD compared to both HC and healthy female siblings, indicating these alleles as potential risk factors for ASD in females. Conversely, the HLA-A*03 allele was more commonly transmitted to healthy female siblings, suggesting it might have a protective effect. Additionally, the HLA-B*44 allele was found to be more prevalent in boys with ASD, indicating it is a potential risk factor for male patients. This is the first Italian study of sex-related HLA association with ASD. If confirmed, these results could facilitate early ASD diagnosis in female patients, allowing earlier interventions, which are crucial in the management of neurodevelopmental disorders. Full article

Neurodevelopmental disorders (NDDs) encompass a range of conditions that begin during the developmental stage and cause deficits that lead to disruptions in normal functioning. One class of chemicals that is of increasing concern for neurodevelopmental disorders is made up of per- and polyfluoroalkyl substances (PFAS). In this comprehensive literature review, we investigated data from epidemiological studies to understand the connection between PFAS exposure and neurodevelopmental endpoints such as cognitive function, intelligence (IQ), and memory, along with behavioral changes like Attention-Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD). When we reviewed the findings from individual studies that analyzed PFAS levels in biological samples and their association with NDD, we concluded that there was a correlation between PFAS and neurodevelopmental disorders. The findings suggest that children exposed to higher PFAS levels could potentially have an increased risk of ASD and ADHD along with an inhibitory effect on IQ. While the results vary from one study to another, there is increasing association between PFAS exposure and neurodevelopmental disorders. Importantly, the findings provide valuable insights into the adverse effects associated with PFAS exposure and neurodevelopment. Full article

Background: In addition to the inherent challenges of their condition, children with autism spectrum disorder (ASD) are also susceptible to the global obesity epidemic. However, concerning the prevalence of obesity within the Moroccan ASD pediatric population, data remain scarce. Methods: A total of 258 children (boys = 195) aged 6 to 12 years old (mean = 9.4 ± 1.4) diagnosed with ASD participated in this study. Besides the body mass and height, four significant anthropometric markers for assessing obesity were examined: body mass index (BMI), body surface area (BSA), waist circumference (WC), and waist-to-height ratio (WHtR). Each anthropometric marker was categorized into one of three cardiometabolic risk levels based on the Z-scores and their corresponding percentiles. The distribution was as follows: low risk (≤84th percentile), high risk (85th–94th percentile), and very high risk (≥95th percentile). Subsequently, a multiple regression analysis was employed to develop an algorithm that generates a composite risk score. This score incorporates all the anthropometric variables simultaneously, while also weighting their individual contributions to the cardiometabolic risk. Results: Children with ASD exhibit an anthropometric profile that markedly increases their susceptibility to cardiometabolic issues. While roughly 11% of the general Moroccan child population is overweight or obese, this figure soars to nearly 60% among children with ASD when considering the central adiposity markers. Furthermore, children from middle-class socioeconomic backgrounds display a more than threefold greater risk of developing overweight or obesity compared to their counterparts from lower socioeconomic backgrounds. Conclusions: This study has, for the first time, provided an up-to-date overview of the cardiometabolic risk in Moroccan children with ASD using traditional anthropometric measurements. The primary risk factor is clearly linked to central (abdominal) adiposity, which is recognized as the most deleterious. This study highlights the need to include general and central obesity markers. This study underscores the importance of incorporating both general and central adiposity markers for a more comprehensive assessment, and it emphasizes the need for closer monitoring within this high-risk population. Full article

The roles of perinatal development, intracellular calcium [Ca²⁺]i, and synaptogenesis disruption are not novel in the autism/ASD literature. The focus on six mechanisms controlling synaptogenesis, each regulated by [Ca²⁺]i, and each aberrant in ASDs is novel. The model presented here predicts that autism epidemic causation involves central roles of both electromagnetic fields (EMFs) and chemicals. EMFs act via voltage-gated calcium channel (VGCC) activation and [Ca²⁺]i elevation. A total of 15 autism-implicated chemical classes each act to produce [Ca²⁺]i elevation, 12 acting via NMDA receptor activation, and three acting via other mechanisms. The chronic nature of ASDs is explained via NO/ONOO(-) vicious cycle elevation and MeCP2 epigenetic dysfunction. Genetic causation often also involves [Ca²⁺]i elevation or other impacts on synaptogenesis. The literature examining each of these steps is systematically examined and found to be consistent with predictions. Approaches that may be sed for ASD prevention or treatment are discussed in connection with this special issue: The current situation and prospects for children with ASDs. Such approaches include EMF, chemical avoidance, and using nutrients and other agents to raise the levels of Nrf2. An enriched environment, vitamin D, magnesium, and omega-3s in fish oil may also be helpful. Full article

(1) Background: Psychiatrists are increasingly required to treat minimally verbal (MV) individuals with autism spectrum disorder (ASD), intellectual disability (ID) and behavior problems without much published guidance. (2) Methods: We reviewed 80 charts of MV patients managed strategically for challenging behaviors, following IRB approval. Data extracted included demographics, ASD/ID level, diagnoses, epilepsy and medications. In this descriptive study, we examined the course of assessment and treatment and made recommendations for a strategic, person-centered approach. (3) Results: Of 53 males and 27 females, mean age 34 years (range 7–76), all had ID; 75 had ASD (94%). Diagnoses included seizures in 40/80 (50%), frequent aggression (89%), self-injury (80%), attention-deficit hyperactivity disorder (ADHD) (64%) and obsessive compulsive disorder (OCD) (34%). The commonest medication classes adjusted were antiseizure medications, antipsychotics, and non-stimulant ADHD medications. (4) Conclusions: Clinical impressions suggested that this strategic psychiatric approach was beneficial, notably a review of antiseizure and all other medications for polypharmacy, behavioral and other side effects, followed by a review of possible childhood/current ADHD and a trial of low-dose non-stimulant ADHD medications if warranted. Low-dose risperidone was often effective and tolerable for irritability and self-injury. Full article

This study explores the genetic risk associations with autism spectrum disorder (ASD) using graph neural networks (GNNs), leveraging the Sfari dataset and protein interaction network (PIN) data. We built a gene network with genes as nodes, chromosome band location as node features, and gene interactions as edges. Graph models were employed to classify the autism risk associated with newly introduced genes (test set). Three classification tasks were undertaken to test the ability of our models: binary risk association, multi-class risk association, and syndromic gene association. We tested graph convolutional networks, Graph Sage, graph transformer, and Multi-Layer Perceptron (Baseline) architectures on this problem. The Graph Sage model consistently outperformed the other models, showcasing its utility in classifying ASD-related genes. Our ablation studies show that the chromosome band location and protein interactions contain useful information for this problem. The models achieved 85.80% accuracy on the binary risk classification, 81.68% accuracy on the multi-class risk classification, and 90.22% on the syndromic classification. Full article

This study investigates the psychological, educational, and technological difficulties faced by primary education students with special needs during online teaching. An interpretative phenomenological analysis was used for the qualitative analysis of data obtained through semi-structured interviews with twenty-two (22) teachers in primary education at a European country. The results revealed that their students showed negative emotions and behaviour. Those diagnosed with autism and learning disabilities had difficulty concentrating in class, while those with sensory disabilities had epileptic instances. Students with mild mental retardation in particular found it difficult to use digital tools. Many problems, however, are due to the lack of infrastructure and digital skills, as well as proper preparation of teachers for online teaching. Therefore, students and teachers should be equipped with the necessary digital skills, specialised digital tools and accessible open educational resources (OER) in order to effectively participate in online education. Full article