Autism Spectrum Disorder Research Articles

Small and Long Non-Coding RNA Analysis for Human Trophoblast-Derived Extracellular Vesicles and Their Effect on the Transcriptome Profile of Human Neural Progenitor Cells

In mice, the fetal brain is dependent upon the placenta for factors that guide its early development. This linkage between the two organs has given rise to the term, the placenta–brain axis. A similar interrelationship between the two organs may exist in humans. We hypothesize that extracellular vesicles (EVs) released from placental trophoblast (TB) cells transport small RNA and other informational biomolecules from the placenta to the brain where their contents have pleiotropic effects. Here, EVs were isolated from the medium in which human trophoblasts (TBs) had been differentiated in vitro from induced pluripotent stem cells (iPSC) and from cultured iPSC themselves, and their small RNA content analyzed by bulk RNA-seq. EVs derived from human TB cells possess unique profiles of miRs, including hsa-miR-0149-3p, hsa-302a-5p, and many long non-coding RNAs (lncRNAs) relative to EVs isolated from parental iPSC. These miRs and their mRNA targets are enriched in neural tissue. Human neural progenitor cells (NPCs), generated from the same iPSC, were exposed to EVs from either TB or iPSC controls. Both sets of EVs were readily internalized. EVs from TB cells upregulate several transcripts in NPCs associated with forebrain formation and neurogenesis; those from control iPSC upregulated a transcriptional phenotype that resembled glial cells more closely than neurons. These results shed light on the possible workings of the placenta–brain axis. Understanding how the contents of small RNA within TB-derived EVs affect NPCs might yield new insights, possible biomarkers, and potential treatment strategies for neurobehavioral disorders that originate in utero, such as autism spectrum disorders (ASDs).

Quantifying neurobehavioral profiles across neurodevelopmental genetic syndromes and idiopathic neurodevelopmental disorders.

To examine neurobehavioral findings in three genetic syndromes (PTEN hamartoma tumor syndrome, Malan syndrome [mutations in the NFIX gene], and SYNGAP1-related disorder), a mixed group of other neurodevelopmental genetic syndromes (NDGS), idiopathic neurodevelopmental disorder, and neurotypical control participants. Using a longitudinal case-control design, caregivers reported neurobehavioral information for 498 participants (PTEN hamartoma tumor syndrome n = 112, Malan syndrome n = 24, SYNGAP1-related disorder n = 47, other NDGS n = 72, idiopathic neurodevelopmental disorder n = 54, neurotypical siblings n = 74, and unrelated neurotypical control participants n = 115) at three timepoints (baseline, and 1-month and 4-month follow-ups) using the online-administered Neurobehavioral Evaluation Tool (NET). NET scales had good scale and test-retest reliability. Unique patterns of neurobehavioral findings emerged, with SYNGAP1-related disorder and Malan syndrome showing generally more severe symptom and skill patterns than for other groups of patients. Patterns could be partly accounted for by estimated cognitive level, speech level, and the presence of autism spectrum disorder. However, even when accounting for these factors, group differences remained. Reliable change indices are reported. Genetic syndromes associated with neurodevelopmental disorders present with unique neurobehavioral profiles that can inform selection of outcome measures in future clinical trials. The NET may be a useful screening and monitoring instrument in clinical practice, where frequent in-person clinic attendance is difficult for many patients.

Proteomics analysis of extracellular vesicles for biomarkers of autism spectrum disorder

BackgroundAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by symptoms that include social interaction deficits, language difficulties and restricted, repetitive behavior. Early intervention through medication and behavioral therapy can eliminate some ASD-related symptoms and significantly improve the life-quality of the affected individuals. Currently, the diagnosis of ASD is highly limited.MethodsTo investigate the feasibility of early diagnosis of ASD, we tested extracellular vesicles (EVs) proteins obtained from ASD cases. First, plasma EVs were isolated from healthy controls (HCs) and ASD individuals and were analyzed using proximity extension assay (PEA) technology to quantify 1,196 protein expression level. Second, machine learning analysis and bioinformatic approaches were applied to explore how a combination of EV proteins could serve as biomarkers for ASD diagnosis.ResultsNo significant differences in the EV morphology and EV size distribution between HCs and ASD were observed, but the EV number was slightly lower in ASD plasma. We identified the top five downregulated proteins in plasma EVs isolated from ASD individuals: WW domain-containing protein 2 (WWP2), Heat shock protein 27 (HSP27), C-type lectin domain family 1 member B (CLEC1B), Cluster of differentiation 40 (CD40), and folate receptor alpha (FRalpha). Machine learning analysis and correlation analysis support the idea that these five EV proteins can be potential biomarkers for ASD.ConclusionWe identified the top five downregulated proteins in ASD EVs and examined that a combination of EV proteins could serve as biomarkers for ASD diagnosis.

Empowering primary care physicians in child and adolescent psychiatry: a needs assessment on collaborative care in Dubai

BackgroundChild and adolescent psychiatric disorders pose significant public health concerns necessitating prompt intervention. Primary care physicians (PCPs) play a critical role as initial points of contact, facilitating early detection, management, and referral of these conditions. In Dubai, integrating mental health services into primary care faces unique challenges, highlighting the need for systemic reforms and enhanced PCP training.ObjectiveThis study investigated perceptions, barriers, and systemic challenges encountered by PCPs in managing child and adolescent psychiatric conditions in Dubai’s primary care setting. It also assessed family physicians’ involvement and preparedness in this domain.MethodsUsing a mixed-methods approach, we conducted a survey among family physicians in Dubai Health facilities and analyzed patient data from family medicine clinics. The survey evaluated formal training, preparedness, encounter frequency, and referral patterns for psychiatric care in young patients. Qualitative insights from open-ended survey questions provided additional understanding of specific challenges.ResultsFindings revealed significant gaps in formal training, with only 33.3% of respondents trained in child and adolescent psychiatry during medical education, and 21.1% participating in Continuing Medical Education (CME). A majority (54.4%) of PCPs felt unprepared to manage psychiatric care in young patients. Patient data analysis showed a predominance of male patients (59.9%) and identified Autism Spectrum Disorder (43.1%) as the most common condition, emphasizing the reliance on specialized care through a high referral rate (63.1%). Major barriers included time constraints, limited psychiatric knowledge, resistance to mental health services, systemic and structural issues, communication challenges, and resource shortages.ConclusionEnhanced training and systemic reforms are urgently needed to integrate mental health services effectively into Dubai’s primary care. Implementing structured collaborative care models, fostering interdisciplinary collaboration, and addressing systemic barriers are crucial for improving child and adolescent psychiatric condition management. These initiatives promise better patient outcomes and more efficient healthcare resource utilization.

Technology-Based Interventions for Autism Spectrum Disorder: A Systematic Review

Introduction: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. Its prevalence has steadily increased, with the CDC reporting a rise from 1 in 100 children in 2006 to 1 in 44 in the United States by 2021. ASD manifests in a spectrum of symptoms, requiring tailored interventions to address each child's unique needs. Recent advancements in technology have shown promise in aiding children with ASD, particularly through robotics and socially assistive robots (SARs). This systematic review explores the impact of technology-based interventions on children with ASD, assessing their efficacy in enhancing social skills, communication, and behavior regulation. Methodology: This review followed PRISMA guidelines, screening 46,580 articles across databases such as PubMed, Cochrane, Science Direct, Google Scholar, and PsychInfo. After applying inclusion and exclusion criteria, 254 studies were selected for in-depth review. The review focused on technology-based interventions, including robotics and socially assistive robots, with MeSH terms such as "Technology," "Artificial Intelligence," "Autism Spectrum Disorder," and "ASD." Results: Technology-based interventions, particularly SARs, demonstrated significant improvements in social interaction, emotional regulation, and communication for children with ASD. Robots such as LEGO Mindstorms and "Kaspar" were effective in enhancing eye contact, cooperation, and emotional recognition. However, results varied depending on the type of technology used, study design, and participant characteristics. Conclusion: While technology-based interventions hold promise for children with ASD, further research is needed to standardize intervention protocols, dosage, and long-term outcomes. These tools offer a non-invasive, scalable solution for enhancing social and cognitive skills, but variability in study designs limits definitive conclusions. Keywords: Autism Spectrum Disorder, Technology, Robotics, Socially Assistive Robots, Communication, Cognitive Skills, Applied Behavioral Analysis

Clinical phenotype and genetic analysis of a child with partial duplication of 10q and a literature review

To explore the clinical phenotype and pathogenesis of a child with partial duplication in the long arm of chromosome 10 (10q), and conduct a review of relevant literature. A child presented at Lianyungang Maternal and Child Health Care Hospital in April 2018 for growth retardation, intellectual disability, and autism spectrum disorder (ASD) was selected as the study subject. Peripheral blood samples were collected from the child and his parents for G-banded chromosomal karyotyping analysis. Genomic DNA was also extracted for chromosomal microarray analysis (CMA). The clinical phenotype and relevant genes were searched in the Online Mendelian Inheritance in Man (OMIM) and the UK Database of Genomic Variation and Phenotype in Humans using Ensembl Resources (DECIPHER). The pathogenicity of chromosomal variation was analyzed based on guidelines from the American College of Medical Genetics and Genomics (ACMG). Relevant literature was searched from the CNKI, Wanfang Data, and PubMed databases by using keywords such as "10q" "duplication" and "trisomy", with the time set as from the establishment of database to December 1, 2023. This study has been approved by the Medical Ethics Committee of the Lianyungang Maternal and Child Health Care Hospital (No. XM2023030). The clinical phenotype of child had included growth retardation, intellectual disability, and ASD. G-banded chromosomal analysis suggested that the child has a karyotype of 46,XY,dup(10)(q23.31q24.33), whilst both of his parents were normal. CMA analysis of the child revealed that the child was arr[19]10q23.31q24.33(87603382_104948862)×3, with a 17.34 Mb duplication in the 10q23.31q24.33 region. Search of the OMIM database suggested that the duplicated segment has contained 171 genes associated with various diseases, and search of the DECIPHER database has identified cases with overlapping with the duplication. A search of the PubMed database has identified 2 publications involving 2 patients with chromosomal duplications overlapping the 10q23.31q24.33 region with a segment length of > 10 Mb. The 2 patients had mainly manifested growth retardation, intellectual disability, ASD, and facial and limb malformations. The main pathogenic genes had included PTEN, WNT8B, LZTS2, NFKB2, PAX2, KIF11, FRA10AC1, and CNNM2. No similar case was retrieved from the CNKI and Wanfang Data databases. The partial 10q duplication as a novel CNV involving genes such as PTEN and WNT8B probably underlay the growth retardation, intellectual disability and ASD in child 1 . This study has enriched the genotype-phenotype spectrum of patients with partial 10q23.31q24.33 duplications.

A deep learning model of dorsal and ventral visual streams for DVSD

Artificial intelligence (AI) methods attempt to simulate the behavior and the neural activity of the brain. In particular, Convolutional Neural Networks (CNNs) offer state-of-the-art models of the ventral visual stream. Furthermore, no proposed model estimates the distance between objects as a function of the dorsal stream. In this paper, we present a quantitatively accurate model for the visual system. Specifically, we propose a VeDo-Net model that comprises both ventral and dorsal branches. As in the ventral visual stream, our model recognizes objects. The model also locates and estimates the distance between objects as a spatial relationship task performed by the dorsal stream. One application of the proposed model is in the simulation of visual impairments. In this study, however, we show how the proposed model can simulate the occurrence of dorsal stream impairments such as Autism Spectrum Disorder (ASD) and cerebral visual impairment (CVI). In the end, we explore the impacts of learning on the recovery of the synaptic disruptions of the dorsal visual stream. Results indicated a direct relationship between the positive and negative changes in the weights of the dorsal stream’s last layers and the output of the dorsal stream under an allocentric situation. Our results also demonstrate that visual–spatial perception impairments in ASD may be caused by a disturbance in the last layers of the dorsal stream.

Health Disparities in Hospitalized Pediatric Patients with Autism Spectrum Disorder and COVID-19

Background/Objectives: Pediatric patients with autism spectrum disorder (ASD) face unique challenges, especially amongst individuals from historically minoritized racial groups. ASD has also been associated with an increased mortality from COVID-19. This study aims to explore the differences in sociodemographic factors and health outcomes (as measured by length of stay) amongst hospitalized pediatric patients with COVID-19 infections and a diagnosis of ASD compared to individuals with a COVID-19 infection alone; Methods: We performed a retrospective cohort study examining pediatric patients (ages birth to 21) who were hospitalized with a diagnosis of ASD and COVID-19 compared to patients with a diagnosis of COVID-19 alone between January 2019 and June 2023 using Epic Systems Corporation’s Cosmos, a de-identified dataset aggregated from electronic health record data. We examined differences in demographic factors and length of stay (LOS) between groups by utilizing chi-square and Wilcoxon rank sum tests. Multiple logistic regression models were utilized to assess the association between length of stay and diagnosis; Results: A total of 21,708 distinct pediatric patients with a diagnosis of ASD and COVID-19 or COVID-19 alone were included in the analytical dataset. Patients with ASD and COVID-19, compared to patients with COVID-19 alone, had a higher proportion of individuals identifying as male and White. Patients with COVID-19 alone, compared to individuals with ASD and COVID-19, had higher proportions of individuals identifying as Black or African American. Higher proportions of individuals with ASD and COVID-19 had public insurance, compared to individuals with COVID-19 alone. Having a diagnosis of ASD and COVID, after controlling for covariates, was associated with higher odds of having a length of stay greater than the three days (cutoff value determined by the median LOS of three days) compared to having a diagnosis of COVID alone (aOR 1.19, 95% CI 1.04–1.35); Conclusions: Our study highlights the health disparities experienced during hospitalizations by pediatric patients with ASD and COVID-19. Further studies should address barriers and support health outcomes for pediatric patients with ASD.

Educational Mobile Apps Enhancing Communication Skills in Children With Autism in Arab Countries

This exploratory literature review investigates the landscape of educational mobile applications designed for children with autism spectrum disorder (ASD) in Arab countries, with a specific focus on their efficacy in improving communication skills. After a comprehensive search of academic databases and digital libraries, nine studies were reviewed. These studies discussed seven Arabic-language applications designed for ASD children. Consistent with previous research findings, these applications were found to be effective in enhancing communication skills among ASD individuals. By shedding light on the few existing Arabic-language educational mobile applications for ASD children in Arab countries, this comprehensive review contributes significantly to the discourse surrounding ASD interventions. Additionally, it highlights the immediate need for linguistically and culturally sensitive educational applications, designed for a specific population of Arab children with ASD.

An IoT-Based Framework for Automated Assessing and Reporting of Light Sensitivities in Children with Autism Spectrum Disorder

Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively. The primary objective of this work is to develop an automated system using Internet of Things (IoT), computer vision, and data mining techniques for assessing visual sensitivities specifically associated with light (color and illumination). For this purpose, an Internet of Things (IoT)-based light sensitivities assessing system (IoT-LSAS) was designed and developed using a visual stimulating device, a bubble tube (BT). The IoT-LSAS integrates various electronic modules for (i) generating colored visual stimuli with different illumination levels and (ii) capturing images to identify children’s emotional responses during sensory stimulation sessions. The system is designed to operate in two different modes: a child control mode (CCM) and a system control mode (SCM). Each mode uses a distinct approach for assessing light sensitivities, where CCM uses a preference-based approach, and SCM uses an emotional response tracking approach. The system was tested on a sample of 20 children with ASD, and the results showed that the IoT-LSAS effectively identified light sensitivities, with a 95% agreement rate in the CCM and a 90% agreement rate in the SCM when compared to the practitioner’s assessment report. These findings suggest that the IoT-LSAS can be used as an alternative to traditional assessment methods for diagnosing light sensitivities in children with ASD, significantly reducing the practitioner’s time required for diagnosis.

Parental views on language choice and its impact on bilingual families of children with autism spectrum disorder: a qualitative study

ABSTRACT India is known for its linguistic and cultural diversity, and is one amongst the most multilingual countries of the world. Yet only little is known about bilingualism in families of children with autism spectrum disorder (ASD). This study, thus, aimed to explore the parental views on language choice and their impact on bilingual families of children with ASD, in India. The study employed a descriptive qualitative research design using in-depth interviews on 12 parents of children with ASD having English as their second language. The data obtained was subjected to inductive thematic analysis. Results revealed four major themes and corresponding subthemes, namely, parental opinion on language approach, language choice and practice, factors determining language choice, and their impact. Parents presented mixed views on bilingualism. Despite having positive opinion regarding bilingualism, parents demonstrated a mismatch between the opinion on language choice and practice. Although parents wish to practice bilingualism, several factors influenced their use of languages with their children with ASD. Some of these factors involved the child’s ability, family factors, the role of English and advice received from professionals and fellow parents. The parental language practice showed impact on the family, education, and intervention of the child. Results of our study support the existing literature and highlight specific factors supporting bilingualism in the Indian context.

Low-intensity ABA intervention for young children with ASD in Italy

ABSTRACT This study investigated the effects of a 12-hr per week education package for 7 children with autism spectrum disorders (ASD) within the context of the Italian public health system. Different from the US, the Italian public health system traditionally offers 1 to 3 hrs per week of speech, occupational, or physical therapy, and/or ABA-based activity for children with ASD. Hence, it is imperative to assess the criteria for effective ABA interventions to enable comparison with various models of care for children with ASD. We employed a treatment package based on the Comprehensive Application of Behavior Analysis to Schooling (CABAS®) model. Each participant received interventions for 4 hours a day, 3 days a week for one year. The results revealed a significant difference in the total scores and each sub-test score of both the Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) and the Childhood Autism Rating Scale-Second Edition (CARS-2) before and after one year of instruction. Our low-intensity intervention appears to effectively improve the participants’ various academic, verbal, and self-management skills, positively impacting their overall development. Our findings provide insights into testing the effectiveness of an alternative model of ABA intervention while also expanding the application of advanced ABA practices to enhance the developmental trajectory of young children.

Neurexin facilitates glycosylation of Dystroglycan to sustain muscle architecture and function in Drosophila

Neurexin, a molecule associated with autism spectrum disorders, is thought to function mainly in neurons. Recently, it was reported that Neurexin is also present in muscle, but the role of Neurexin in muscle is still poorly understood. Here, we demonstrate that the overexpression of Neurexin in muscles effectively restored the locomotor function of Drosophila neurexin mutants, while rescuing effects are observed within the nervous. Notably, the defects in muscle structure and function caused by Neurexin deficiency were similar to those caused by mutations in dystroglycan, a gene associated with progressive muscular dystrophy. The absence of Neurexin leads to muscle attachment defects, emphasizing the essential role of Neurexin in muscle integrity. Furthermore, Neurexin deficiency reduces Dystroglycan glycosylation on the cell surface, which is crucial for maintaining proper muscle structure and function. Finally, Neurexin guides Dystroglycan to the glycosyltransferase complex through interactions with Rotated Abdomen, a homolog of mammalian POMT1. Our findings reveal that Neurexin mediates muscle development and function through Dystroglycan glycosylation, suggesting a potential association between autism spectrum disorders and muscular dystrophy.

Correction: A new hybrid reasoning model based on rules, cases and processes: application to care of individuals facing autism spectrum disorders

Correction: A new hybrid reasoning model based on rules, cases and processes: application to care of individuals facing autism spectrum disorders

Neuroanatomical basis of language ability in an autism subgroup with moderate language deficits.

Children with autism spectrum disorder (ASD) are highly heterogenous in their language abilities. A number of studies have shown neural correlates of language deficits in children with ASD, but the underlying neuroanatomical foundation of early language deficits in ASD remains largely elusive. In this study, we analyzed MRI data from a cohort of Chinese children with ASD (n = 67) and typical development (TD, n = 37) aged 1.5 to 6.5 years. The ASD sample was classified into two subgroups based on the median of the language scores: ASD with moderate language deficits (ASDmoderate, n = 34) and ASD with severe language deficits (ASDsevere, n = 34). We tested the group differences in the brain volumes between TD and two ASD subgroups, and also examined the associations between cortical grey matter volume and language abilities in TD and ASD subgroups, separately. We observed significant group differences in grey matter and white matter volume, with post-hoc analyses specifically indicating significant differences between TD and ASDmoderate subgroup. Significant correlations between grey matter volume and language scores were observed exclusively within the ASDmoderate subgroup, including positive associations in the bilateral superior temporal gyrus, hippocampus, and left inferior parietal lobe, and negative correlations in the bilateral precuneus. These findings provide novel evidence for the neuroanatomical basis related to language ability in an ASD subgroup with moderate language deficits, and offer new insights into the heterogeneity of language deficits in children with ASD.

Research progress on melatonin, 5-HT, and orexin in sleep disorders of children with autism spectrum disorder.

Sleep disorders are among the common comorbidities of autism spectrum disorder (ASD), which not only affect the daily life and learning ability of children but may also exacerbate other symptoms of ASD, seriously impacting the quality of life of children and their families. Given this, understanding the neurobiological mechanisms of sleep disorders in children with ASD has significant research value for developing effective intervention strategies. Melatonin, 5-HT, and orexin are key neurotransmitters that regulate the sleep-wake cycle. Through in-depth analysis of the biological functions and regulatory pathways of these neurotransmitters, new perspectives may be provided for personalized treatment of sleep disorders in children with ASD. This article reviews the research progress on melatonin, 5-HT, and orexin in sleep disorders among children with autism spectrum disorder, focusing on exploring the mechanisms of these key neurotransmitters in sleep disorders of children with ASD and how they affect the sleep-wake cycle, providing a theoretical basis for improving the sleep quality of children with ASD.

Challenges, coping strategies, and sources of support available to South African parents with children with autism spectrum disorder during the COVID-19 lockdown: a qualitative study

Parenting a child with autism spectrum disorder (ASD) is known to be complex and challenging and likely to be exacerbated under conditions of stress and uncertainty. We aimed to explore how parents experienced parenting their child with ASD during the initial COVID-19 lockdown in 2020. We also aimed to identify the coping strategies and sources of support available to them during this time. Using an exploratory qualitative design within an interpretivist paradigm, we interviewed 23 parents of children with ASD between the ages of 6 and 12 years. Parents were recruited from an online support group for ASD in South Africa. Interviews were semi-structured and transcribed verbatim for thematic analysis, using ATLAS.ti. We identified five themes: (1) The experience of communicating lockdown rules and disease containment measures to children, (2) Consequences of disruption to routine, (3) Parenting and interpersonal relationships in conditions of confinement, (4) Help-seeking and sources of support, and (5) Finding ways to cope amid a crisis. The findings demonstrate that the initial COVID-19 lockdown placed parents of children with ASD under considerable stress. Disrupted routines and interrupted access to financial, psychological, social, and educational support during the initial lockdown period exacerbated parenting experiences. This study highlights the importance of providing parents of children with ASD strategies to communicate significant change and various forms of support to navigate the negative effects of routine disruptions during conditions of uncertainty and crisis.

Identifying the impact of ARHGAP and MAP gene families on autism spectrum disorders.

The rising incidence of Autism Spectrum Disorder (ASD) has become a major concern, affecting children's psychological well-being and placing a significant strain on healthcare systems. Despite its impact, the etiological mechanisms underpinning ASD remain elusive. This study leveraged dorsolateral prefrontal cortex gene data from 452 individuals of European descent, sourced from the CommonMindConsortium, and examined ASD-related gene expression data from the Gene Expression Omnibus (GEO) database (GSE18123), along with Genome-Wide Association Studies (GWAS) data from the Lundbeck Foundation Integrated Psychiatric Research and Psychiatric Genomics Consortium. Expression quantitative trait loci data were sourced from the GTExv8 database. We employed Transcriptome-Wide Association Studies (TWAS) and Weighted Gene Co-expression Network Analysis (WGCNA) to pinpoint genes within ASD-associated susceptibility gene families (ARHGAP, MAP). Four genes-ARHGAP27, MAPT, ARHGAP19, and MAP1B-were scrutinized, and their biological implications were elucidated through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-Protein Interaction (PPI) analysis and conditional analysis within the TWAS framework helped identify pivotal genes (ARHGAP27, MAPT). A subsequent verification phase involving Mendelian Randomization (MR) evaluated the potential causal links between the identified genes and ASD. The findings revealed no causal association between ARHGAP19, MAP1B, and ASD. In contrast, significant causal relationships were established for ARHGAP27 and MAPT, suggesting that ARHGAP27 may elevate ASD risk as a susceptibility gene, whereas MAPT appears to reduce the risk as a protective gene.

Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI

Background: As the demand for early and accurate diagnosis of autism spectrum disorder (ASD) increases, the integration of machine learning (ML) and explainable artificial intelligence (XAI) is emerging as a critical advancement that promises to revolutionize intervention strategies by improving both accuracy and transparency. Methods: This paper presents a method that combines XAI techniques with a rigorous data-preprocessing pipeline to improve the accuracy and interpretability of ML-based diagnostic tools. Our preprocessing pipeline included outlier removal, missing data handling, and selecting pertinent features based on clinical expert advice. Using R and the caret package (version 6.0.94), we developed and compared several ML algorithms, validated using 10-fold cross-validation and optimized by grid search hyperparameter tuning. XAI techniques were employed to improve model transparency, offering insights into how features contribute to predictions, thereby enhancing clinician trust. Results: Rigorous data-preprocessing improved the models’ generalizability and real-world applicability across diverse clinical datasets, ensuring a robust performance. Neural networks and extreme gradient boosting models achieved the best performance in terms of accuracy, precision, and recall. XAI techniques demonstrated that behavioral features significantly influenced model predictions, leading to greater interpretability. Conclusions: This study successfully developed highly precise and interpretable ML models for ASD diagnosis, connecting advanced ML methods with practical clinical application and supporting the adoption of AI-driven diagnostic tools by healthcare professionals. This study’s findings contribute to personalized intervention strategies and early diagnostic practices, ultimately improving outcomes and quality of life for individuals with ASD.

Time perception and delay discounting in the FMR1 knockout rat.

There is substantial evidence for timing (time perception) abnormalities related to developmental disabilities, particularly autism spectrum disorder. These findings have been reported in humans and nonhuman preclinical models. Our research objective was to extend that work to a genetic knockout (KO) model of fragile X/developmental disability, the FMR1KO rat. We also sought to test delay discounting in the model and assess potential relations between timing and choice behavior. Consistent with previous human and nonhuman work, we found reduced timing precision in the FMR1 KO rats. We also discovered significantly increased smaller, sooner reward choice in the FMR1 KO rats. Performance on the timing task appeared to be unrelated to performance on the choice task for both model and control rats. These results add to what has become increasingly clear: timing is disrupted in humans diagnosed with developmental disabilities and in nonhuman models designed to model developmental disabilities. Our findings are consistent with those of previous work and the first to our knowledge to show such effects in the FMR1 KO rat. We discuss the potential clinical implications and future directions surrounding potential "timing interventions" for individuals diagnosed with developmental disabilities.