Autism Spectrum Disorder Research Research Articles

Causal effects of 731 immune cell phenotypes on autism spectrum disorder: a Mendelian randomization study

ObjectiveThe role of different immune cells in autism spectrum disorders (ASD) is still controversial. The purpose of this study was to evaluate the causal effects of different immune cell phenotypes on ASD via Mendelian randomization (MR).MethodsDatasets of immune cell phenotypes were obtained from the European Bioinformatics Institute, and datasets of ASD were obtained from the IEU Open GWAS project. Single nucleotide polymorphisms were selected based on the assumptions of association, independence, and exclusivity. Inverse variance weighted was utilized as the main method for MR analysis. MR-Egger was employed to assess the horizontal pleiotropy of the results. Cochran’s Q and leave-one-out method were used for heterogeneity analysis and sensitivity analysis of the results, respectively.ResultsMR analysis showed that TD CD8br AC [odds ratio (OR), 1.137; 95% confidence interval (CI), 1.031–1.254; p = 0.010], CD8br %leukocyte (OR, 1.142; 95% CI, 1.067–1.223; p < 0.001), CD8br and CD8dim %leukocyte (OR, 1.117; 95% CI, 1.032–1.210; p = 0.006), naive CD8br %T cell (OR, 1.052; 95% CI, 1.004–1.104; p = 0.035), CD28− CD8dim %T cell (OR, 1.097; 95% CI, 1.038–1.158; p < 0.001), CD127− CD8br AC (OR, 1.086; 95% CI, 1.006–1.171; p = 0.034), CD45 on CD8br (OR, 1.059; 95% CI, 1.021–1.099; p = 0.002), CD3 on HLA DR+ CD8br (OR, 1.098; 95% CI, 1.041–1.158; p < 0.001), CD4 on activated Treg (OR, 1.048; 95% CI, 1.001–1.096; p = 0.046), CD3 on CD39+ resting Treg (OR, 1.070; 95% CI, 1.012–1.131; p = 0.018), IgD+ CD38− %lymphocyte (OR, 1.103; 95% CI, 1.023–1.190; p = 0.011), CD62L− plasmacytoid DC %DC (OR, 1.046; 95% CI, 1.001–1.093; p = 0.046), and FSC-A on plasmacytoid DC (OR, 1.075; 95% CI, 1.003–1.153; p = 0.042) were associated with increased genetic susceptibility to ASD. MR-Egger displayed no horizontal pleiotropy (p ≥ 0.05). Cochran’s Q revealed no heterogeneity of results (p ≥ 0.05). Sensitivity analysis indicated that the results were robust.ConclusionThis MR analysis revealed 13 immune cell phenotypes associated with increased genetic susceptibility to ASD and emphasized the importance of CD8 T cells and Tregs, which provides new directions for the pathogenesis and drug research of ASD.

Abnormalities in both stimulus-induced and baseline MEG alpha oscillations in the auditory cortex of children with Autism Spectrum Disorder.

The neurobiology of Autism Spectrum Disorder (ASD) is hypothetically related to the imbalance between neural excitation (E) and inhibition (I). Different studies have revealed that alpha-band (8-12Hz) activity in magneto- and electroencephalography (MEG and EEG) may reflect E and I processes and, thus, can be of particular interest in ASD research. Previous findings indicated alterations in event-related and baseline alpha activity in different cortical systems in individuals with ASD, and these abnormalities were associated with core and co-occurring conditions of ASD. However, the knowledge on auditory alpha oscillations in this population is limited. This MEG study investigated stimulus-induced (Event-Related Desynchronization, ERD) and baseline alpha-band activity (both periodic and aperiodic) in the auditory cortex and also the relationships between these neural activities and behavioral measures of children with ASD. Ninety amplitude-modulated tones were presented to two groups of children: 20 children with ASD (5 girls, Mage = 10.03, SD = 1.7) and 20 typically developing controls (9 girls, Mage = 9.11, SD = 1.3). Children with ASD had a bilateral reduction of alpha-band ERD, reduced baseline aperiodic-adjusted alpha power, and flattened aperiodic exponent in comparison to TD children. Moreover, lower raw baseline alpha power and aperiodic offset in the language-dominant left auditory cortex were associated with better language skills of children with ASD measured in formal assessment. The findings highlighted the alterations of E / I balance metrics in response to basic auditory stimuli in children with ASD and also provided evidence for the contribution of low-level processing to language difficulties in ASD.

Gender diversity in autistic and neurotypical youth over adolescence and puberty: A longitudinal study.

Recent research in autism spectrum disorder (ASD) has suggested a higher prevalence of gender diversity in individuals diagnosed with ASD. Adolescence is a critical period for the consolidation of gender identity, yet the extent to which the experience of gender diversity is stable over adolescence and puberty in autistic youth is poorly understood. The aim of the study was to examine the consistency of gender diversity using the gender diversity screening questionnaire for self- and parent-report of youth (GDSQ-S, GDSQ-P) over a four-year longitudinal study of pubertal development in youth with ASD (N = 140, 36 assigned-female-at birth (AFAB)) and typical development (TD, N = 104, 58 assigned-male-at-birth [AMAB]) and their parents. The extent to which diagnosis (ASD vs. TD), assigned sex (AFAB vs. AMAB) and developmental level (age, puberty) predict GDSQ trajectory over time was explored. There was a significant diagnosis by sex-assigned-at-birth by age interaction for GDSQ-S Gender Diversity, p = 0.002, showing higher scores in autistic AFAB youth over adolescence, and TD AFAB showing initially lower, then increasing levels over adolescence. For GDSQ-P, Gender Incongruence was significantly different between the groups, p = 0.032, showing higher incongruence for autistic AFAB around age 10, decreasing between age 12-14 before increasing again, while TD AFAB evidence the inverse trend. AMAB trends were stable. The significant diagnostic, developmental and sex-based differences indicate AFAB youth experience greater gender diversity that evolves over development. Findings suggest gender identity formation is nuanced and may be influenced by pubertal progression, hormonal patterns, and psychosocial factors. Results underscore the need for enhanced understanding of the unique, dynamic profiles of females-assigned-at-birth.

Tcf4 dysfunction alters dorsal and ventral cortical neurogenesis in Pitt-Hopkins syndrome mouse model showing sexual dimorphism

Pitt-Hopkins syndrome (PTHS) is a neurodevelopmental disorder caused by haploinsufficiency of transcription factor 4 (TCF4). In this work, we focused on the cerebral cortex and investigated in detail the progenitor cell dynamics and the outcome of neurogenesis in a PTHS mouse model. Labeling and quantification of progenitors and newly generated neurons at various time points during embryonic development revealed alterations affecting the dynamic of cortical progenitors since the earliest stages of cortex formation in PTHS mice. Consequently, establishment of neuronal populations and layering of the cortex were found to be altered in heterozygotes subjects at birth. Interestingly, defective layering process of pyramidal neurons was partially rescued by reintroducing TCF4 expression using focal in utero electroporation in the cerebral cortex. Coincidentally with a defective dorsal neurogenesis, we found that ventral generation of interneurons was also defective in this model, which may lead to an excitation/inhibition imbalance in PTHS. Overall, sex-dependent differences were detected with more marked effects evidenced in males compared with females. All of this contributes to expand our understanding of PTHS, paralleling the advances of research in autism spectrum disorder and further validating the PTHS mouse model as an important tool to advance preclinical studies.

Application of the Community-based Participatory Research in Autism Spectrum Disorder: A Scoping Review

Application of the Community-based Participatory Research in Autism Spectrum Disorder: A Scoping Review

Current and future applications of light-sheet imaging for identifying molecular and developmental processes in autism spectrum disorders.

Autism spectrum disorder (ASD) is identified by a set of neurodevelopmental divergences that typically affect the social communication domain. ASD is also characterized by heterogeneous cognitive impairments and is associated with cooccurring physical and medical conditions. As behaviors emerge as the brain matures, it is particularly essential to identify any gaps in neurodevelopmental trajectories during early perinatal life. Here, we introduce the potential of light-sheet imaging for studying developmental biology and cross-scale interactions among genetic, cellular, molecular and macroscale levels of circuitry and connectivity. We first report the core principles of light-sheet imaging and the recent progress in studying brain development in preclinical animal models and human organoids. We also present studies using light-sheet imaging to understand the development and function of other organs, such as the skin and gastrointestinal tract. We also provide information on the potential of light-sheet imaging in preclinical drug development. Finally, we speculate on the translational benefits of light-sheet imaging for studying individual brain-body interactions in advancing ASD research and creating personalized interventions.

Autism spectrum disorder: pathogenesis, biomarker, and intervention therapy.

Autism spectrum disorder (ASD) has become a common neurodevelopmental disorder. The heterogeneity of ASD poses great challenges for its research and clinical translation. On the basis of reviewing the heterogeneity of ASD, this review systematically summarized the current status and progress of pathogenesis, diagnostic markers, and interventions for ASD. We provided an overview of the ASD molecular mechanisms identified by multi-omics studies and convergent mechanism in different genetic backgrounds. The comorbidities, mechanisms associated with important physiological and metabolic abnormalities (i.e., inflammation, immunity, oxidative stress, and mitochondrial dysfunction), and gut microbial disorder in ASD were reviewed. The non-targeted omics and targeting studies of diagnostic markers for ASD were also reviewed. Moreover, we summarized the progress and methods of behavioral and educational interventions, intervention methods related to technological devices, and research on medical interventions and potential drug targets. This review highlighted the application of high-throughput omics methods in ASD research and emphasized the importance of seeking homogeneity from heterogeneity and exploring the convergence of disease mechanisms, biomarkers, and intervention approaches, and proposes that taking into account individuality and commonality may be the key to achieve accurate diagnosis and treatment of ASD.

Paraeducator Perspectives on Supporting Students With Autism Spectrum Disorder: Implications for Improving Paraeducator Training and Supervision

The number of students with autism spectrum disorder (ASD) in PreK–12 schools has increased over time. However, paraeducators supporting students with ASD typically receive limited training and are unprepared for their roles. As part of a larger survey study, we conducted a content analysis of paraeducator responses across three open-ended survey items focused on their skills, training, and challenges specific to supporting students with ASD. Despite reporting limited preparation and training, paraeducators described a variety of knowledge, skills, and dispositions related to supporting students with ASD, many of which resulted from their practical experiences as opposed to school-based training and supervision. Our findings also confirm that paraeducators face a number of challenges in collaborating with other educational professionals and families and supporting students with ASD across settings due to limited training and preparation. We present implications for developing paraeducator training programs in the area of ASD and future research directions.

Biosignal comparison for autism assessment using machine learning models and virtual reality

Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies. In this study, we examined a VR screening tool consisting of four virtual scenes, and we compared machine learning models based on implicit (motor skills and eye movements) and explicit (behavioral responses) biosignals. Machine learning models were developed for each biosignal within the virtual scenes and then combined into a final model per biosignal. A linear support vector classifier with recursive feature elimination was used and tested using nested cross-validation. The final model based on motor skills exhibited the highest robustness in identifying ASD, achieving an AUC of 0.89 (SD = 0.08). The best behavioral model showed an AUC of 0.80, while further research is needed for the eye-movement models due to limitations with the eye-tracking glasses. These findings highlight the potential of motor skills in enhancing objectivity and reliability in the early assessment of ASD compared to other biosignals.

Bullying and Autism Spectrum Disorder: Correlating the Victimization of High-Functioning Autism Students with Educational Practices in the Context of Inclusion in Primary Education

AbstractBullying and victimization in children with autism spectrum disorder (ASD) is a problem of particular importance, as this category of students is at high risk of victimization by other students, which negatively affects their psychosocial and emotional development. The purpose of this study is to investigate the rate of victimization of children with high-functioning autism (AHF) by their peers in primary school, and whether this rate correlates with teachers’ education professionals’ classroom practices for the inclusion. Data collection was conducted using two questionnaires, the Autism Inclusion Questionnaire (AIQ) (Segall & Campbell in Autism inclusion questionnaire, 2007), which explores the educational practices that teachers utilize in terms of including students with ASD in the general classroom (Segall & Campbell in Research in Autism Spectrum Disorders 6(3):1156–1167, 2012), and the questionnaire on victimization of children with ASD by their peers (Belidou in Autism spectrum disorder and victimization: teachers’ views of the association with theory of mind and friendship (Master thesis), 2017). The survey was based on the responses of 143 teachers who teach primary school students diagnosed with high-functioning ASD. The results showed that 34.3% of teachers observed that AHF children are at higher risk of victimization compared to typically developing children. Also, it was found that there are several educational practices of children with autism, which are associated with the victimization of children belonging to the high-functioning autism spectrum.

Prevalence and determinants of autism spectrum disorder in children in Saudi Arabia in comparison to developed countries: a systematic review and meta-analysis

This study addressed the prevalence and determinants of autism spectrum disorder (ASD) in children, with an aim to provide a comprehensive overview of global trends in ASD prevalence, considering the challenges posed by methodological variations, diverse definitions of autism, and varying diagnostic criteria among different nations. A systematic literature search yielded 3,457 studies, narrowed down to 1,834 after eliminating duplicates. The screening process, involved a meticulous review of titles, abstracts, and full-text articles, resulted in the exclusion of 870 studies that did not meet the inclusion criteria. Quality assessment identified and removed seven low-quality studies. The final meta-analysis incorporated 25 studies, presenting a diverse snapshot of ASD prevalence in developed countries. The selected studies spanned the years 2015 to 2023, ensuring a contemporary representation. The meta-analysis revealed a global prevalence of ASD in children at 0.51%, with a 95% confidence interval ranging from 0.4% to 1%. Regional variations were observed, with Saudi Arabia exhibiting a prevalence of 1.33%, surpassing the global average. Gender disparity was consistent, with males having a higher representation, reflecting a global trend. Differences in the definition of autism, screening tools, and diagnostic criteria presented challenges in cross-country comparisons, emphasizing the need for standardized approaches in future research. This study contributed valuable insights into the global prevalence of ASD, highlighting regional variations, gender disparities, and methodological challenges. The elevated prevalence in Saudi Arabia underscores the importance of tailored interventions, while the methodological differences emphasize the need for standardization in ASD research.

The Importance of Data Sources for Machine Learning Applications in Autism: A Mini Review

Autism spectrum disorder (ASD) is a group of lifelong heterogeneous neurodevelopmental conditions with a wide range of severity levels that affect social communication and social interaction. Diagnosis of ASD relies on subjective observation of these clinical phenotypes. The growing body of big data generated by subjective methods and more recently by objective high-throughput technologies such as omics for the detection of biomolecules, is being successfully applied to a rapidly-growing number of machine learning (ML) algorithms to inform research for diagnostics and interventions for patients with ASD. While most reviews in this area are focused on the ML approaches, we highlight the impact of the database on the expected outcomes in ML-based ASD research studies.

Autism spectrum disorders and the gastrointestinal tract: insights into mechanisms and clinical relevance.

Autism spectrum disorders (ASDs) are recognized as central neurodevelopmental disorders diagnosed by impairments in social interactions, communication and repetitive behaviours. The recognition of ASD as a central nervous system (CNS)-mediated neurobehavioural disorder has led most of the research in ASD to be focused on the CNS. However, gastrointestinal function is also likely to be affected owing to the neural mechanistic nature of ASD and the nervous system in the gastrointestinal tract (enteric nervous system). Thus, it is unsurprising that gastrointestinal disorders, particularly constipation, diarrhoea and abdominal pain, are highly comorbid in individuals with ASD. Gastrointestinal problems have also been repeatedly associated with increased severity of the core symptoms diagnostic of ASD and other centrally mediated comorbid conditions, including psychiatric issues, irritability, rigid-compulsive behaviours and aggression. Despite the high prevalence of gastrointestinal dysfunction in ASD and its associated behavioural comorbidities, the specific links between these two conditions have not been clearly delineated, and current data linking ASD to gastrointestinal dysfunction have not been extensively reviewed. This Review outlines the established and emerging clinical and preclinical evidence that emphasizes the gut as a novel mechanistic and potential therapeutic target for individuals with ASD.

From Data to Insights: Machine Learning Empowers Prognostic Biomarker Prediction in Autism.

Autism Spectrum Disorder (ASD) poses significant challenges to society and science due to its impact on communication, social interaction, and repetitive behavior patterns in affected children. The Autism and Developmental Disabilities Monitoring (ADDM) Network continuously monitors ASD prevalence and characteristics. In 2020, ASD prevalence was estimated at 1 in 36 children, with higher rates than previous estimates. This study focuses on ongoing ASD research conducted by Erciyes University. Serum samples from 45 ASD patients and 21 unrelated control participants were analyzed to assess the expression of 372 microRNAs (miRNAs). Six miRNAs (miR-19a-3p, miR-361-5p, miR-3613-3p, miR-150-5p, miR-126-3p, and miR-499a-5p) exhibited significant downregulation in all ASD patients compared to healthy controls. The current study endeavors to identify dependable diagnostic biomarkers for ASD, addressing the pressing need for non-invasive, accurate, and cost-effective diagnostic tools, as current methods are subjective and time-intensive. A pivotal discovery in this study is the potential diagnostic value of miR-126-3p, offering the promise of earlier and more accurate ASD diagnoses, potentially leading to improved intervention outcomes. Leveraging machine learning, such as the K-nearest neighbors (KNN) model, presents a promising avenue for precise ASD diagnosis using miRNA biomarkers.

Molecular Autism Research in Africa: Emerging Themes and Prevailing Disparities

AbstractAfrican populations are consistently underrepresented in molecular research on autism spectrum disorders (ASD). Yet, Africa’s genetic diversity could reveal novel mechanisms associated with ASD etiology. We review the molecular ASD research from Africa between 2016–2022, highlighting region-specific limitations, opportunities, and areas of progress. We emphasize a need to advance null-hypothesis based molecular studies in Africa, particularly in critically understudied Sub-Saharan African (SSA) populations. Using South Africa as a case study, we show that this geographical disparity is not solely attributable to sociocultural barriers nor to an absence of molecular research infrastructure. We emphasize the importance of interdisciplinary collaboration within SSA and internationally to harness existing infrastructure for the expansion of molecular ASD research in Africa.

Divergent electroencephalogram resting-state functional network alterations in subgroups of autism spectrum disorder: a symptom-based clustering analysis.

Autism spectrum disorder (ASD) is characterized by etiological and phenotypic heterogeneity. Despite efforts to categorize ASD into subtypes, research on specific functional connectivity changes within ASD subgroups based on clinical presentations is limited. This study proposed a symptom-based clustering approach to identify subgroups of ASD based on multiple clinical rating scales and investigate their distinct Electroencephalogram (EEG) functional connectivity patterns. Eyes-opened resting-state EEG data were collected from 72 children with ASD and 63 typically developing (TD) children. A data-driven clustering approach based on Social Responsiveness Scales-Second Edition and Vinland-3 scores was used to identify subgroups. EEG functional connectivity and topological characteristics in four frequency bands were assessed. Two subgroups were identified: mild ASD (mASD, n = 37) and severe ASD (sASD, n = 35). Compared to TD, mASD showed increased functional connectivity in the beta band, while sASD exhibited decreased connectivity in the alpha band. Significant between-group differences in global and regional topological abnormalities were found in both alpha and beta bands. The proposed symptom-based clustering approach revealed the divergent functional connectivity patterns in the ASD subgroups that was not observed in typical ASD studies. Our study thus provides a new perspective to address the heterogeneity in ASD research.

Clinical utility of the standardized observation tool Autism Behavior Coding System for early intervention research in autism spectrum disorder.

The Autism Behavior Coding System (ABCS) was developed to help evaluating the effectiveness of early intensive interventions in children with autism spectrum disorder (ASD). The video-based ABCS assesses eight core autistic behavioral variables during therapist-child interaction using standardized quantitative criteria, four behaviors according to their frequency of occurrence, four according to their duration. The present study focuses (1) on the correspondence of ABCS scores with scores on two standard clinical instruments (the ADOS-2 and an ASD-adaptation of the Children's Global Assessment Scale, DD-CGAS), (2) on the sensitivity to change of ABCS scores by the end of an intensive 18 days intervention period (EIP) and (c) on the predictability of short- and longer-term changes in social and repetitive behaviors from ABCS scores at baseline and EIP. Data from 51 children (42 M, 9 F; median age 45 months) followed over 1 year were available. There were significant correlations at baseline between several ABCS scores and ADOS-2 as well as DD-CGAS scores. Correlations at EIP between some ABCS and DD-CGAS scores were highly significant. Four ABCS scores reflected significant changes from baseline to EIP. Several baseline ABCS scores were predictive of DD-CGAS and ADOS-2 scores at EIP and Year 1. However, associations between ABCS score changes from baseline to EIP and the clinical scale changes by Year 1 were not significant. It is concluded that several ABCS scores have adequate clinical validity and sensitivity to change. The short-term changes in ABCS scores and their relationship to longer-term clinical changes need further study.

Identification of two novel autism genes, TRPC4 and SCFD2, in Qatar simplex families through exome sequencing.

This study investigated the genetic underpinnings of autism spectrum disorder (ASD) in a Middle Eastern cohort in Qatar using exome sequencing. The study identified six candidate autism genes in independent simplex families, including both four known and two novel autosomal dominant and autosomal recessive genes associated with ASD. The variants consisted primarily of de novo and homozygous missense and splice variants. Multiple individuals displayed more than one candidate variant, suggesting the potential involvement of digenic or oligogenic models. These variants were absent in the Genome Aggregation Database (gnomAD) and exhibited extremely low frequencies in the local control population dataset. Two novel autism genes, TRPC4 and SCFD2, were discovered in two Qatari autism individuals. Furthermore, the D651A substitution in CLCN3 and the splice acceptor variant in DHX30 were identified as likely deleterious mutations. Protein modeling was utilized to evaluate the potential impact of three missense variants in DEAF1, CLCN3, and SCFD2 on their respective structures and functions, which strongly supported the pathogenic natures of these variants. The presence of multiple de novo mutations across trios underscored the significant contribution of de novo mutations to the genetic etiology of ASD. Functional assays and further investigations are necessary to confirm the pathogenicity of the identified genes and determine their significance in ASD. Overall, this study sheds light on the genetic factors underlying ASD in Qatar and highlights the importance of considering diverse populations in ASD research.

Differential DNA Methylation from Autistic Children Enriches Evidence for Genes Associated with ASD and New Candidate Genes.

The etiology of Autism Spectrum Disorders (ASD) is a result of the interaction between genes and the environment. The study of epigenetic factors that affect gene expression, such as DNA methylation, has become an important area of research in ASD. In recent years, there has been an increasing body of evidence pointing to epigenetic mechanisms that influence brain development, as in the case of ASD, when gene methylation dysregulation is present. Our analysis revealed 853 differentially methylated CpG in ASD patients, affecting 509 genes across the genome. Enrichment analysis showed five related diseases, including autistic disorder and mental disorders, which are particularly significant. In this work, we identified 64 genes that were previously reported in the SFARI gene database, classified according to their impact index. Additionally, we identified new genes that have not been previously reported as candidates with differences in the methylation patterns of Mexican children with ASD.

A study of genetic heterogeneity in autism spectrum disorders based on plasma proteomic and metabolomic analysis: multiomics study of autism heterogeneity.

Genetic heterogeneity poses a challenge to research and clinical translation of autism spectrum disorder (ASD). In this study, we conducted a plasma proteomic and metabolomic study of children with ASD with and without risk genes (de novo mutation) and controls to explore the impact of genetic heterogeneity on the search for biomarkers for ASD. In terms of the proteomic and metabolomic profiles, the groups of children with ASD carrying and those not carrying de novo mutation tended to cluster and overlap, and integrating them yielded differentially expressed proteins and differential metabolites that effectively distinguished ASD from controls. The mechanisms associated with them focus on several common and previously reported mechanisms. Proteomics results highlight the role of complement, inflammation and immunity, and cell adhesion. The main pathways of metabolic perturbations include amino acid, vitamin, glycerophospholipid, tryptophan, and glutamates metabolic pathways and solute carriers-related pathways. Integrating the two omics analyses revealed that L-glutamic acid and malate dehydrogenase may play key roles in the pathogenesis of ASD. These results suggest that children with ASD may have important underlying common mechanisms. They are not only potential therapeutic targets for ASD but also important contributors to the study of biomarkers for the disease.