Autism Spectrum Disorder Research Research Articles

Autism in romantic relationships: A content analysis of challenges and strengths (2013–2024)

Abstract Since the Diagnostic and Statistical Manual, Version 5 reconceptualized autism spectrum disorder (ASD) in 2013, ASD research has broadened. To summarize what the field has identified regarding challenges and strengths in autistic romantic relationships, we conducted a content analysis review of peer‐reviewed articles published from 2013 to 2024 using a systematic lens. Of the total possible relevant articles identified, 42 articles met our inclusion (peer reviewed articles) and exclusion criteria (e.g., focus on autistic children). The literature revealed several challenges in these relationships, including sensory sensitivities, stigmas, and communication barriers. Some strengths, such as pattern recognition and truthful communication, were found, but overall strengths‐based research was lacking. There was no literature including relationships with both partners on the autism spectrum, and most studies only included individual‐level data. Other gaps in the literature include barriers to relational support and limited inclusion of queer relationships. Future directions for research, practice, and policy are discussed.

Machine Learning and Deep Learning over Discovery of ASD: A Descriptive Review

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition impacting behavior, communication, and social interaction. The term "spectrum" highlights the variability in symptoms and severity, ranging from mild to severe. While ASD typically manifests within the first two years of life, it can remain undiagnosed until adolescence. The exact causes of ASD are still unclear, though genetic research continues to provide valuable insights. Early detection and intervention are essential for better outcomes, enabling timely support and therapy. Leveraging big data and machine learning (ML) and deep learning (DL) techniques has proven beneficial in ASD detection and analysis. This paper reviews existing ML and DL models for ASD classification and prediction, examining over 60 research articles. The analysis covers both supervised and unsupervised ML methods and explores current ASD screening tests employed in laboratory diagnostics by psychologists and behavioral counselors. The review aims to provide insights into the advancements in ASD detection using data-driven approaches. It also serves as a guide for researchers focused on expanding knowledge in health informatics and medical research. Additionally, this paper discusses how mathematical, statistical, and data analytic techniques can be applied to enhance ASD data mining and self-analysis. This review supports the continuous evolution of ASD research and the development of more effective, data-supported diagnostic tools. Received: 7 March 2024 | Revised: 22 April 2025 | Accepted: 7 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Anjana Poonia: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing - original draft, Writing - review & editing, Visualization. Sunil Kumar: Resources, Data curation. Ghanshyam Raghuwanshi: Conceptualization, Validation, Investigation, Writing - review & editing, Supervision, Project administration.

Autoantibody profiling of autism spectrum disorder patients with monoamine oxidase a deficiency

Autoantibody profiling of autism spectrum disorder patients with monoamine oxidase a deficiency

Oxidative stress response and NRF2 signaling pathway in autism spectrum disorder.

Oxidative stress response and NRF2 signaling pathway in autism spectrum disorder.

Positron Emission Tomography in Autism Spectrum Disorder: Current Status and Future Perspectives.

Positron Emission Tomography in Autism Spectrum Disorder: Current Status and Future Perspectives.

Evolution and emerging directions in occupational therapy for autism: A bibliometric evaluation of global literature

The study aims to explore global research trends, influential themes, and existing gaps in occupational therapy (OT) related to Autism Spectrum Disorder (ASD). It seeks to provide clinicians and researchers with comprehensive insights into the evolution, focus areas, and impact of OT interventions in managing ASD. A bibliometric analysis was conducted using data extracted from the Scopus database. A total of 1,002 peer-reviewed journal articles published between 1989 and 2024 were retrieved based on a targeted search strategy incorporating keywords relevant to OT and ASD. Bibliometric tools were utilized to analyze publication trends, influential authors and journals, collaboration networks, citation patterns, and keyword co-occurrences. The analysis revealed a significant rise in publications over the past decade, with the United States leading in both output and citations. Key themes include sensory-based interventions, daily living skills, and an emerging interest in telehealth and virtual reality. Notably, research is predominantly focused on children, with limited studies on adults, culturally diverse populations, and specific OT interventions like vocational training. Visual mapping indicated evolving research priorities and interdisciplinary collaboration patterns. Occupational therapy plays a critical role in ASD intervention. However, despite the increasing research volume, notable gaps persist particularly in long-term outcome studies, adult populations, cultural inclusivity, and technology-driven interventions. Addressing these gaps will enhance the efficacy and reach of OT strategies in ASD care. This study offers evidence-based direction for future OT research and clinical application in ASD. It encourages stakeholders to prioritize longitudinal designs, diversify study populations, and integrate innovative technologies into therapeutic frameworks to improve functional outcomes and quality of life for individuals with ASD.

Terra Incognita - Contributions of the Olivo-Cerebellar System to Autism Spectrum Disorder.

The inferior olivary nuclei (ION), a key component of the olivo-cerebellar system, remain understudied in autism spectrum disorder (ASD) research despite evident cerebellar involvement. This perspective piece aims to elucidate the critical role of the ION in cerebellar microcircuitry and its potential implications in ASD pathophysiology. We review the olivo- cerebellar system and the structural and functional alterations of the ION in autism, highlighting findings from neuroanatomical, neuroimaging, and behavioral studies. ION disruptions, although underexplored, may have a major role in the symptomatology of ASD, particularly higher-order cognitive abilities, rapid stimuli processing, and motor coordination. Specifically, we highlight how anomalies in olivary neuron morphology and olivo-cerebellar connectivity patterns may underlie deficits in temporal processing and motor learning observed in ASD. Furthermore, we discuss the challenges in brainstem imaging and recent advancements in ultra-high field (UHF) 7T MRI technology, as standard neuroimaging techniques. As these neuroimaging techniques continue to evolve, further investigation of the functional territories of the ION holds promise for providing essential understandings into ASD processes and may lead to pioneering therapeutic options targeting this crucial brainstem area.

The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications

The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications

Studies with Students on the Spectrum in Higher Education: A Systematic Literature Review using PRISMA

Systematic reviews have been periodically conducted to examine the experiences of students with autism spectrum disorder (ASD) at university. However, these have not focused on general trends in methodology for ASD research. The present review aims to identify trends related to research methodology, such as sampling procedures, demographics, data collection instruments, and research topics. For this purpose, Scopus-indexed research articles were identified following inclusion and exclusion criteria. The final sample included 39 empirical studies in which autistic students in higher education participated. The results of the systematic literature review underscore important issues related to research method trends: the use of open invitations to recruit participants, the use of interviews for data collection, and a slight predominance of qualitative studies. ASD research has largely overlooked differences related to majors, academic years, and gender, which is a methodological limitation in these studies. Most of the studies have been conducted in the United Kingdom and the United States, neglecting the experiences of autistic students in Latin America, Africa and Asia. The main current concerns in ASD research are the students’ experience and profiles, the factors of academic success, and mental health. It is suggested that future systematic reviews consider increasing the databases used to account for all the currently available ASD research.

Deep CNN Architectures for Autism Spectrum Disorder Detection in Neuroimaging Data

Autism Spectrum Disorder (ASD) creates neurological developmental difficulties which lead to problems with social contact and communication abilities and repetitive patterns of conduct. Detection of ASD needs to happen early and accurately because this enables prompt support services. This research demonstrates deep Convolutional Neural Networks (CNNs) for ASD detection through TensorFlow which stands as one of the most popular deep learning frameworks. A framework built with a complete dataset which includes neuroimaging information and behavioral evaluations with genetic factors trains a CNN model effectively to detect ASD patterns. By designing its framework systematically, the model operates to find complex features in its input data while this process boosts diagnostic performance. The deep CNN method achieves better ASD classification than established strategies while utilizing TensorFlow as its implementation platform according to initial tests. The research demonstrates deep learning techniques enable better early ASD detection which benefits both healthcare professionals and ASD research scientists in their field of work.

Deregulated mRNA and microRNA Expression Patterns in the Prefrontal Cortex of the BTBR Mouse Model of Autism.

Autism spectrum disorder (ASD) is a neurodevelopmental condition caused by both genetic and environmental factors. Since no single gene variant accounts for more than 1% of the cases, the converging actions of ASD-related genes and other factors, including microRNAs (miRNAs), may contribute to ASD pathogenesis. To date, few studies have simultaneously investigated the mRNA and miRNA profiles in an ASD-relevant model. The BTBR mouse strain displays a range of behaviors with ASD-like features but little is known about the protein-coding and noncoding gene expression landscape that may underlie the ASD-like phenotype. Here we performed parallel mRNA and miRNA profiling using the prefrontal cortex (PFC) of BTBR and C57BL/6 J (B6) mice. This identified 1063 differentially expressed genes and 48 differentially expressed miRNAs. Integration of mRNA and miRNA data identified a strong inverse relationship between upregulated (DEGs) and downregulated miRNAs, and vice versa. Pathway analysis, taking account of the inverse relationship between differentially expressed miRNAs and their target mRNAs highlighted significant shared enrichment in immune signaling, myelination, and neurodevelopmental processes. Notably, miRNA changes were predicted to affect synapse-related functions but we did not find enrichment of protein-coding genes linked to cellular components or biological processes related to synapses in the PFC of BTBR mice, indicating processes may evade miRNA control. In contrast, other miRNAs were predicted to have extensive relationships with DEGs suggesting their role as potential hub coordinators of gene expression. Profiling findings were confirmed via qRT-PCR for representative protein-coding transcripts and miRNAs. Our study underscores the complex interplay between gene expression and miRNA regulation within immune and inflammatory pathways in the BTBR model, offering insights into the neurodevelopmental mechanisms of ASD. These results support the value of the BTBR mouse model and identify strategies that could adjust molecular pathways for therapeutic applications in ASD research.

Transcripts derived from AmnSINE1 repetitive sequences are depleted in the cortex of autism spectrum disorder patients.

Autism spectrum disorder (ASD) is a brain developmental disability with a not-fully clarified etiogenesis. Current ASD research largely focuses on coding regions of the genome, but up to date much less is known about the contribution of non-coding elements to ASD risk. The non-coding genome is largely made of DNA repetitive sequences (RS). Although RS were considered slightly more than "junk DNA", today RS have a recognized role in almost every aspect of human biology, especially in developing human brain. Our aim was to test if RS transcription may play a role in ASD. Global RS transcription was firstly investigated in postmortem dorsolateral prefrontal cortex of 13 ASD patients and 39 matched controls. Results were validated in independent datasets. AmnSINE1 was the only RS significantly downregulated in ASD specimens. The role of AmnSINE1 in ASD has been investigated at multiple levels, showing that the 1,416 genes containing AmnSINE1 are associated with nervous system development and autism susceptibility. This has been confirmed in a different experimental setting, such as in organoid models of the human cerebral cortex, harboring different ASD causative mutations. AmnSINE1 related genes are transcriptionally co-regulated and are involved not only in brain formation but can specifically be involved in ASD development. Looking for a possible direct role of AmnSINE1 non-coding transcripts in ASD, we report that AmnSINE1 transcripts may alter the miRNA regulatory landscape for genes involved in neurogenesis. Our findings provide preliminary evidence supporting a role for AmnSINE1 in ASD development.

Exploration of various approaches for detection of autism spectrum disorder

Autism spectrum disorder (ASD) presents a complex and diverse set of challenges, necessitating innovative and data-driven approaches for effective understanding, diagnosis, and intervention. This review explores recent advancements in methodologies, technologies, and frameworks aimed at addressing ASD and also highlights novel data collection methods, focusing on the integration of wearable internet of things (IoT) sensors for real-time behavioral monitoring and data capture from individuals with ASD. Additionally, the utilization of machine learning (ML), deep learning (DL), and hybrid techniques for data analysis, feature optimization, and prediction of ASD are extensively discussed, showcasing significant progress in early diagnosis and personalized intervention planning. The challenges such as class imbalance, feature selection, and data collection efficiency are identified and addressed using the proposed ASD framework. The review also emphasizes the development of recommendation systems designed to the unique behavioral profiles and needs of individuals with ASD. The findings reveal that integrating these advanced technologies and methodologies can lead to more accurate diagnoses and effective interventions, contributing to the broader field of ASD research.

Machine Learning Methods to Detect Autism Among Children

Autism Spectrum Disorder (ASD) is a developmental condition affecting communication, behavior, and social interaction. Early detection is crucial for timely intervention, yet it is often delayed due to limited specialists and difficulty in recognizing symptoms. In this study, we propose a machine learning-based approach using Natural Language Processing (NLP) and PySpark to analyze unstructured text data from online forums, social media, and caregiver reports. Our methodology involves data collection, feature selection, and classification using deep learning models such as LSTM-RNN. By leveraging PySpark’s scalability, we process large text datasets efficiently to identify linguistic markers of ASD. The goal is to enhance early autism detection by analyzing caregiver-reported observations, ultimately supporting early intervention efforts. Future research will explore advanced ML techniques to reduce overfitting and improve model performance. This study contributes to ASD research by demonstrating the potential of NLP-driven approaches for scalable and automated autism detection

H-GWNN: A Population Graph-Based Autism Spectrum Disorders Identification Framework for Tackling Data Heterogeneity

aims: In recent years, the integration of multi-site neuroimaging datasets with deep learning methods has yielded substantial advancements in the realm of autism spectrum disorder (ASD) research. background: However, existing graph convolutional neural networks (GCNs) only aggregate neighbor information at a fixed scale and cannot adjust the scale parameters to aggregate the best information. objective: In this paper, a homogeneous graph wavelet neural network (H-GWNN) model is proposed to learn representations for graph classification in an end-to-end manner. method: Specifically, the heterogeneity of multi-site datasets is processed by the ComBat algorithm, which is based on location and scale effects. Image data and phenotypic data are fused to construct a population graph, and the most discriminative information on the graph is extracted by adjusting the scale in a graph wavelet neural network. result: Experiments on 1035 subjects from the autism dataset ABIDE show that the classification accuracy of H-GWNN on autism is 83.58%, which exceeds that of other related GCN models. conclusion: The findings demonstrate that the proposed model can not only process the heterogeneity problem, but also analyze node features at the optimal scale. It captures discriminative features that identify diseases and improves classification performance for brain disease diagnosis. other: N/A

Transcranial Direct Current Stimulation Can Modulate Brain Complexity and Connectivity in Children with Autism Spectrum Disorder: Insights from Entropy Analysis.

The core characteristics of autism spectrum disorder (ASD) are atypical neurodevelopmental disorders. Transcranial direct current stimulation (tDCS), as a non-invasive brain stimulation technique, has been applied in the treatment of various neurodevelopmental disorders. Entropy analysis methods can quantitatively describe the complexity of EEG signals and information transfer. This study recruited 24 children with ASD and 24 age- and gender-matched typically developing (TD) children, using multiple entropy methods to analyze differences in brain complexity and effective connectivity between the two groups. Furthermore, this study explored the regulatory effect of tDCS on brain complexity and effective connectivity in children with ASD. The results showed that children with ASD had lower brain complexity, with excessive effective connectivity in the δ, θ, and α frequency bands and insufficient effective connectivity in the β frequency band. After tDCS intervention, the brain complexity of children with ASD significantly increased, while effective connectivity in the δ and θ frequency bands significantly decreased. The results from behavioral-scale assessments also indicated positive behavioral changes. These findings suggest that tDCS may improve brain function in children with ASD by regulating brain complexity and effective connectivity, leading to behavioral improvements, and they provide new perspectives and directions for intervention research in ASD.

Teaching students to effectively evaluate scientific evidence and advocate for research in the context of autism spectrum disorder and the neurodiversity movement.

Teaching students to effectively evaluate scientific evidence and advocate for research in the context of autism spectrum disorder and the neurodiversity movement.

Untangling the Molecular Mechanisms Contributing to Autism Spectrum Disorder Using Stem Cells.

Autism spectrum disorder (ASD) is a complex neuro developmental condition characterized by significant genetic and phenotypic variability, making diagnosis and treatment challenging. The heterogeneity of ASD-associated genetic variants and the absence of clear causal factors in many cases complicate personalized care. Traditional models, such as postmortem brain tissue and animal studies, have provided valuable insights but are limited in capturing the dynamic processes and human-specific aspects of ASD pathology. Recent advances in human induced pluripotent stem cell (iPSC) technology have transformed ASD research by enabling the generation of patient-derived neural cells in both two-dimensional cultures and three-dimensional brain organoid models. These models retain the donor's genetic background, allowing researchers to investigate disease-specific cellular and molecular mechanisms while identifying potential therapeutic targets tailored to individual patients. This commentary highlights how stem cell-based approaches are advancing our understanding of ASD and paving the way for more personalized diagnostic and therapeutic strategies.

Neurodegeneration in Autism: A Study of Clusterin, Very Long-Chain Fatty Acids, and Carnitine.

The clinical identification of regression phenomena in ASD lacks specific biological or laboratory criteria and is often based on family history and highly subjective observations by clinicians. The present study aimed to investigate the potential role of plasma clusterin (CLU), very long-chain fatty acids (VLCFA), and carnitine as biomarkers of neurodegeneration in children with autism spectrum disorder (ASD) with and without regression. By exploring these biomarkers, we sought to provide insights into mitochondrial dysfunction, glial activation, and lipid metabolism, which may contribute to the pathophysiology of ASD and aid in the early diagnosis and intervention of regression phenomena in ASD. Ninety children aged 2-6years were included: 30 with autism spectrum disorder (ASD), 30 with regressive ASD, and 30 healthy controls. Psychiatric assessments were conducted using DSM-5 criteria, CARS, ABC, RBS-R, and ASSQ scales. Regression in ASD was evaluated retrospectively using a modified ADI-R questionnaire. Fasting blood samples were collected, and plasma clusterin (CLU), VLCFA, and carnitine levels were measured. Statistical analyses were performed using MANOVA to assess the effect of group differences on dependent biochemical variables. Serum clusterin and carnitine levels showed no significant differences between groups. However, C22 VLCFA levels were significantly higher in both autism groups compared to controls (p = 0.04), with post hoc analysis indicating the difference between the non-regressive and control groups (p = 0.02). Serum carnitine was positively correlated with stereotypic behaviors subscale scores (r = 0.37, p = 0.004) and total scores (r = 0.35, p = 0.006) of RBS-R. Our study provides insights into the complexities of biomarker research in autism spectrum disorder (ASD), highlighting the challenges in identifying consistent biological markers for regression and non-regression phenotypes. Although no significant findings were observed, further biomarker studies are essential to distinguish possible endophenotypes, improve early diagnosis, and uncover potential therapeutic targets in ASD.

Phenotypic divergence between individuals with self-reported autistic traits and clinically ascertained autism

While allowing for rapid recruitment of large samples, online research relies heavily on participants’ self-reports of neuropsychiatric traits, foregoing the clinical characterizations available in laboratory settings. Autism spectrum disorder (ASD) research is one example for which the clinical validity of such an approach remains elusive. Here we compared 56 adults with ASD recruited in person and evaluated by clinicians to matched samples of adults recruited through an online platform (Prolific; 56 with high autistic traits and 56 with low autistic traits) and evaluated via self-reported surveys. Despite having comparable self-reported autistic traits, the online high-trait group reported significantly more social anxiety and avoidant symptoms than in-person ASD participants. Within the in-person sample, there was no relationship between self-rated and clinician-rated autistic traits, suggesting they may capture different aspects of ASD. The groups also differed in their social tendencies during two decision-making tasks; the in-person ASD group was less perceptive of opportunities for social influence and acted less affiliative toward virtual characters. These findings highlight the need for a differentiation between clinically ascertained and trait-defined samples in autism research.