Assessment Of Autism Spectrum Disorder Research Articles

A comparison of visual attention to pictures in the Autism Diagnostic Observation Schedule in children and adolescents with ADHD and/or autism.

Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which frequently co-occur. The Autism Diagnostic Observation Schedule (ADOS) is commonly used to aid with diagnostic assessment of ASD but was not originally designed for use in those with comorbid ADHD. Visual attention to social stimuli has been often studied in ASD using eye-tracking, to obtain quantitative indices of how attention is deployed to different parts of a social image/scene. As the ADOS includes tasks that rely on attending to and processing images of social scenes, these measures of visual attention could provide useful additional objective measurement alongside ADOS scores to enhance the characterisation of autistic symptoms in those with ADHD. Children with ASD, comorbid ASD and ADHD, ADHD and Neurotypical (NT) controls were recruited (n=84). Visual attention was measured using eye-tracking during free viewing of social scenes selected from the ADOS. The full ADOS was then administered. Stimulant medication was temporarily withdrawn during this assessment. Research diagnoses were based on the Development and Wellbeing Assessment (DAWBA), ADOS, Social Communication Questionnaire (SCQ, a measure of ASD severity) and Conners' Rating Scales (CRS-3, a measure of ADHD severity) following clinical consensus. Using factorial ANOVAs to model ADHD, Autism and their interaction, we found that fixation duration to faces was reduced in those with ASD (ASD and ASD+ADHD) compared to those without ASD (ADHD and NT). Reduced visual attention to faces in the whole sample was associated with Autism symptom severity (SCQ subscale scores) but not ADHD symptom severity (CRS-3 scores). Our findings provide preliminary evidence in support of implementing visual attention measurement during assessment of ASD in the context of comorbidity with ADHD. For example, if a child with ADHD was found to reduce attention to faces in ADOS pictures this may suggest additive difficulties on the autism spectrum. Replication across a larger sample would be informative. This work has future potential in the clinic to help with complex cases, including those with co-occurring ADHD and ASD.

The Brain Gene Registry: a data snapshot

Monogenic disorders account for a large proportion of population-attributable risk for neurodevelopmental disabilities. However, the data necessary to infer a causal relationship between a given genetic variant and a particular neurodevelopmental disorder is often lacking. Recognizing this scientific roadblock, 13 Intellectual and Developmental Disabilities Research Centers (IDDRCs) formed a consortium to create the Brain Gene Registry (BGR), a repository pairing clinical genetic data with phenotypic data from participants with variants in putative brain genes. Phenotypic profiles are assembled from the electronic health record (EHR) and a battery of remotely administered standardized assessments collectively referred to as the Rapid Neurobehavioral Assessment Protocol (RNAP), which include cognitive, neurologic, and neuropsychiatric assessments, as well as assessments for attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Co-enrollment of BGR participants in the Clinical Genome Resource’s (ClinGen’s) GenomeConnect enables display of variant information in ClinVar. The BGR currently contains data on 479 participants who are 55% male, 6% Asian, 6% Black or African American, 76% white, and 12% Hispanic/Latine. Over 200 genes are represented in the BGR, with 12 or more participants harboring variants in each of these genes: CACNA1A, DNMT3A, SLC6A1, SETD5, and MYT1L. More than 30% of variants are de novo and 43% are classified as variants of uncertain significance (VUSs). Mean standard scores on cognitive or developmental screens are below average for the BGR cohort. EHR data reveal developmental delay as the earliest and most common diagnosis in this sample, followed by speech and language disorders, ASD, and ADHD. BGR data has already been used to accelerate gene-disease validity curation of 36 genes evaluated by ClinGen’s BGR Intellectual Disability (ID)-Autism (ASD) Gene Curation Expert Panel. In summary, the BGR is a resource for use by stakeholders interested in advancing translational research for brain genes and continues to recruit participants with clinically reported variants to establish a rich and well-characterized national resource to promote research on neurodevelopmental disorders.

Examining Language, Speech and Behaviour Characteristics: A Cross-Sectional Study in Saudi Arabia Using the Arabic Version of Gilliam Autism Rating Scale-Third Edition.

Autism spectrum disorder (ASD) exhibits diverse manifestations influenced by demographic factors. This study evaluates these variations within Saudi Arabia, aiming to investigate language, speech and behaviour characteristics across different demographics in Saudi Arabia using the Arabic Version of the Gilliam Autism Rating Scale-Third Edition (A-GARS-3). Employing a cross-sectional design, 178 participants were stratified by developmental status (n = 124 school settings, n = 54 clinical setting), sex (Females = 77, Males =101), age (range = 3-22), and geographical region (different provinces in Saudi Arabia). The A-GARS-3 measured ASD manifestations across six subscales. The study identified significant differences in ASD manifestations by developmental status, with higher ASD likelihood and severity in clinical settings. Younger children showed more pronounced ASD characteristics, and males were slightly more likely to be diagnosed with ASD. Geographical analysis revealed regional differences in severity. The findings underline the importance of demographic considerations in ASD assessment and diagnosis, suggesting the need for age-specific and culturally sensitive approaches. The A-GARS-3 is a reliable tool for the Saudi context. Regional disparities in ASD prevalence and severity indicate a need for tailored health policies and resources across Saudi provinces.

Clinician Opinions Regarding the Usefulness of the BOSA for ASD Assessment in a Service for Children Aged Under 12 Years.

The COVID-19 pandemic and the subsequent social distancing policies forced healthcare teams to drastically alter the way they deliver services. This was particularly challenging for clinicians involved in diagnosing autism spectrum disorder (ASD), as assessment tools and methods required face-to-face social interactions between clinicians and children. To address this, the Brief Observation of Symptoms of Autism (BOSA) was developed to ensure that people suspected of ASD can receive diagnostic assessments during the pandemic. This project aimed to explore clinicians' opinions on the BOSA, particularly regarding the usefulness of the assessment for clinicians to clarify diagnostic outcomes of ASD assessments. Both quantitative and qualitative data was gathered within an NHS community paediatric team. This included a questionnaire for clinicians to complete, and data from the BOSA assessments done in the service. Thematic analysis and descriptive statistics revealed that many clinicians felt that the BOSA can be beneficial in certain cases, such as selective mutism, and found the BOSA particularly helpful for observing parent-child interactions. These findings highlighted important information that the Autism Diagnostic Observation Schedule Second Edition (ADOS-2) does not give opportunities to observe. Clinicians reported that at times, the BOSA materials, brevity and parental administration created barriers to gathering information for diagnostic decisions. As may be expected, clinicians showed a clear preference for the more familiar and validated ADOS-2. However, the study highlights perceived limitations of the ADOS-2 and strengths of the BOSA, with recommendations made for future practice and research.

IJCM_333A: Household Air Pollution Exposure and Autism Spectrum Disorder Outcomes in Children: Insights from the HAPIN India Study

Background: The global public health challenge of household air pollution, stemming from conventional cooking practices and the widespread use of solid fuels, is a critical concern. Exposure to indoor air pollutants has been associated with adverse health outcomes, particularly affecting vulnerable populations like pregnant women and young children. The Household Air Pollution and Neurodevelopment (HAPIN) study addresses this issue through a multicentric randomized controlled trial spanning four diverse countries: Guatemala, India, Peru, and Rwanda. Objectives: The study aims to evaluate the prevalence of autistic spectrum disorder among children residing in HAPIN study site of India. Methodology: A cross-sectional assessment for autism spectrum disorders within the HAPIN cohort was done.365 children aged 2 to 3 years were studied to explore the potential link between household air pollution and autism spectrum disorder (ASD). Household air quality, measuring PM2.5, CO, and black carbon, was assessed using established protocols. Neurodevelopmental outcomes, particularly ASD, was screened using the Modified Checklist for Autism in Toddlers (M-CHAT) in children aged 2 to 3 years, and screened positive children were evaluated by a psychiatrist to diagnose autism spectrum disorder using DSM-5 criteria. Results: There is significant association between exposure to air pollutants (PM2.5, CO, and black carbon) and the diagnosis of autism spectrum disorder (ASD), with a prevalence rate of 5.48%. Conclusion: ASD prevalence rate of 5.48%, underscore the global relevance of the impact of household air pollution on neurodevelopment. Urgent interventions are warranted to mitigate the risk of ASD, particularly for vulnerable populations like pregnant women and young children, emphasizing the imperative for cleaner cooking practices and improved indoor air quality.

Assessment of Autism Spectrum Disorders in Children with Visual Impairment and Blindness: A Scoping Review.

There is a higher incidence of diagnosed Autism Spectrum Disorder (ASD) in children with visual impairment and blindness (VIB) than in typically sighted children. However, we currently lack appropriate assessment measures to fully understand the neurodevelopment of children with VIB. Numerous factors, such as common characteristics between children with VIB and ASD and the reliance of visual behaviours in assessments of ASD, complicate the clinical and diagnostic understanding of these children. This scoping review aims to describe the published knowledge on ASD assessment in children with VIB. The literature search was performed through MEDLINE, PsycINFO and Scopus. Reference lists of pertinent articles were scrutinized for snowball searching. Articles retained were based on original empirical studies, were relevant to or conducted with children or adolescents with VIB and described assessments for ASD. Pertinent information was extracted, and a thematic analysis was performed. Only 13 articles retrieved pertained to and described the assessment of ASD in children with VIB. The following themes emerged: appropriateness of commonly used ASD assessment tools for children with VIB, modification of pre-existing ASD assessment tools for a better assessment, creation of new assessment tools for this population, time points of assessment, and professional training and practice guidelines. The reviewed literature highlights that there is still much work to be done to better understand the complex relationship between VIB and ASD, and consensus is needed on how best to go about assessing neurodevelopmental disorders in children with VIB.

Developmental and behavioral phenotypes of pediatric patients with PTEN hamartoma tumor syndrome.

Our study characterized the neurodevelopmental spectrum of individuals with PTEN Hamartoma Tumor Syndrome (PHTS), a syndrome that predisposes to both neurodevelopmental phenotypes and cancer risk. We aim to better understand life-impacting neurodevelopmental features of PHTS. Our study recruited 20 children/adolescents with PHTS, who were then administered assessments for autism spectrum disorder (ASD) and other neurocognitive measures, including assessment of IQ, executive and adaptive functioning, and health-related quality of life. Thirteen individuals (65%) were identified as having ASD, of which five were newly diagnosed during the study. Of those, ASD symptom severity was in the mild-moderate range for 77%. Overall, IQ was in the average range, with a mean of 92.61 (SD 24.45, p = 0.5), though there was a non-statistically significant trend toward individuals without ASD having a higher mean IQ (102.7 vs 82.3; p = 0.1). Subjects had significant impairment in processing speed (mean 75.38, SD 24.75, p < 0.05), decreased adaptive functioning skills across all domains, and a trend toward having more executive functioning problems. Individuals with PHTS are at increased risk of neurodevelopmental disorders, including ASD and impaired executive and adaptive functioning. Although clear guidelines exist for cancer surveillance for individuals with PHTS, additional guidelines and screening for neurodevelopmental disorders are warranted.

Patterns of Referral and Clinical Presentation of Autism Spectrum Disorder in Children at Al Jalila Children Specialty Hospital: A Retrospective Descriptive Study

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is gradually gaining popularity. However, there is minimal scientific research exploring the clinical presentation of this highly complex disorder and the general understanding of such varying presentations by general practitioners and pediatric physicians. Objective: This study aims to explore the patterns of referral of patients with ASD to a tertiary hospital in Dubai, United Arab Emirates, and to understand better and analyze the general knowledge of referring physicians through their impression of the patient’s clinical presentation Methods: This is a retrospective descriptive study where all the data were collected electronically from past medical records and referral documents taken from Al Jalila Children's Specialty Hospital (AJCH). Patients who were referred were later assessed by the neurodevelopmental specialists using the NICE Guidelines. The data were analyzed statistically and described using different statistical tools. These results were then considered in light of the local clinician's needs for diagnostic support and the possibility of developing a national screening program for ASD. Results: Within a sample of 90 referrals, 69 (76.7%) were predominantly male, 73 (81.1%) were Emirati nationals, and the majority fell within the age group of 0 to 5 years old. Of the 90 referred patients, 53 (58.9%) were diagnosed with ASD by neurodevelopmental specialists using the NICE Guidelines. Physicians were keen on picking up features of speech and language deficits, hyperactivity, and inappropriate or atypical behavior. However, they seemed hesitant and required further assessment when interpreting and reporting features revolving around social communicative skills, interaction and rapport, and evaluating patients’ ideas and imagination. Conclusion: Physicians practicing in the United Arab Emirates (UAE) are advised to assess the full range of clinical symptoms of ASD according to their respective categories as listed in the National Institute for Health and Care Excellence (NICE) guidelines. Furthermore, the Dubai Health Authority (DHA) has published the first edition document titled “Dubai Clinical Practice Guidelines for Autism Spectrum Disorder (ASD) in Children and Adolescents,” designed to align physician recognition and practice with the government's objectives toward all individuals with ASD. Their achievement aligns with the goals of our study as it will make the clinical recognition of ASD clinically more sound and prevent misdiagnosis. Finally, the authors hope that this study aids in developing a national screening program dedicated to general practitioners and pediatric physicians in providing a structured assessment for ASD, which in return will result in early intervention by neurodevelopmental specialists for further diagnosis and management, ultimately improving the quality of life of the patients and their families.

Diagnostic assessment of autism spectrum disorder in transgender and gender diverse youth

Objective: Higher prevalence of autism spectrum disorder (ASD) diagnosis and associated traits has been observed among transgender and gender diverse (TGD) youth, and the number of TGD youth requesting evaluation for autism is growing. This study explored the demographic and clinical profiles of TGD youth evaluated in a specialty autism clinic. Method: Retrospective autism evaluation results for 41 TGD youth aged 5–18 years and 67 cisgender-matched controls were included in the study. Results: Approximately, half of TGD youth were diagnosed with autism (TGDASD+; n = 19 vs. TGDASD−; n = 22). There were no group differences in sex assigned at birth, gender identity, FSIQ, race, or ethnicity. Compared to TGDASD− (allistic) youth, TGD autistics had significantly lower adaptive functioning and were more likely to have an IEP eligibility of ASD. Anxiety and mood disorders were more common in TGD youth, whereas language disorders were more prevalent in cisgender controls. Attention-Deficit/Hyperactivity Disorder (ADHD) was more common among TGDASD− youth (72%) than TGDASD+ youth (47%), though not significantly. Conclusions: TGD youth with school-based IEP eligibilities of ASD and lower adaptive functioning are more likely to be diagnosed with ASD upon medical evaluation. The combination of identifying as TGD and having ADHD may heighten suspicion for ASD. Asking about gender identity during autism evaluations for children middle school-aged and older is recommended.

Identifying Autism Spectrum Disorder in a High-risk Follow-up Program through Quality Improvement Methodology.

Children born prematurely are at increased risk for autism spectrum disorder (ASD). ASD can be diagnosed between 18 and 24 months of age, but access barriers and medical complexity can delay diagnosis. ASD screening was implemented in a high-risk infant follow-up program using QI methodology. The project aimed to screen 60% of children and refer 90% of those with positive screens. The team developed a standardized workflow to administer the M-CHAT-R/F to HRIF patients between the ages of 16-22 months. Telehealth ASD assessment, using the TELE-ASD-PEDS, was conducted for those who screened positive. Monthly team meetings were held to implement change cycles and review the impact of the previous month's change. Within 7 months of program implementation, ASD screening exceeded the 60% aim. The program referred 72% of patients who screened as medium/high risk on the M-CHAT-R/F. The remaining patients were not referred per provider discretion. Twenty-seven percent of patients who received an autism evaluation received an ASD diagnosis. The average age at diagnosis was 22.5 months. An ASD screening protocol was implemented for patients enrolled in a high-risk infant follow-up program. Patients identified as at risk for ASD received an expedited telehealth ASD evaluation. The screening protocol was maintained for 13 months and is now part of the standard workflow. Screening has been expanded to other HRIF clinics, and evaluation appointments have been added to meet access needs. QI methodology is an effective tool for implementing ASD screening and referral in multidisciplinary HRIF programs.

Exploring the presence of a sex-specific phenotype of autism spectrum disorder in a random cohort of males and females: a retrospective case note service audit

Purpose Recent research has proposed a specific female phenotype within autism spectrum disorder (ASD). It suggests females exhibit differences in social communication styles with higher levels of camouflaging and compensatory strategies, as well as variance in restrictive repetitive behaviours (RRBs); however, many existing studies have been based on either small, disproportionate or child and adolescent samples, leaving questions about the specific phenotype. This study aims to explore the sex difference and phenotype in a clinic sample of individuals diagnosed with autism. Design/methodology/approach A service evaluation of sex/ gender differences on 150 historical ASD assessment reports (75 males, 75 females) using a 103-item questionnaire developed from a quantitative review of existing literature was undertaken. Findings Females camouflaged more significantly than males in five different areas (thinking how to act next, preparing conversation in advance, making lists of prompts/social responses, wearing a mask/acting, less monotone voice); however, these were not maintained in post-analysis correction. Originality/value This study points the evidence towards a different phenotype of Autism that is more common in women than men rather than a unique female phenotype.

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.

Autism spectrum disorder, extremism and risk assessment.

To date, there is no evidence supporting the existence of an association between Autism Spectrum Disorder (ASD) and extremism in the general population. However, there is increasing recognition that several features of ASD may provide the context of vulnerability to engage in extremist behaviour. This paper sets out the case for a dedicated clinical approach to better integrate clinical risk appraisal processes with an assessment of ASD individuals' vulnerabilities within the Criminal Justice System. In this paper the Framework for the Assessment of Risk & Protection in Offenders on the Autistic Spectrum (FARAS): A Guide for Risk Assessors Working with Offenders on the Autistic Spectrum is explored. In developing the FARAS, Al-Attar proposed seven facets of ASD that 'may have different functional links with push and pull factors to terrorism' (p. 928), which include circumscribed interests; rich vivid fantasy and impaired social imagination; need for order, rules, rituals, routine and predictability; obsessionality, repetition and collecting; social interaction and communication difficulties; cognitive styles and Sensory processing. We describe the FARAS within the context of the most widely used clinical risk appraisal 'aide memoire' instruments integral to the Structured Professional Judgement of risk process, namely the HCR20v3.

Literature Survey Paper on Epilepsy and Autism Spectrum Disorder Detection and Analysis Using Machine Learning

The detection and cure of epilepsy and autism spectrum disorder (ASD) are significantly complicated by their co-occurrence. This survey research investigates an integrated method for identifying ASD using behavioural characteristic questionnaires and epilepsy using EEG corpus inside a single system. We provide an overview of all the relevant research, emphasizing the difficulties in diagnosing each of these disorders separately and in combination. Our suggested approach combines behavioural questionnaire assessments for ASD with EEG-based analysis for epilepsy detection in an effort to improve diagnostic accuracy and expedite the evaluation process. This study examines the approaches, difficulties, and developments in both domains, providing perspectives on possible overlaps and prospects for further investigation. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics.

Psychological Assessment of Autism Spectrum Disorders using Machine Learning

This research aims to advance the field of autism spectrum disorder (ASD) assessment by proposing a machine learning-based approach tailored for school and community settings. Leveraging a diverse dataset encompassing behavioral, physiological, and neuroimaging data, we apply advanced machine learning algorithms to develop predictive models for early ASD detection. Our study integrates expert interviews to validate the clinical utility of these models and explores ethical considerations surrounding data privacy and bias. Preliminary results show promising accuracy in ASD identification. This research contributes to a more accessible and objective ASD assessment, with implications for early intervention and inclusive support, particularly in educational and community contexts.

COVID-19: Remote diagnostic assessment of children with suspected Autism Spectrum Disorder, a selected literature review

Diagnostic evaluations of neurodevelopmental disorders dramatically reduced and slowed down following the disruptions caused by Sars-CoV-2 pandemic. For this, it was necessary to identify solutions allowing diagnostic assessment, screening, and early patient care by implementing remote diagnostic protocols respecting the restrictions imposed by the lockdown. Centres and services specialized in Autism Spectrum Disorder (ASD) had to quickly implement innovative diagnostic protocols able to satisfy patients' needs, minimizing the chances of spreading the virus at the same time. This work collects the studies conducted so far on remote assessment of ASD both prior to and relating to the period of the SARS-CoV-2 pandemic. It also explores standard diagnostic tools that have been adapted for remote administration, in order to evaluate strengths and limitations of these methodologies. Remote assessment methodologies were initially employed to overcome the difficulties of long-distance diagnoses and to respond to families' need for continuity of public and private services. These studies underlined the validity of some remote diagnostic protocols and how telediagnosis may enable the diagnostic evaluation, making it more accessible and thus favouring timely intervention. We discussed the opportunity to integrate in person and remote approaches for diagnosing neurodevelopmental conditions, even in the post covid emergency period.

Autistic traits, sensory sensitivity and eating disturbances in a sample of young adults referring to a generalized mental health clinic

PurposeThe relationship between autistic traits and eating disturbances has been given considerable attention over the last decades. The rise of a dimensional approach to psychopathology has expanded the way we think about autism, acknowledging that subthreshold autistic manifestations span across the general population and are more pronounced in psychiatric patients. Here we investigated the prevalence of eating disorders and its potential relationship with autistic traits and sensory sensitivity in a group of patients who were referred for the first time to a mental health outpatient clinic, without a formal diagnosis yet.Methods259 young adults (between 18 and 24 years old) completed: the Eating Attitude Test (EAT-26), the Swedish Eating Assessment for Autism Spectrum Disorders (SWEAA), the Autism Quotient (AQ), the Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R), and the Sensory Perception Quotient—Short Form 35 item (SPQ-SF35).Results23.55% of participants scored above the cut-off at the EAT-26, suggesting that they presented a risk for eating disorders and should be assessed by a specialized clinician; associations emerged between hypersensitivity in the touch and vision domain and both the EAT-26 and the SWEAA; the presence of autistic traits was largely associated with eating disturbances.ConclusionsThis study underlines the significance of the eating domain as a central psychopathological feature in the distress experienced by young adults with general psychiatric symptoms and psychological suffering; it adds evidence to the association between autistic traits and eating disorders and opens to new research questions about the role of subthreshold autistic traits in general psychopathology.Level of evidence: Level I: Evidence obtained from experimental studies.

Identifying key players in complex networks via network entanglement

Empirical networks exhibit significant heterogeneity in node connections, resulting in a few vertices playing critical roles in various scenarios, including decision-making, viral marketing, and population immunization. Thus, identifying key vertices is a fundamental research problem in Network Science. In this paper, we introduce vertex entanglement (VE), an entanglement-based metric capable of quantifying the perturbations caused by individual vertices on spectral entropy, residing at the intersection of quantum information and network science. Our analytical analysis reveals that VE is closely related to network robustness and information transmission ability. As an application, VE offers an approach to the challenging problem of optimal network dismantling, and empirical experiments demonstrate its superiority over state-of-the-art algorithms. Furthermore, VE also contributes to the diagnosis of autism spectrum disorder (ASD), with significant distinctions in hub disruption indices based on VE between ASD and typical controls, promising a diagnostic role for VE in ASD assessment.

Investigating the early signs of ASD in preschool children in the UAE as a model for GCC countries

PurposeIdentifying autism spectrum disorder (ASD) in children’s early years significantly impacts their academic performance and future life. It allows them to receive special support services and the required early intervention. Preschool teachers spend considerable time with their students, which allows them to observe their behaviors in different settings like classrooms and playgrounds. However, teachers need to be knowledgeable of ASD early assessment to detect them. Whereas, all well-known ASD assessment tools are designed for populations from different cultures than the Gulf Cooperation Councils' (GCC) culture. This study emphasizes the importance of preschool age, from 3 to 5, in indicating children's early signs of ASD. This paper aims to indicate the early signs of ASD in preschool children in the UAE as a model for other GCC countries.Design/methodology/approachA qualitative method consisting of interviews with preschool teachers and the ASD children’s parents was conducted to investigate the ASD early symptoms in children.FindingsThe findings of the study show that there is a direct connection between the opinions expressed by preschool teachers and the parents of children with ASD (Autism Spectrum Disorder). These results also highlight the presence of early indications of ASD among preschool-aged children in the United Arab Emirates, such as difficulties in communication, repetitive actions, delayed speech, excessive focus on tablets, trouble paying attention, hyperactivity, sleep problems, and aggressive behavior.Originality/valueThe outcomes suggest a strong connection between the feedback from preschool teachers and the parents of children with ASD. The study uncovers initial indications of ASD in preschool-aged children from the United Arab Emirates, such as difficulties in communication, repetitive actions, delayed speech, excessive fascination with tablets, problems with attention, hyperactivity, sleep disturbances, and aggressive behavior.

Efficient Diagnosis of Autism Spectrum Disorder Using Optimized Machine Learning Models Based on Structural MRI

Autism spectrum disorder (ASD) affects approximately 1.4% of the population and imposes significant social and economic burdens. Because its etiology is unknown, effective diagnosis is challenging. Advancements in structural magnetic resonance imaging (sMRI) allow for the objective assessment of ASD by examining structural brain changes. Recently, machine learning (ML)-based diagnostic systems have emerged to expedite and enhance the diagnostic process. However, the expected success in ASD was not yet achieved. This study evaluates and compares the performance of seven optimized ML models to identify sMRI-based biomarkers for early and accurate detection of ASD in children aged 5 to 10 years. The effect of using hyperparameter tuning and feature selection techniques are investigated using two public datasets from Autism Brain Imaging Data Exchange Initiative. Furthermore, these models are tested on a local Saudi dataset to verify their generalizability. The integration of the grey wolf optimizer with a support vector machine achieved the best performance with an average accuracy of 71% (with further improvement to 71% after adding personal features) using 10-fold Cross-validation. The optimized models identified relevant biomarkers for diagnosis, lending credence to their truly generalizable nature and advancing scientific understanding of neurological changes in ASD.