The diagnosis of ASD with MRI: a systematic review and meta-analysis.

While diagnosing autism spectrum disorder (ASD) based on an objective test is desired, the current diagnostic practice involves observation-based criteria. This study is a systematic review and meta-analysis of studies that aim to diagnose ASD using magnetic resonance imaging (MRI). The main objective is to describe the state of the art of diagnosing ASD using MRI in terms of performance metrics and interpretation. Furthermore, subgroups, including different MRI modalities and statistical heterogeneity, are analyzed. Studies that dichotomously diagnose individuals with ASD and healthy controls by analyses progressing from magnetic resonance imaging obtained in a resting state were systematically selected by two independent reviewers. Studies were sought on Web of Science and PubMed, which were last accessed on February 24, 2023. The included studies were assessed on quality and risk of bias using the revised Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate random-effects model was used for syntheses. One hundred and thirty-four studies were included comprising 159 eligible experiments. Despite the overlap in the studied samples, an estimated 4982 unique participants consisting of 2439 individuals with ASD and 2543 healthy controls were included. The pooled summary estimates of diagnostic performance are 76.0% sensitivity (95% CI 74.1-77.8), 75.7% specificity (95% CI 74.0-77.4), and an area under curve of 0.823, but uncertainty in the study assessments limits confidence. The main limitations are heterogeneity and uncertainty about the generalization of diagnostic performance. Therefore, comparisons between subgroups were considered inappropriate. Despite the current limitations, methods progressing from MRI approach the diagnostic performance needed for clinical practice. The state of the art has obstacles but shows potential for future clinical application.

Autism spectrum disorder: evaluation of community-based screening program.

Background/aim: This study was conducted to evaluate the results of autism spectrum disorder (ASD) screenings conducted in a region of Istanbul between 2018 and 2023. Materials and methods: This descriptive study was conducted between April 2018 and February 2023 among 25,839 children aged between 18-36 months who had been screened for autism spectrum disorder in Sultanbeyli, Istanbul. Children between 18-36 months are examined and a form consisting of 5 questions and typical symptoms of ASD is filled. Each question is answered as yes or no. Answering yes to at least one of the questions is sufficient to direct them to child psychiatry. Results: Between 2018 and 2023, a total of 25,839 children were screened for autism spectrum disorders, 1449 children were found to be at risk, and 88 were diagnosed with autism spectrum disorder. According to the sex distribution of the children, the male:female ratio is 3.6:1. The 5-year prevalence was found to be 0.9%. With the effect of the pandemic between 2020 and 2021, screening rates have decreased and the number of diagnoses has decreased. The most common symptom among those diagnosed is delay in speaking, and the second is inability to make eye contact. Conclusion: Autism spectrum disorder is a developmental disorder whose prevalence is increasing globally and for which early diagnosis is important. To recognize this disease, it is necessary to increase screening and raise awareness among families. This study will also shed light on future studies on this subject.

Autism spectrum: parents' perspectives reflecting the different needs of different families.

BACKGROUND: Parents of children on the autism spectrum often face great challenges in the care of their child. Early support tailored to families' individual needs is therefore crucial for the development and quality of life of both children on the autism spectrum and their families. However, to date it is unclear whether the support available meets the parents' needs. STUDY AIM: To investigate how the system of care, support, and therapies for children on the autism spectrum is perceived by their parents. METHOD: A total of 57 parents of Swiss children on the autism spectrum participated in an online survey, and 20 of them participated in additional semi-structured interviews. RESULTS: We found that parents of children on the autism spectrum may face substantial challenges and that social support is essential. Two thirds of the participating parents reported a long and difficult diagnostic process as challenging, and 60% expressed their need for closer follow-up after diagnosis and more support. Only one third of the parents stated that they manage their everyday lives well, whereas 17.5% felt exhausted, and more than half of the parents responded that they felt challenged. One fifth indicated that they had poor family support, and half reported substantial financial challenges. At the same time, most families also emphasize how important their neurodivergent children are to the family`s life together. CONCLUSION: It is important that primary pediatricians not only initiate the diagnostic process, but also assess the different needs of the different family independent of the diagnosis and, if necessary, initiate adequate measures or guide parents to institutions in charge. Parents who do not actively express their individual needs should nevertheless be advised about support services, including financial counseling. The positive aspects mentioned by families can be emphasized and used as resources to improve their quality of life.

Early treatment for children with mental health problems and genetic conditions through a parenting intervention (The GAP): study protocol for a pragmatic randomized controlled trial.

BACKGROUND: Children with genetic conditions are at increased risk for mental health and neurodevelopmental problems, often accompanied by significant parental distress. Genetic and family factors can impact children and parents' mental health. Early parenting interventions, like the Incredible Years® programs, have demonstrated to improve parental distress and children's mental health. The recent version for young children with language delays or autism spectrum disorder (IY-ASLD®) has shown to be feasible and effective to support parents in their children's developmental trajectories. The effectiveness of treatments for children with genetic conditions and neurodevelopmental problems is largely unexplored, leaving significant gaps in evidence-based options. Clinicians lack guidance, especially when patients exhibit language or social communication impairments but do not meet diagnostic criteria for a full-blown autism spectrum disorder (ASD). We aim to fill this gap, providing evidence on the feasibility and effectiveness of the IY-ASLD® intervention for such patients. METHODS: We designed a prospective multicenter pragmatic randomized controlled trial including approximately 68 children aged 3 to 7 years, recruited from three tertiary care reference hospitals. Inclusion criteria will necessitate genetic confirmation of a neurodevelopmental disorder along with language, communication, or socialization difficulties. Individuals with an ASD diagnosis will be excluded. All subjects are included in a territorial register for rare conditions (ReMin, Registre de Malalties Minoritàries de Catalunya). Families will randomly be assigned to the intervention or the control group. The intervention will be held online by clinical psychologists and child and adolescent psychiatrists. DISCUSSION: Our group has recently piloted the online implementation of the IY-ASLD® intervention for the first time in Spain, for parents of children with language delays, socialization difficulties, or ASD, but not genetically determined. Our multicenter research consortium is well-positioned to recruit patients with rare conditions and implement efficient treatment pathways within the National Health System. Given the geographical dispersion of families affected by rare conditions, the online format offers logistical advantages and improved therapy access, enhancing homogeneity across all patients. The results of this study will inform clinicians and policymakers about evidence-based treatment options for this vulnerable and overlooked group of young children. TRIAL REGISTRATION: ClinicalTrials.gov NCT06125093 . Date of registration: first submitted 2023-10-23; first posted 2023-11-09. URL of trial registry record.

Urine manganese, cadmium, lead, arsenic, and selenium among autism spectrum disorder children in Kuala Lumpur.

Background: The development of autism spectrum disorder (ASD) may stem from exposure to environmental pollutants such as heavy metals. The primary objective of this study is to determine the role of heavy metals of concern such as manganese (Mn), cadmium (Cd), lead (Pb), arsenic (As), and essential trace element selenium (Se) among ASD children in Kuala Lumpur, Malaysia. Method: A total of 155 preschoolers in Kuala Lumpur between the ages 3 to 6 participated in an unmatched case-control study, comprising ASD children (n = 81) recruited from an early intervention program for autism, and 74 children without autism who were recruited from public preschools. Urine samples were collected at home, delivered to the study site, and transported to the environmental lab within 24 hours. Inductively coupled plasma mass spectrometry (ICP-MS) was applied to measure the concentration of heavy metals in the samples. Data were analysed using bivariate statistical tests (Chi-square and T-test) and logistic regression models. Result: This study demonstrated that Cd, Pb, and As urine levels were significantly greater in children without autism relative to those affected with ASD (p < 0.05). No significant difference was in the levels of Se (p = 0.659) and Mn (p = 0.875) between children with ASD and the control group. The majority of children in both groups have urine As, Pb, and Cd values lower than 15.1 µg/dL, 1.0 µg/dL, and 1.0 µg/dL, respectively which are the minimal risk values for noncarcinogenic detrimental human health effect due to the heavy metal's exposure . Factors associated with having an ASD child included being a firstborn, male, and higher parental education levels (adjusted odds ratios (aOR) > 1, p < 0.05). Conclusion: Preschoolers in this study demonstrated low levels of heavy metals in their urine samples, which was relatively lower in ASD children compared to the healthy matched controls. These findings may arise from the diminished capacity to excrete heavy metals, especially among ASD children, thereby causing further accumulation of heavy metals in the body. These findings, including the factors associated with having an ASD child, may be considered by healthcare professionals involved in child development care, for early ASD detection. Further assessment of heavy metals among ASD children in the country and interventional studies to develop effective methods of addressing exposure to heavy metals will be beneficial for future reference.

Atypical Autism: Causes, Diagnosis and Support.

Autism spectrum disorder (ASD) is a group of neurobehavioral disorders defined by persistent deficits in social communication and social interactions with repetitive behaviors, and it is typically diagnosed within the first three years of life [...].

Screening for Autism Spectrum Disorder in Young Children: Still Not Enough Evidence.

BACKGROUND: Early detection of autism spectrum disorder (ASD) has the potential to significantly reduce the impact of the condition, however previous reviews have found little evidence to support screening programs for ASD in young children. METHODS: We conducted a review with the aim of updating evidence on 3 aspects: (a) diagnostic stability of ASD in young children; (b) accuracy of ASD screening tools in young children; and (c) the benefits of early interventions in screen-detected young children with ASD. RESULTS: A total of 33 studies were included in our review. Five studies looking at diagnostic stability reported estimates ranging from 71.9% to 100%, however the majority only included a follow-up of 24 months and all studies raised concerns regarding the risk of bias due particularly to lack of blinding, sample size, and patient flow. A total of 25 studies, reported in 26 articles, were identified that reported accuracy data on 11 screening tools. Most of the reports were concerned with versions of M-CHAT, reporting sensitivity estimates from 0.67 to 1.0; however, many of these were deemed to be of high risk of bias due to lack of blinding and follow-up. Four studies reported on early interventions in screen-detected children; however, the majority did not find significant improvements on the relevant outcomes. CONCLUSIONS: Overall, the evidence on screening for ASD in young children captured by this review is not conclusive regarding the 3 aspects of screening in this population. Future studies should attempt to ensure blinded diagnostic assessments, include longer follow-up periods and limit attrition.

Exploring Implicit Biological Heterogeneity in ASD Diagnosis Using a Multi-Head Attention Graph Neural Network.

BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental disorder exhibiting heterogeneous characteristics in patients, including variability in developmental progression and distinct neuroanatomical features influenced by sex and age. Recent advances in deep learning models based on functional connectivity (FC) graphs have produced promising results, but they have focused on generalized global activation patterns and failed to capture specialized regional characteristics and accurately assess disease indications. METHODS: To overcome these limitations, we propose a novel deep learning method that models FC with multi-head attention, which enables simultaneous modeling of the intricate and variable patterns of brain connectivity associated with ASD, effectively extracting abnormal patterns of brain connectivity. The proposed method not only identifies region-specific correlations but also emphasizes connections at specific, transient time points from diverse perspectives. The extracted FC is transformed into a graph, assigning weighted labels to the edges to reflect the degree of correlation, which is then processed using a graph neural network capable of handling edge labels. RESULTS: Experiments on the autism brain imaging data exchange (ABIDE) I and II datasets, which include a heterogeneous cohort, showed superior performance over the state-of-the-art methods, improving accuracy by up to 3.7%p. The incorporation of multi-head attention in FC analysis markedly improved the distinction between typical brains and those affected by ASD. Additionally, the ablation study validated diverse brain characteristics in ASD patients across different ages and sexes, offering insightful interpretations. CONCLUSION: These results emphasize the effectiveness of the method in enhancing diagnostic accuracy and its potential in advancing neurological research for ASD diagnosis.

Familial Recurrence of Autism: Updates From the Baby Siblings Research Consortium.

OBJECTIVES: Autism spectrum disorder (ASD) is estimated to be ∼10 times higher in children with versus without an autistic sibling in population-based studies. Prospective studies of infant siblings have revealed even higher familial recurrence rates. In the current prospective longitudinal study, we provide updated estimates of familial ASD recurrence using a multinational database of infants with older autistic siblings. METHODS: Data were collated across 18 sites of the Baby Siblings Research Consortium, an international network studying the earliest manifestations of ASD. A total of 1605 infants with an older autistic sibling were followed from early in life to 3 years, when they were classified as ASD or non-ASD. Hierarchical generalized linear modeling, with site as a random effect, was used to examine predictors of recurrence in families and calculate likelihood ratios. RESULTS: A total of 20.2% of siblings developed ASD, which is not significantly higher than the previously reported rate of 18.7%. Male infant sex and >1 older affected sibling were significant predictors of familial recurrence. Proband sex also influenced recurrence rates, with siblings of female probands significantly more likely to develop ASD than siblings of male probands. Race and maternal education were also associated with recurrence in families. CONCLUSIONS: The familial recurrence rate of ASD, as measured in infant sibling studies, has not changed appreciably since previous estimates were made in 2011. Younger siblings of autistic children, particularly those who are male, have an affected female sibling, multiple affected siblings, or are impacted by social inequities, should be closely monitored and promptly referred for diagnostic evaluation.

Developing a phenotype risk score for tic disorders in a large, clinical biobank.

Tics are a common feature of early-onset neurodevelopmental disorders, characterized by involuntary and repetitive movements or sounds. Despite affecting up to 2% of children and having a genetic contribution, the underlying causes remain poorly understood. In this study, we leverage dense phenotype information to identify features (i.e., symptoms and comorbid diagnoses) of tic disorders within the context of a clinical biobank. Using de-identified electronic health records (EHRs), we identified individuals with tic disorder diagnosis codes. We performed a phenome-wide association study (PheWAS) to identify the EHR features enriched in tic cases versus controls (n = 1406 and 7030; respectively) and found highly comorbid neuropsychiatric phenotypes, including: obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, and anxiety (p < 7.396 × 10-5). These features (among others) were then used to generate a phenotype risk score (PheRS) for tic disorder, which was applied across an independent set of 90,051 individuals. A gold standard set of tic disorder cases identified by an EHR algorithm and confirmed by clinician chart review was then used to validate the tic disorder PheRS; the tic disorder PheRS was significantly higher among clinician-validated tic cases versus non-cases (p = 4.787 × 10-151; ß = 1.68; SE = 0.06). Our findings provide support for the use of large-scale medical databases to better understand phenotypically complex and underdiagnosed conditions, such as tic disorders.

New advances in the diagnosis and treatment of autism spectrum disorders.

Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders that affect individuals' social interactions, communication skills, and behavioral patterns, with significant individual differences and complex etiology. This article reviews the definition and characteristics of ASD, epidemiological profile, early research and diagnostic history, etiological studies, advances in diagnostic methods, therapeutic approaches and intervention strategies, social and educational integration, and future research directions. The highly heritable nature of ASD, the role of environmental factors, genetic-environmental interactions, and the need for individualized, integrated, and technology-driven treatment strategies are emphasized. Also discussed is the interaction of social policy with ASD research and the outlook for future research and treatment, including the promise of precision medicine and emerging biotechnology applications. The paper points out that despite the remarkable progress that has been made, there are still many challenges to the comprehensive understanding and effective treatment of ASD, and interdisciplinary and cross-cultural research and global collaboration are needed to further deepen the understanding of ASD and improve the quality of life of patients.

ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.

The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes ASD-SWNet, a new shared-weight feature extraction and classification network. It resolves the issue found in previous studies of inefficiently integrating unsupervised and supervised learning, thereby enhancing diagnostic precision. The approach utilizes functional magnetic resonance imaging to improve diagnostic accuracy, featuring an autoencoder (AE) with Gaussian noise for robust feature extraction and a tailored convolutional neural network (CNN) for classification. The shared-weight mechanism utilizes features learned by the AE to initialize the convolutional layer weights of the CNN, thereby integrating AE and CNN for joint training. A novel data augmentation strategy for time-series medical data is also introduced, tackling the problem of small sample sizes. Tested on the ABIDE-I dataset through nested ten-fold cross-validation, the method achieved an accuracy of 76.52% and an AUC of 0.81. This approach surpasses existing methods, showing significant enhancements in diagnostic accuracy and robustness. The contribution of this paper lies not only in proposing new methods for ASD diagnosis but also in offering new approaches for other neurological brain diseases.

Autism spectrum disorders detection based on multi-task transformer neural network.

Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising diagnostic tool, but challenging due to the complex and unclear etiology of autism. And it is difficult to effectively identify ASD patients with a single data source (single task). Therefore, to address this challenge, we propose a novel multi-task learning framework for ASD identification based on rs-fMRI data, which can leverage useful information from multiple related tasks to improve the generalization performance of the model. Meanwhile, we adopt an attention mechanism to extract ASD-related features from each rs-fMRI dataset, which can enhance the feature representation and interpretability of the model. The results show that our method outperforms state-of-the-art methods in terms of accuracy, sensitivity and specificity. This work provides a new perspective and solution for ASD identification based on rs-fMRI data using multi-task learning. It also demonstrates the potential and value of machine learning for advancing neuroscience research and clinical practice.

Microcephaly type 22 and autism spectrum disorder: A case report and review of literature.

INTRODUCTION: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with a multifaceted etiology. This case report explores the ischemic cryptogenic vascular dissection as a potential underlying cause of ASD. METHODS: A 9-year-old child presented with symptoms of ASD, including social interaction difficulties, repetitive behaviors, and cognitive challenges. Despite conventional ASD treatments, significant improvement was only observed after addressing an underlying ischemic cryptogenic vascular dissection identified through DCE-CT. RESULTS: Following a reconstructive treatment approach to the vascular dissection, the patient showed marked improvement in cognitive functions, social abilities, and a reduction in ASD-related symptoms whether during the perioperative period or during approximately 5-month follow-up. CONCLUSION: This case suggests that ischemic cryptogenic vascular dissection may contribute to the symptoms of ASD. Identifying and treating underlying vascular anomalies may offer a new avenue for mitigating ASD symptoms, emphasizing the need for comprehensive diagnostic estimations in ASD management.