Challenges Surrounding the Diagnosis of Autism in Children

Take a Closer Look: Considerations for Autism Spectrum Disorder Assessment in Female Children and Adolescents

Assessment for autism spectrum disorder (ASD) in the pediatric female population entails unique diagnostic complexities. Females are often misdiagnosed, undiagnosed, or receive an ASD diagnosis at a later age than males. Male bias in ASD, masking behaviors, cultural norms, and overlapping neurodevelopmental comorbidities (such as attention deficit/hyperactivity disorder and intellectual disability) contribute to this phenomenon. The authors present two clinical cases evaluated in an interdisciplinary developmental behavioral pediatrics (DBP) team to highlight these considerations. Cases describe adolescent and school aged females with medical complexity who did not initially appear to have ASD symptoms but later were diagnosed with ASD. Patient anonymity is preserved. Best practice recommendations are discussed. Shared decision making, intentional history taking, thorough observation of behavior and restrictive/repetitive/sensory interests in multiple settings, and attention to social communication in the context of cognitive capacity are essential for ASD assessment in pediatric females.

A multi-frequency approach of the altered functional connectome for autism spectrum disorder identification

Autism spectrum disorder stands as a multifaceted and heterogeneous neurodevelopmental condition. The utilization of functional magnetic resonance imaging to construct functional brain networks proves instrumental in comprehending the intricate interplay between brain activity and autism spectrum disorder, thereby elucidating the underlying pathogenesis at the cerebral level. Traditional functional brain networks, however, typically confine their examination to connectivity effects within a specific frequency band, disregarding potential connections among brain areas that span different frequency bands. To harness the full potential of interregional connections across diverse frequency bands within the brain, our study endeavors to develop a novel multi-frequency analysis method for constructing a comprehensive functional brain networks that incorporates multiple frequencies. Specifically, our approach involves the initial decomposition of functional magnetic resonance imaging into distinct frequency bands through wavelet transform. Subsequently, Pearson correlation is employed to generate corresponding functional brain networks and kernel for each frequency band. Finally, the classification was performed by a multi-kernel support vector machine, to preserve the connectivity effects within each band and the connectivity patterns shared among the different bands. Our proposed multi-frequency functional brain networks method yielded notable results, achieving an accuracy of 89.1%, a sensitivity of 86.67%, and an area under the curve of 0.942 in a publicly available autism spectrum disorder dataset.

The Assessment of Comprehensive Care for Autistic Children Provided by Polish Primary Healthcare Physicians in Parental View

Objectives. Important factor in caring for ASD patients is the successful cooperation between the physician and the parent. Methods. The online survey has been conducted. For statistical analysis the t-student test and the U-Mann Whitney test were performed to compare 2 variables, then the Kruskal-Wallis test or ANOVA as well as The Pearson correlation and Sperman's rank correlation were performed. Results. 80.5% of respondent's PHPs did not notice the child's developmental difficulties. 22.02% of respondents can speak to their PHP about a child's difficulties. PHPs take different strategies to make patient contact with the healthcare system easier. We discovered statistically significant differences in the assessment of PHPs' abilities depending on the physicians' specialty, the children's age, the age at ASD diagnosis. Conclusion. The care for autistic patients provided by Polish PHPs is insufficient and the topic needs to be addressed immediately.

Females with autism spectrum disorders show stronger DNA methylation signatures than males in perinatal tissues

Autism spectrum disorder (ASD) comprises a group of neurodevelopmental conditions currently diagnosed by behavioral assessment in childhood, with reported underdiagnosis in females. Though diagnosis in early life is linked to improved outcomes, we currently lack objective screening tools for newborns. To address this gap, we sought to identify a sex-specific DNA methylation signature for ASD using perinatal tissues that reflect dysregulation in the brain. DNA methylation was assayed from ASD and typically developing (TD) newborn blood, umbilical cord blood, placenta, and post-mortem cortex samples using whole genome bisulfite sequencing (WGBS) in a total of 511 samples. We found that methylation levels of differentially methylated regions (DMRs) differentiated samples by ASD diagnosis in females more than males across the perinatal tissues. We tested three theories for ASD sex differences in newborn blood, finding epigenetic support for an X chromosome-related female protective effect, as well as a high replication rate of DMRs (48.1%) in females across two independent cohorts. In our pan-tissue analysis, three genes (X-linked BCOR, GALNT9, OPCML) mapped to ASD DMRs replicated in all four female tissues. ASD DMRs from all tissues were enriched for neuro-related processes (females) and SFARI ASD-risk genes (females and males). Overall, we found a highly replicated methylation signature of ASD in females across perinatal tissues that reflected dysregulation in the brain and involvement of X chromosome epigenetics. This comparative study of perinatal tissues shows the promise of newborn blood DNA methylation biomarkers for early detection of females at risk for ASD and emphasizes the importance of sex-stratification in ASD studies.

Isolation and Characterization of Cell-Free DNA from Cerebral Organoids

Early detection of neurological conditions is critical for timely diagnosis and treatment. Identifying cellular-level changes is essential for implementing therapeutic interventions prior to symptomatic disease onset. However, monitoring brain tissue directly through biopsies is invasive and poses a high risk. Bodily fluids such as blood or cerebrospinal fluid contain information in many forms, including proteins and nucleic acids. In particular, cell-free DNA (cfDNA) has potential as a versatile neurological biomarker. Yet, our knowledge of cfDNA released by brain tissue and how cfDNA changes in response to deleterious events within the brain is incomplete. Mapping changes in cfDNA to specific cellular events is difficult in vivo, wherein many tissues contribute to circulating cfDNA. Organoids are tractable systems for examining specific changes consistently in a human background. However, few studies have investigated cfDNA released from organoids. Here, we examined cfDNA isolated from cerebral organoids. We found that cerebral organoids release quantities of cfDNA sufficient for downstream analysis with droplet-digital PCR and whole-genome sequencing. Further, gene ontology analysis of genes aligning with sequenced cfDNA fragments revealed associations with terms related to neurodevelopment and autism spectrum disorder. We conclude that cerebral organoids hold promise as tools for the discovery of cfDNA biomarkers related to neurodevelopmental and neurological disorders.

Approaches to improving mental health care for autistic children and young people: a systematic review and meta-analysis

Autistic children and young people (CYP) experience mental health difficulties but face many barriers to accessing and benefiting from mental health care. There is a need to explore strategies in mental health care for autistic CYP to guide clinical practice and future research and support their mental health needs. Our aim was to identify strategies used to improve mental health care for autistic CYP and examine evidence on their acceptability, feasibility, and effectiveness. A systematic review and meta-analysis were carried out. All study designs reporting acceptability/feasibility outcomes and empirical quantitative studies reporting effectiveness outcomes for strategies tested within mental health care were eligible. We conducted a narrative synthesis and separate meta-analyses by informant (self, parent, and clinician). Fifty-seven papers were included, with most investigating cognitive behavioral therapy (CBT)-based interventions for anxiety and several exploring service-level strategies, such as autism screening tools, clinician training, and adaptations regarding organization of services. Most papers described caregiver involvement in therapy and reported adaptations to communication and intervention content; a few reported environmental adjustments. In the meta-analyses, parent- and clinician-reported outcomes, but not self-reported outcomes, showed with moderate certainty that CBT for anxiety was an effective treatment compared to any comparison condition in reducing anxiety symptoms in autistic individuals. The certainty of evidence for effectiveness, synthesized narratively, ranged from low to moderate. Evidence for feasibility and acceptability tended to be positive. Many identified strategies are simple, reasonable adjustments that can be implemented in services to enhance mental health care for autistic individuals. Notable research gaps persist, however.

Cluster analysis of teachers report for identifying symptoms of autism spectrum and/or attention deficit hyperactivity in school population: EPINED study

An early detection of Neurodevelopmental Disorders (NDDs) is crucial for their prognosis; however, the clinical heterogeneity of some disorders, such as autism spectrum disorder (ASD) or attention-deficit hyperactivity disorder (ADHD) is an obstacle to accurate diagnoses in children. In order to facilitate the screening process, the current study aimed to identify symptom-based clusters among a community-based sample of preschool and school-aged children, using behavioral characteristics reported by teachers. A total of 6894 children were assessed on four key variables: social communication differences, restricted behavior patterns, restless-impulsiveness, and emotional lability (pre-schoolers) or inattention and hyperactivity-impulsivity (school-aged). From these behavioral profiles, four clusters were identified for each age group. A cluster of ASD + ADHD and others including children with no pathology was clearly identified, whereas two other clusters were characterized by subthreshold ASD and/or ADHD symptoms. In the school-age children, the presence of ADHD was consistently observed with ASD patterns. In pre-schoolers, teachers were more proficient at identifying children who received a diagnosis for either ASD and/or ADHD from an early stage. Considering the significance of early detection and intervention of NDDs, teachers' insights are important. Therefore, promptly identifying subthreshold symptoms in children can help to minimize consequences in social and academic functioning.

Pregnancy Planning and its Association with Autism Spectrum Disorder: Findings from the Study to Explore Early Development

OBJECTIVES

To examine associations between pregnancy planning and autism spectrum disorder (ASD) in offspring.

METHODS

The Study to Explore Early Development (SEED), a multi-site case-control study, enrolled preschool-aged children with ASD, other DDs, and from the general population (POP). Some children with DDs had ASD symptoms but did not meet the ASD case definition. We examined associations between mother's report of trying to get pregnant (pregnancy planning) and (1) ASD and (2) ASD symptomatology (ASD group, plus DD with ASD symptoms group combined) (each vs. POP group). We computed odds ratios adjusted for demographic, maternal, health, and perinatal health factors (aORs) via logistic regression. Due to differential associations by race-ethnicity, final analyses were stratified by race-ethnicity.

RESULTS

Pregnancy planning was reported by 66.4%, 64.8%, and 76.6% of non-Hispanic White (NHW) mothers in the ASD, ASD symptomatology, and POP groups, respectively. Among NHW mother-child pairs, pregnancy planning was inversely associated with ASD (aOR = 0.71 [95% confidence interval 0.56-0.91]) and ASD symptomatology (aOR = 0.67 [0.54-0.84]). Pregnancy planning was much less common among non-Hispanic Black mothers (28-32% depending on study group) and Hispanic mothers (49-56%) and was not associated with ASD or ASD symptomatology in these two race-ethnicity groups.

CONCLUSION

Pregnancy planning was inversely associated with ASD and ASD symptomatology in NHW mother-child pairs. The findings were not explained by several adverse maternal or perinatal health factors. The associations observed in NHW mother-child pairs did not extend to other race-ethnicity groups, for whom pregnancy planning was lower overall.

Comprehension of Ditransitive Constructions in Mandarin-Speaking Children with Developmental Language Disorder and Children with Autism Spectrum Disorder Plus Language Impairment

This study examined and compared the comprehension of Mandarin ditransitive constructions in children with developmental language disorder (DLD) and children with autism spectrum disorder plus language impairment (ALI). Eighteen children with DLD, 17 children with ALI, and 27 age-matched typically developing (TDA) children, participated in a sentence-picture matching task on four patterns of Mandarin ditransitive constructions. Both children with DLD and children with ALI received significantly lower accuracy than TDA children in general and their most common errors were thematic role reversals. However, while children with ALI evinced a generalized deficit in all four patterns, only the comprehension of S1 (Subj. + Vgei + IO + DO) and S3 (Subj. + gei + IO + V + DO) was affected in children with DLD, with that of S2 (Subj. + V + DO + gei + IO) and S4 (Subj. + V + IO + DO) preserved in this population. Additionally, thematic role reversal errors were more dominant in children with DLD than in children with ALI who also committed a relatively higher proportion of Wrong Theme and No Recipient errors. It is concluded that the primary deficit of children with DLD lies in representing dependent relationships between the arguments and the verb as involved in thematic role assignment, but this is less critical in children with ALI, with their performance on the comprehension task possibly also related to other factors associated with the condition. To enhance the development of ditransitive constructions, intervention efforts for children with DLD and children with ALI could focus on strengthening the connection between each argument and its thematic role.

Maternal Thyroid Dysfunction During Pregnancy as an Etiologic Factor in Autism Spectrum Disorder: Challenges and Opportunities for Research

Background: Autism spectrum disorder (ASD) is a neurodevelopmental condition with unknown etiology. Both genetic and environmental factors have been associated with ASD. Environmental exposures during the prenatal period may play an important role in ASD development. This narrative review critically examines the evidence for a relationship between maternal thyroid dysfunction during pregnancy and ASD in the child. Summary: Studies that assessed the associations of hypothyroidism, hyperthyroidism, hypothyroxinemia, thyroid hormone concentrations, or autoimmune thyroid disease with ASD outcomes were included. Most research focused on the relationship between hypothyroidism and ASD. Multiple population-based studies found that maternal hypothyroidism was associated with higher likelihood of an ASD diagnosis in offspring. Associations with other forms of maternal thyroid dysfunction were less consistent. Findings may have been affected by misclassification bias, survival bias, or publication bias. Studies using medical records may have misclassified subclinical thyroid dysfunction as euthyroidism. Two studies that assessed children at early ages may have misclassified those with ASD as typically developing. Most studies adjusted for maternal body mass index (BMI) and/or mental illness, but not interpregnancy interval or pesticide exposure, all factors associated with fetal survival and ASD. Most studies reported a combination of null and statistically significant findings, although publication bias is still possible. Conclusions: Overall, evidence supported a positive association between maternal thyroid dysfunction during pregnancy and ASD outcomes in the child, especially for hypothyroidism. Future studies could reduce misclassification bias by using laboratory measures instead of medical records to ascertain thyroid dysfunction and evaluating children for ASD at an age when it can be reliably detected. Survival bias could be further mitigated by adjusting models for more factors associated with fetal survival and ASD. Additional research is needed to comprehensively understand the roles of maternal levothyroxine treatment, iodine deficiency, or exposure to thyroid-disrupting compounds in the relationship between maternal thyroid dysfunction and child ASD outcomes.

Des-Arg(9) bradykinin as a causal metabolite for autism spectrum disorder

BACKGROUND

Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder (ASD). However, the etiology of ASD is not completely understood. The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD.

AIM

To estimate and interpret the causal relationship between ASD and metabolite profile, taking into consideration both genetic and environmental influences.

METHODS

A two-sample Mendelian randomization (MR) analysis was conducted using summarized data from large-scale genome-wide association studies (GWAS) including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls. Metabolites in plasma were set as exposures with ASD as the main outcome. The causal relationships were estimated using the inverse variant weight (IVW) algorithm. We also performed leave-one-out sensitivity tests to validate the robustness of the results. Based on the drafted metabolites, enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome, SwissTargetPrediction, STRING, and Metascape.

RESULTS

Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD (β = 0.262, SE = 0.064, PIVW = 4.64 × 10-5). The association was robust, with no significant heterogeneity among instrument variables (PMR Egger = 0.663, PIVW = 0.906) and no evidence of pleiotropy (P = 0.949). Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9) bradykinin and ASD.

CONCLUSION

Through the application of MR, this study provides practical insights into the potential causal association between plasma metabolites and ASD. These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical practice in treating ASD.

Exploring the varied manifestations of structural violence in the lives of children on the autism spectrum and their families: a qualitative longitudinal study in Kurdistan, Iran

BACKGROUND

There are many dimensions regarding autism that are closely connected to social structures, policies, and power dynamics, silently impacting the well-being of individuals within the autism spectrum. This research aims to explore these overlooked aspects using a theoretical framework called "structural violence."

METHODS

The study was conducted in Kurdistan, Iran, and a qualitative longitudinal approach was chosen. A purposive sampling method was employed to select the participants, with 11 parents taking part. The study data comprised 29 interviews using a topic guide conducted over a span of 2 years. Thematic analysis and a matrix-based approach were utilized for data analysis. To enhance the scientific rigor of this research, four criteria, including Guba and Lincoln's principles, were implemented to ensure methodological accuracy.

RESULTS

The research findings highlight four primary forms through which structural violence impacts children on the autism spectrum and their families: access to healthcare, geographic disparities, awareness and stigma, and poverty and financial burden. Additionally, the study identified 11 subthemes related to structural violence in the context of autism and families.

CONCLUSIONS

We illustrated how structural forces create barriers to accessing adequate healthcare services, exacerbate discrimination based on ethnicity and geography, perpetuate stigma, and contribute to poverty and the inability to meet basic needs. These factors not only worsen health issues but also deepen existing disparities in healthcare access and outcomes for children on the autism spectrum and families. We emphasize the urgent need for systemic changes to address these issues. It is essential to promote public awareness, provide better access to health and support services, and address economic and political factors that contribute to these inequalities.

Pre-autism: What a paediatrician should know about early diagnosis of autism‎

Autism, also known as an autism spectrum disorder, is a complex neurodevelopmental disorder usually diagnosed in the first three years of a child's life. A range of symptoms characterizes it and can be diagnosed at any age, including adolescence and adulthood. However, early diagnosis is crucial for effective management, prognosis, and care. Unfortunately, there are no established fetal, prenatal, or newborn screening programs for autism, making early detection difficult. This review aims to shed light on the early detection of autism prenatally, natally, and early in life, during a stage we call as "pre-autism" when typical symptoms are not yet apparent. Some fetal, neonatal, and infant biomarkers may predict an increased risk of autism in the coming baby. By developing a biomarker array, we can create an objective diagnostic tool to diagnose and rank the severity of autism for each patient. These biomarkers could be genetic, immunological, hormonal, metabolic, amino acids, acute phase reactants, neonatal brainstem function biophysical activity, behavioral profile, body measurements, or radiological markers. However, every biomarker has its accuracy and limitations. Several factors can make early detection of autism a real challenge. To improve early detection, we need to overcome various challenges, such as raising community awareness of early signs of autism, improving access to diagnostic tools, reducing the stigma attached to the diagnosis of autism, and addressing various culturally sensitive concepts related to the disorder.

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

BACKGROUND

Diagnostic delays in autism are common, with the time to diagnosis being up to 3 years from the onset of symptoms. Such delays have a proven detrimental effect on individuals and families going through the process. Digital health products, such as mobile apps, can help close this gap due to their scalability and ease of access. Further, mobile apps offer the opportunity to make the diagnostic process faster and more accurate by providing additional and timely information to clinicians undergoing autism assessments.

OBJECTIVE

The aim of this scoping review was to synthesize the available evidence about digital biomarker tools to aid clinicians, researchers in the autism field, and end users in making decisions as to their adoption within clinical and research settings.

METHODS

We conducted a structured literature search on databases and search engines to identify peer-reviewed studies and regulatory submissions that describe app characteristics, validation study details, and accuracy and validity metrics of commercial and research digital biomarker apps aimed at aiding the diagnosis of autism.

RESULTS

We identified 4 studies evaluating 4 products: 1 commercial and 3 research apps. The accuracy of the identified apps varied between 28% and 80.6%. Sensitivity and specificity also varied, ranging from 51.6% to 81.6% and 18.5% to 80.5%, respectively. Positive predictive value ranged from 20.3% to 76.6%, and negative predictive value fluctuated between 48.7% and 97.4%. Further, we found a lack of details around participants' demographics and, where these were reported, important imbalances in sex and ethnicity in the studies evaluating such products. Finally, evaluation methods as well as accuracy and validity metrics of available tools were not clearly reported in some cases and varied greatly across studies. Different comparators were also used, with some studies validating their tools against the Diagnostic and Statistical Manual of Mental Disorders criteria and others through self-reported measures. Further, while in most cases, 2 classes were used for algorithm validation purposes, 1 of the studies reported a third category (indeterminate). These discrepancies substantially impact the comparability and generalizability of the results, thus highlighting the need for standardized validation processes and the reporting of findings.

CONCLUSIONS

Despite their popularity, systematic evaluations and syntheses of the current state of the art of digital health products are lacking. Standardized and transparent evaluations of digital health tools in diverse populations are needed to assess their real-world usability and validity, as well as help researchers, clinicians, and end users safely adopt novel tools within clinical and research practices.

Frozen in Time, a Focused Review of Autism Prevalence in Canadian Indigenous Communities

The unprecedented global continuous rise in autism prevalence is often referred to as a Pandemic while its parallel cost increase to society portrays a Tsunami. Autism data originates mostly from industrialized High-Income geopolitical regions in Europe, North America, and Asian regions. Although efforts to determine autism data from regions in Low and Mid-economies are ongoing, prevalence information from geographically remote and economically vulnerable communities within the privileged regions is largely undetermined, as is the case of the Canadian Indigenous communities, the First Nations, Inuit, and Métis highlighted in this focused review. The underlying theoretical approach adopted here is Transcultural Psychiatry with its emphasis on Context including sociopolitical circumstances, considered the gateway to understanding health, illness, and recovery in groups and individuals. Accordingly, the review includes a concise relevant government system description and history of the relations with Indigenous peoples to provide context to present indigenous relations to Canadian government agencies. Scores in these communities face a myriad of survival challenges encompassing meagre health resources and services. Establishing autism prevalence data in these communities are exceedingly difficult due to multiple factors. While prominent among them are their strong ties to traditional approaches to health, illness, and autism conceptualization, the crucial obstacle is Crown and Provincial government authorities' and agencies' historically rooted colonial response to the needs of families with autistic members. It embodies a posture of infantilization, an attitude that is "frozen in time" in the approach, practice, accommodations, and services for these families. The review provides the preferred autism terminology, information sources, article flow, and Future Directions, all found in the Introduction's first paragraphs.

Awareness and perceptions regarding autism among school teachers in Jazan region, Kingdom of Saudi Arabia

Background

Identification of Autism Spectrum Disorder (ASD) especially at early stages is highly important to improve the quality of life and help to decrease the disease burden. This study was performed to assess the school teachers' level of knowledge and perception regarding Autism.

Materials and Methods

An observational cross-sectional study was carried out in the Jazan region among a random sample of school teachers (males and females) who are working at the governmental primary Schools, throughout the scholastic year 2019-2020. A structured validated self-administered questionnaire was used for data collection. It includes three sections: sociodemographic information of the participants, the Autism Knowledge Questionnaire (AKQ), and statements exploring the teachers' attitudes toward children with autism.

Results

The study included 403 primary school teachers. The age of 47.1% of them ranged between 35 and 44 years. Slightly more than half (50.9%) were males. Overall, the total autism knowledge score ranged between 2 and 26, out of a possible 30 with a median (interquartile range "IQR") of 15 (12-18). Teachers who ever encountered a child with autism had higher significant knowledge scores about autism compared to their counterparts (mean ranks were 263.95 and 179.32, respectively), P < 0.001. Female compared to male teachers more significantly agreed that teachers not trained in special education should not be expected to deal with a child with autism, P = 0.018. Teachers aged between 35 and 44 years are more willing to have an autistic child in their class in compare to those ages ≥55 years that they would not want children with autism in their class, P = 0.036 and more interested in attending training in the area of childhood developmental and behavioral disorders, P = 0.010. Postgraduate teachers were more agreed than Bachelor holders that teachers not trained in special education should not be expected to deal with a child with autism, P = 0.001. Diploma-holder teachers were more agreed than postgraduates that children with autism are too impaired to benefit from activities of a general school, P = 0.019 and students with Autism should be given every opportunity to function in the regular education classroom whenever possible, P = 0.028.

Conclusion

The knowledge about autism among primary school teachers is average in general, however, they expressed mostly positive attitudes towards autistic children.

Applications of identity-based theories to understand the impact of stigma and camouflaging on mental health outcomes for autistic people

Autistic people have long been conceptualized from a deficit-based model of disability, but recent self-advocates and scholars have asserted the importance of recognizing autism as both a disability and an important part of a person's social identity. The autistic identity is subject to specific stigma and stressors beyond everyday discrimination and prejudice, which can have many downstream implications on mental health and well-being. Prior research on camouflaging has explained both quantitatively and qualitatively how autistic people conform to norms and mask their autistic traits to better fit in with non-autistic societal standards. Given this paradigm shift in understanding autistic peoples' lived experiences, researchers must also begin to reshape the theories guiding their work in order to improve diagnosis, intervention, and supports. This review examines the extant research on identity-related stigma and camouflaging and their subsequent impacts on mental health outcomes in autism. A model is proposed integrating identity-based theories-specifically the social model of disability, social identity theory, and minority stress model-to explain relationships across research areas and better explain the experiences of autistic people. We discuss how identity-based theories can be applied in autism research to better understand the impacts of stigma and camouflaging on autistic peoples' lived experiences and reduce disparities in their mental health outcomes.

Early Diagnosis of Autism Spectrum Disorder: A Review and Analysis of the Risks and Benefits

Autism spectrum disorder (ASD) is a neurodevelopmental condition made up of enduring challenges in social communication and interaction and the presence of repetitive and restricted behavior patterns. Early diagnosis of autism is crucial for timely intervention and improved long-term outcomes. This review aims to explore some of its signs and symptoms, look into some diagnostic tools, and analyze the benefits and risks associated with an early diagnosis of autism. The symptoms of ASD vary from child to child, some of which are: avoidance of eye contact, lack of response to names, excessive fear, and lack of interactive and pretend play. Early identification of these symptoms by caregivers and healthcare providers facilitates the need for diagnosis and appropriate interventions. Some screening and diagnostic tools that have been found to help make the diagnosis are the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F), the Social Communication Questionnaire (SCQ), the Parents' Evaluation of Developmental Status (PEDS), and the Childhood Autism Rating Scale (CARS), amongst others. The benefits of early diagnosis include the opportunity for early intervention, which has been shown to enhance developmental outcomes and improve adaptive skills. Early identification allows for the implementation of specialized interventions tailored to the specific needs of individuals with autism, targeting social communication, language development, and behavioral challenges. Furthermore, early diagnosis enables families to access appropriate support services, educational resources, and community programs, facilitating better coping mechanisms, reducing parental stress, and increasing adult independence. However, early diagnosis of autism also entails certain risks. One significant concern is the potential for labeling and stigmatization, which can impact the child's self-esteem and social interactions. There is a risk of overdiagnosis or misdiagnosis, leading to unnecessary interventions and treatments. Additionally, the diagnostic process can be lengthy, complex, and emotionally challenging for families, requiring comprehensive assessments by multidisciplinary teams. This review highlights the importance of a balanced approach when considering the benefits and risks of early diagnosis. Early identification allows for timely interventions that significantly improve developmental outcomes and quality of life for individuals with autism. To mitigate the risks, it is crucial to ensure accurate and reliable diagnostic procedures, support families throughout the process, and promote societal awareness and acceptance. We also highlighted some future directions in the management of autism, including the use of biomarkers and the use of artificial intelligence and learning for diagnosing ASD.

Early Identification of Children at Risk of Communication Disorders: Introducing a Novel Battery of Dynamic Assessments for Infants

PURPOSE

Many children with communication disorders (CDs) experience lengthy gaps between parental reporting of concerns and formal identification by professionals. This means that children with CDs are denied access to early interventions that may help support the development of communication skills and prevent possible negative sequelae associated with long-term outcomes. This may be due, in part, to the lack of assessment instruments available for children younger than 3 years of age. This study therefore reports on promising preliminary data from a novel set of valid dynamic assessment (DA) measures designed for infants.

METHOD

We recruited 53 low-risk children and two groups of children considered to be at high risk for CDs (n = 17, social high risk, and n = 22, language high risk) due to family members with language and social communication difficulties. The children were between 1 and 2 years of age and were assessed using a battery of five DA tasks related to receptive vocabulary, motor imitation, response to joint attention, turn taking, and social requesting. A set of standardized measures were also used.

RESULTS

The DA tasks showed high levels of interrater reliability and relationships with age across a cross-sectional sample of children from the low-risk group. Three tasks showed moderate to strong correlations with standardized measures taken at the same age, with particularly strong correlations between the DA of receptive vocabulary and other receptive language measures. The DA of receptive vocabulary was also the only task to discriminate between the three risk groups, with the social high risk group scoring lower.

CONCLUSIONS

These results provide preliminary information about early DA tasks, forming the basis for further research into their utility. DA tasks might eventually facilitate the development of new methods for detecting CDs in very young children, allowing earlier intervention and support.

Machine learning determination of applied behavioral analysis treatment plan type

BACKGROUND

Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20-40 h/week of treatment. Focused ABA targets individual behaviors and typically involves 10-20 h/week of treatment. Determining the appropriate treatment intensity involves patient assessment by trained therapists, however, the final determination is highly subjective and lacks a standardized approach. In our study, we examined the ability of a machine learning (ML) prediction model to classify which treatment intensity would be most suited individually for patients with ASD who are undergoing ABA treatment.

METHODS

Retrospective data from 359 patients diagnosed with ASD were analyzed and included in the training and testing of an ML model for predicting comprehensive or focused treatment for individuals undergoing ABA treatment. Data inputs included demographics, schooling, behavior, skills, and patient goals. A gradient-boosted tree ensemble method, XGBoost, was used to develop the prediction model, which was then compared against a standard of care comparator encompassing features specified by the Behavior Analyst Certification Board treatment guidelines. Prediction model performance was assessed via area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

The prediction model achieved excellent performance for classifying patients in the comprehensive versus focused treatment groups (AUROC: 0.895; 95% CI 0.811-0.962) and outperformed the standard of care comparator (AUROC 0.767; 95% CI 0.629-0.891). The prediction model also achieved sensitivity of 0.789, specificity of 0.808, PPV of 0.6, and NPV of 0.913. Out of 71 patients whose data were employed to test the prediction model, only 14 misclassifications occurred. A majority of misclassifications (n = 10) indicated comprehensive ABA treatment for patients that had focused ABA treatment as the ground truth, therefore still providing a therapeutic benefit. The three most important features contributing to the model's predictions were bathing ability, age, and hours per week of past ABA treatment.

CONCLUSION

This research demonstrates that the ML prediction model performs well to classify appropriate ABA treatment plan intensity using readily available patient data. This may aid with standardizing the process for determining appropriate ABA treatments, which can facilitate initiation of the most appropriate treatment intensity for patients with ASD and improve resource allocation.

Invisible Disabilities and Inequality

In this address, I consider the realized and potential contributions of sociological social psychology to research on inequality based on invisible disabilities and the challenges that invisible disabilities pose to current social psychological theories. Drawing from the social structure and personality framework, I advance the general notion of invisible disability as a dimension of inequality, consider how four basic social psychological processes (social categorization, identity, status, and stigmatization) have and can help us understand how invisible disabilities shape outcomes over the life course, and suggest new lines of research social psychologists could pursue. I close with brief comments about the benefits of such an agenda for sociological social psychology as well as how these lines of research can inform theories of stratification.

Early identification of autism spectrum disorder by multi-instrument fusion: A clinically applicable machine learning approach

Autism spectrum disorder (ASD), developmental language disorder (DLD), and global developmental delay (GDD) are common neurodevelopmental disorders in early childhood; however, the differential diagnosis of these disorders is difficult because of overlapping symptoms. Drawing on a cohort of 2004 children with ASD, DLD, or GDD, this study developed machine learning classifiers using decision trees, support vector machines, eXtreme gradient boosting (XGB), logistic regression, and neural networks by combining several easily accessible behavioral and developmental assessment instruments. The best-performing XGB model was further simplified into a two-stage decision model (TS-DM) to achieve better interpretability. Model performance was tested and compared with that of 12 pediatricians on an external dataset of 60 children. The accuracies of the resident pediatricians, senior pediatricians, TS-DM, and XGB were 53.3%, 66.7%, 75.0%, and 78.3%, respectively. Machine learning has the potential to identify these three neurodevelopmental disorders by integrating information from multiple instruments and thereby may increase our understanding of the roles of different behavioral and developmental characteristics in the different diagnoses.

Impact of parental rheumatoid arthritis on risk of autism spectrum disorders in offspring: A systematic review and meta-analysis

BACKGROUND

To investigate the association of risk of offspring autism spectrum disorder (ASD) with both maternal and paternal rheumatoid arthritis (RA).

METHODS

The Embase, Medline, Cochrane Library databases were searched for studies that investigated the association of parental RA with risk of offspring ASD. The primary outcome was the associations of maternal/paternal RA with the risk of offspring ASD. Subgroup analyses were conducted based on the timing of maternal RA diagnosis (i.e., before/after childbirth) and geographical location (i.e., Western vs. Asian countries) of studies.

RESULTS

Ten studies published between 2005 and 2022 involving 6,177,650 participants were analyzed. Pooled results revealed a significant association between maternal RA and the risk of ASD (OR = 1.246, p < 0.001, 10 studies), while there was no association of paternal RA with the risk of offspring ASD (OR = 1.104, p = 0.253, four studies). Subgroup analysis demonstrated no correlation between diagnosis of maternal RA before childbirth and the risk of offspring ASD (OR = 1.449, p = 0.192, four studies), while there was a significant association of maternal RA regardless of the timing of diagnosis with the risk of offspring ASD (OR = 1.227, p = 0.001, six studies). Subgroup analysis on geographical location showed a significant association of maternal RA with the risk of offspring ASD regardless of the study location (all p < 0.05).

CONCLUSION

Our findings supported an association between maternal RA and an elevated risk of ASD in offspring. However, given the limited numbers of studies investigating the risk of offspring ASD in mothers diagnosed with RA before childbirth, further studies are warranted to elucidate this issue.

SYSTEMATIC REVIEW REGISTRATION

[www.crd.york.ac.uk/prospero/], identifier [CRD42022358470].

Early Detection of Male-Predominant Phenotypes in the Pattern of Ultrasonic Vocalizations Emitted by Autism Spectrum Disorder Model (Crmp4-Knockout) Mice

Male predominance is a known feature of autism spectrum disorder (ASD). Although ASD mouse models can be useful for elucidating mechanisms underlying abnormal behaviors relevant to human ASD, suitable models to analyze sex differences in ASD pathogenesis remain insufficient. Herein, we used collapsin response mediator protein 4 (Crmp4)-knockout (KO) mice exhibiting ASD-like phenotypes in a male-predominant manner and analyzed ultrasonic vocalizations (USVs) to detect potential differences between genotypes and sexes during the early postnatal period. We recorded isolation-induced USVs emitted from wild-type (WT) and Crmp4-KO littermates and compared the total number of USVs between genotypes and sexes. We classified USVs into 10 types based on internal pitch changes, lengths, and shapes and compared the number of USVs in each type by genotypes and sex. Male Crmp4-KO mice exhibited a reduction in the total number of USVs. Crmp4-KO decreased the number of USVs in 7 out of 10 USV types, and male KO mice exhibited a greater reduction than females in 3 of the 7 types. This study offers a suitable ASD animal model and tool for assessing sex-based communication deficits during the early postnatal period, both of which would be valuable for elucidating the underlying mechanism.

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder

Autism spectrum disorder (ASD) can be reliably diagnosed at 18 months, yet significant diagnostic delays persist in the United States. This double-blinded, multi-site, prospective, active comparator cohort study tested the accuracy of an artificial intelligence-based Software as a Medical Device designed to aid primary care healthcare providers (HCPs) in diagnosing ASD. The Device combines behavioral features from three distinct inputs (a caregiver questionnaire, analysis of two short home videos, and an HCP questionnaire) in a gradient boosted decision tree machine learning algorithm to produce either an ASD positive, ASD negative, or indeterminate output. This study compared Device outputs to diagnostic agreement by two or more independent specialists in a cohort of 18-72-month-olds with developmental delay concerns (425 study completers, 36% female, 29% ASD prevalence). Device output PPV for all study completers was 80.8% (95% confidence intervals (CI), 70.3%-88.8%) and NPV was 98.3% (90.6%-100%). For the 31.8% of participants who received a determinate output (ASD positive or negative) Device sensitivity was 98.4% (91.6%-100%) and specificity was 78.9% (67.6%-87.7%). The Device's indeterminate output acts as a risk control measure when inputs are insufficiently granular to make a determinate recommendation with confidence. If this risk control measure were removed, the sensitivity for all study completers would fall to 51.6% (63/122) (95% CI 42.4%, 60.8%), and specificity would fall to 18.5% (56/303) (95% CI 14.3%, 23.3%). Among participants for whom the Device abstained from providing a result, specialists identified that 91% had one or more complex neurodevelopmental disorders. No significant differences in Device performance were found across participants' sex, race/ethnicity, income, or education level. For nearly a third of this primary care sample, the Device enabled timely diagnostic evaluation with a high degree of accuracy. The Device shows promise to significantly increase the number of children able to be diagnosed with ASD in a primary care setting, potentially facilitating earlier intervention and more efficient use of specialist resources.

Pathogenic Infections during Pregnancy and the Consequences for Fetal Brain Development

Pathogens comprised of viruses, bacteria, gut microbiome, and parasites are a leading cause of ever-emerging diseases in humans. Studying pathogens for their ability to cause diseases is a topic of critical discussion among scientists and pharmaceutical centers for effective drug development that diagnose, treat, and prevent infection-associated disorders. Pathogens impact health either directly by invading the host or by eliciting an acute inflammatory immune response. This paradigm of inflammatory immune responses is even more consequential in people who may be immunocompromised. In this regard, pregnancy offers an altered immunity scenario, which may allow the onset of severe diseases. Viruses, such as Influenza, HIV, and now SARS-CoV-2, associated with the COVID-19 pandemic, raise new concerns for maternal and fetal/neonatal health. Intrauterine bacterial and parasitic infections are also known to impact pregnancy outcomes and neonatal health. More importantly, viral and bacterial infections during pregnancy have been identified as a common contributor to fetal brain development defects. Infection-mediated inflammatory uterine immune milieu is thought to be the main trigger for causing poor fetal brain development, resulting in long-term cognitive impairments. The concept of in utero programming of childhood and adult disorders has revolutionized the field of neurodevelopment and its associated complications. Recent findings in mice and humans clearly support the idea that uterine immunity during pregnancy controls the health trajectory of the child and considerably influences the cognitive function and mental health. In this review, we focus on the in utero programming of autism spectrum disorders (ASD) and assess the effects of pathogens on the onset of ASD-like symptoms.

Detecting Time Concept Competence in Children with Autism Spectrum and Attention Disorders

The importance of time concept in human existence is "ancient history" celebrated in the biblical book Ecclesiastes. Indeed, our time-sensitive mechanisms are literally carved into our biology and neurology on a molecular level, gifting us with neural clocks. However, time in human consciousness is not the time indicated by physical clocks: time is a subjective reality in our psychological makeup due to the nature of the temporal neural mechanisms and unique properties of physical time. Nonetheless, subjective time requires anchoring to physical time which permeates our language, endeavors, and entire existence, a process hinging on time-related skills such as estimates and measures of passage and duration of time. Moreover, accurate time reading, a critical adaptive life-skill, is imperative for effective function in all societal activities. Because it embodies the complexity of the time construct, it is central to instruction of time concept in primary education. It is often measured in children by clock drawings, a cognitive integrative skill with errors pointing to neuroanatomical differences impacting the integrity of executive function. Time competence in children with atypical neurobiological development and high prevalence, as in autism spectrum disorders (ASD), and attention disorders (ADHD), is often compromised, calling for investigation of its function. This thematic review article aims to: 1) discuss the complexity of time concept and its underlying bio-neurological mechanisms, 2) elucidate difficulties children with ASD and those with ADHD exhibit in temporal development, and 3) demonstrate the use of a set of clinical tools in uncovering temporal competence and ecological executive function in two children with ASD, and a child with ADHD, using a clock drawing task and error analyses; children's time knowledge questionnaire; a behavior rating parent questionnaire examining ecological executive function, and parent open-ended questions related to their children's time difficulties. A discussion, directions, and a take-home message round out the article.

Future Prospects for Epigenetics in Autism Spectrum Disorder

Despite decades of investigation into the genetics of autism spectrum disorder (ASD), a current consensus in the field persists that ASD risk is too heterogeneous to be diagnosed by a single set of genetic variants. As such, ASD research has broadened to include assessment of other molecular biomarkers implicated in the condition that may be reflective of environmental exposures or gene by environment interactions. Epigenetic variance, and specifically differential DNA methylation, have emerged as areas of particularly high interest to ASD, as the epigenetic markers from specific chromatin loci collectively can reflect influences of multiple genetic and environmental factors and can also result in differential gene expression patterns. This review examines recent studies of the ASD epigenome, detailing common gene pathways found to be differentially methylated in people with ASD, and considers how these discoveries may inform our understanding of ASD etiology. We also consider future applications of epigenetics in ASD research and clinical practice, focusing on substratification, biomarker development, and experimental preclinical models of ASD that test causality. In combination with other -omics approaches, epigenomics allows an improved conceptualization of the multifactorial nature of ASD, and opens future lines of inquiry for both basic research and clinical practice.

Recent Advances of Telepractice for Autism Spectrum Disorders in Speech and Language Pathology

Telepractice or teletherapy is defined as the means of service delivery via technology-based platforms that allow long-distance interventions across populations, age spans, and geographical locations. Specifically, telepractice capitalizes on communication technologies such as online modules, videoconferencing, and computerized software programs, in servicing populations with communication challenges. Notably, telepractice has emerged as a potentially effective, low-cost, and promising means of conducting and completing online assessment, diagnosis, and intervention sessions for individuals with autism spectrum disorders (ASD). Moreover, telepractice allows the inclusion of parents, caregivers, and educators as potential facilitators in supporting treatment delivery in populations with ASD. This thematic review article aims to highlight and acquaint practitioners and other stakeholders with relatively recent information regarding the advantages and disadvantages of the telepractice service delivery model in ASD. Furthermore, the emergence of the COVID-19 pandemic has impacted service delivery in the field of Health Sciences including speech and language pathology (SLP). Consequently, the need to avoid face-to-face therapy sessions, recommended to SLPs as to all health practitioners, resulted in the call for adaptation and adjustment of service telepractice. This paper attempts to answer this call by suggesting supplementing traditional diagnostic and therapy resources with training modules consisting of online materials and use of animated and specially designed programs in SLP intervention with individuals with ASD. Lastly, the paper includes a discussion of research findings in the field, a conclusion, and a take-home message.

Language and Speech Characteristics in Autism

The aim of this paper is to provide information regarding diversity in speech and language profiles of individuals with Autism Spectrum Disorders (ASD) and try to classify these profiles according to the combination of the communication difficulties. Research findings confirm the existence of heterogeneity of communication challenges in ASD across the lifespan. A lot of children with ASD experience communication challenges and strengths across all language sub-systems including pragmatics, grammar, semantics, syntax, phonology, and morphology in both oral and written language, while some children with autism demonstrate exceptional language abilities incl. linguistic creativity. Communication issues vary on a continuum of severity so that some children may be verbal, whereas others remain non-verbal or minimally-verbal. The diversity of profiles in speech and language development stem from either the presence of comorbid factors, as a core symptom of autistic behavior without comorbidity or both, with the development of complex clinical symptoms. Difficulties with the semantic aspect of language affect the individual's skills in abstract thinking, multiple meanings of words, concept categorization, and so on. Finally, the coexistence of ASD with other communication difficulties such as a Language Disorder, Apraxia of Speech, Speech Sound Disorders or/and other neurodevelopmental disorders raises the need for examining more carefully the emergence of new clinical profiles and clinical markers useful in performing differential diagnosis and different intervention.

Effects of Washed Fecal Bacteria Transplantation in Sleep Quality, Stool Features and Autism Symptomatology: A Chinese Preliminary Observational Study

PURPOSE

Autism spectrum disorder is a highly complex neurological and psychosocial disorder characterized by social dysfunction, severe reduction in speech, and a single stereotyped behavior. The treatment methods are currently limited, and children with autism generally suffer from constipation and sleep disorders. It is urgent to find an alternative psychotropic drug, given the drug dependence and adverse reactions that may occur with long-term medication.

PATIENTS AND METHODS

This retrospective study included 49 children with autism at the first affiliated Hospital of Guangdong Pharmaceutical University, who received washed fecal microbiota transplantation (WMT) treatment between June 2019 and July 2021 and compared the sleep disorder scores between the constipation group, control group and blank group.

RESULTS

Second WMT could significantly improve the sleep disorder scores in the constipation group (p=0.026) and the decrease in sleep disturbance scale for children (SDSC) score was synchronized with the increase in Bristol stool form scale (BSFS) score. However, there was no significant difference between patients without constipation (p=0.54), and the behavior of autism improved in both groups.

CONCLUSION

WMT could relieve constipation and improve sleep disorders in children with autism, with no deterioration in stool morphology and sleep disorders in other children. Moreover, there were no obvious serious adverse clinical events after WMT.