Clinical Diagnosis Of Autism Spectrum Disorder Research Articles

A - 60 Validity of Caregiver Reported Behavioral Symptoms in Autism Spectrum Disorder Evaluations

Abstract Objective Informant report of a child’s behavior is an important component of Autism Spectrum Disorder (ASD) evaluations. Informant-report measures with symptom validity scales are crucial to identifying invalid reporting of symptoms. This study examined the prevalence of, and characteristics associated with caregiver-report symptom validity (SVT) failure in an ASD evaluation clinic. Method Particpants were 212 youth, 6 to 18 years old (Mage = 11.5, 79.7% white, 91% non-Hispanic) whose caregivers completed a Conners, 3rd Edition, Adaptive Behavior Assessment System (ABAS) 2nd or 3rd Edition, and Child Behavior Checklist (CBCL) as part of a multidisciplinary autism evaluation. Results Rates of symptom validity failure, specifically negative impression (z = −3.338, p < 0.001) and inconsistency (z = −2.051, p = 0.04) were significantly higher in this sample compared to the Conners, 3rd edition general population normative sample. Elevated negative impression index was associated with lower scores on the ABAS Social index (r = 0.152, p = 0.028) and higher scores on the CBCL Withdrawn scale (r = −0.285, p < 0.001). Those with an elevated negative impression index were more likely to not receive an ASD diagnosis [Χ2 = 8.116, p = 0.006]. Conclusion Caregiver report in children seeking diagnostic clarification for ASD is more likely to raise concerns for validity of reported behavioral symptoms than in the general population. Negative impression management is associated with lower likelihood of clinical ASD diagnosis, perhaps suggestive of caregiver motivation to obtain ASD diagnosis for non-autistic children. These findings support consideration of SVT failure when examining caregiver-reported behavioral symptoms.

Types and Predictors of Service use Among Young Children Recommended to Receive Intensive Services After Initial Autism Spectrum Disorder Diagnosis.

Intensive services are recommended for young children with autism spectrum disorder (ASD). Limited research on service receipt in states with mandated ASD-specific service coverage suggests that it remains low, and factors associated with intensity are unclear. Participants were 206 children from the Boston Outcomes of Autism in Toddlers study living in states with autism mandates, diagnosed with ASD through a multidisciplinary consultation at 12-36 months, and recommended to receive at least 20h of services weekly. Outcome measures were caregiver-reported receipt of total and ASD-specific services within 18 months of ASD diagnosis. Separate negative binomial regression models were run for each outcome, with covariates of child adaptive and cognitive functioning, age, gender, household income, primary insurance, and maternal educational level. The sample was 83% male with a mean age of 24.5 months at ASD diagnosis. Mean Vineland adaptive behavior composite and Bayley cognitive standard scores were 73 and 81, respectively. 90% of children received ASD-specific services. The median intensities for total and ASD-specific services were 22 and 17h weekly respectively, with 44% of the participants receiving at least 20h of ASD-specific services weekly. Adjusted regression models found significant associations between lower adaptive scores and increased total and ASD-specific service receipt. Children in insurance-mandated states received a high intensity of intervention after clinical ASD diagnosis. Lower child adaptive functioning was associated with increased service receipt, while socioeconomic factors were not associated. Additional research in other regions and mandate-ineligible populations is needed.

Social Functioning in Children and Adolescents with ADHD and Autism Spectrum Disorder: A Cross-Disorder Comparison

ABSTRACT Objective Social functioning can be defined according to three main components: social perception, social performance, and social knowledge. Although they are important in daily life relationships and in children’s adaptation, these components have never been tested together in children and adolescents with Attention Deficit/Hyperactivity Disorder (ADHD) or with Autism Spectrum Disorder (ASD) using lab-based tasks. The present study used a cross-disorder approach to compare the performance of children with ADHD and ASD and non-diagnosed (ND) peers utilizing a task that involves these three fundamental social functioning components. Methods Two hundred and twenty-five Italian children (86% boys) aged between 8 and 16 (66 with a clinical diagnosis of ADHD; 51 with a clinical diagnosis of ASD, level 1; 108 ND children) were enrolled. The three groups were matched for age, gender, and IQ. Social functioning was assessed using a lab-based task, including videos of problematic interactions among peers, created ad hoc for the study, and a semi-structured interview based on the Social Information Processing model. Results Data were analyzed using one-way ANOVAs and multinomial mixed effects models. Our findings suggested that both groups with ADHD and ASD presented social functioning difficulties in comparison to ND children. However, a different pattern of performance emerged. Children with ADHD showed higher difficulties in social performance than those with ASD, whereas autistic children revealed more difficulties in social perception and in some aspects of social knowledge. Conclusions Our findings have important clinical implications for assessment, intervention, and differential diagnosis, and should encourage clinicians to investigate different aspects of social functioning and identify specific strengths and weaknesses in each social profile.

Evaluating screening for autism spectrum disorder using cluster randomization

We evaluated the rate of autism spectrum disorder (ASD) in a group invited to a screening program compared to the rates in two groups who received usual care. The population eligible for screening was all children in Iceland registered for their 30-month well-child visits at primary healthcare centers (PHCs) from March 1, 2016, to October 31, 2017 (N = 7173). The PHCs in the capital area of Reykjavik were the units of cluster randomization. Nine PHCs were selected for intervention (invited group), while eight PHCs received usual care (control group 1). PHCs outside the capital area were without randomization (control group 2). An interdisciplinary team, including a pediatrician contributing with physical and neurological examination, a psychologist evaluating autism symptoms using a diagnostic instrument, and a social worker interviewing the parents, reached a consensus on the clinical diagnosis of ASD according to the ICD-10 diagnostic system. Children in the population were followed up for at least two years and 119 cases were identified. The overall cumulative incidence of ASD was 1.66 (95% confidence interval (CI): 1.37, 1.99). In the invited group the incidence rate was 2.13 (95% CI: 1.60, 2.78); in control group 1, the rate was 1.83 (95% CI: 1.31, 2.50); and in control group 2, the rate was 1.02 (95% CI: 0.66, 1.50). Although the rate of ASD was higher in the invited group than in the control groups, the wide confidence intervals prevented us from concluding definitively that the screening detected ASD more readily than usual care.

The Early Social Cognition Inventory - Autism Version: psychometric properties for young children with autism spectrum disorder

In this study, we examined the psychometric properties of the Early Social Cognition Inventory (ESCI) in children with autism spectrum disorder (ASD). The study included 76 typically developing children (TDC) (mean age= 39.28 months) and 81 participants (mean age= 36.07 months) with clinical diagnoses of ASD. The parents completed both the ESCI and Modified checklist for Autism in Toddlers (M-CHAT) checklists. Our exploratory factor analysis of the ESCI in children with autism revealed two factors (joint attention and understanding beliefs and emotions) with 17 items. We named this 17-item scale the Early Social Cognition Inventory - Autism Version (ESCI-AV). Regarding convergent validity, we found a strong correlation between the ESCI-AV and the M-CHAT (rho = 0.786, p < .001. The discriminant validity of the ESCI-AV was determined by comparing ESCI-AV scores of children with ASD and children with TDC using an independent t-test. The results revealed that TDC scored significantly higher than children with ASD on the ESCI-AV (t = −11.61, df= 155, p < 0.001), thus indicating good discriminant validity of the ESCI-AV. In addition, reliability was assessed using Kuder-Richardson 20 (KR20) and Guttman spilt-half coefficients. Both KR20 (0.85) and Guttman’s split-half (0.88) were high, indicating good reliability and internal consistency of the ESCI-AV. This study indicates that the ESCI-AV measures social cognition in children with ASD under the age of 5 years in a reliable and valid way.

Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and assessment scales, lacking objective diagnostic indicators. In this study, we introduced a novel approach for diagnosing ASD, leveraging T1-based gray matter and ASL-based cerebral blood flow network metrics. Thirty preschool-aged patients with ASD and twenty-two typically developing (TD) individuals were enrolled. Brain network features, including gray matter and cerebral blood flow metrics, were extracted from both T1-weighted magnetic resonance imaging (MRI) and ASL images. Feature selection was performed using statistical t-tests and Minimum Redundancy Maximum Relevance (mRMR). A machine learning model based on random vector functional link network was constructed for diagnosis. The proposed approach demonstrated a classification accuracy of 84.91% in distinguishing ASD from TD. Key discriminating network features were identified in the inferior frontal gyrus and superior occipital gyrus, regions critical for social and executive functions in ASD patients. Our study presents an objective and effective approach to the clinical diagnosis of ASD, overcoming the limitations of subjective behavioral observations. The identified brain network features provide insights into the neurobiological mechanisms underlying ASD, potentially leading to more targeted interventions.

A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD.

A hybrid graph network model for ASD diagnosis based on resting-state EEG signals

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder and early diagnosis is crucial for effective treatment. Stable and effective biomarkers are essential for understanding the underlying causes of the disorder and improving diagnostic accuracy. Electroencephalography (EEG) signals have proven to be reliable biomarkers for diagnosing ASD. Extracting stable connectivity patterns from EEG signals helps ensure robustness in ASD diagnostic systems. In this study, we propose a hybrid graph convolutional network framework called Rest-HGCN, which utilizes resting-state EEG signals to capture differential patterns of brain connectivity between normal children and ASD patients using graph learning strategies. The Rest-HGCN combines brain network analysis techniques and data-driven strategies to extract discriminative graph features from resting-state EEG signals. By automatically extracting differential graph patterns from these signals, the Rest-HGCN achieves reliable ASD diagnosis. To evaluate the performance of Rest-HGCN, we conducted ASD diagnosis experiments using k-fold cross-validation on the public ABC-CT resting EEG dataset. The proposed Rest-HGCN model achieved accuracies of 87.12 % and 85.32 % in single-subject and cross-experiment analyses, respectively. The results suggest that Rest-HGCN can effectively capture discriminant graph patterns from resting EEG signals and achieve robust ASD diagnosis. This may provide an effective and convenient tool for clinical ASD diagnosis.

Persistence of Autism Spectrum Disorder From Early Childhood Through School Age

While the prevalence of autism spectrum disorder (ASD) continues to increase and early diagnosis is emphasized, there is limited information on outcomes for children diagnosed with ASD in early childhood using contemporary diagnostic criteria. To determine the frequency with which children who are clinically diagnosed with ASD at 12 to 36 months of age continue to meet diagnostic criteria for ASD at 5 to 7 years of age and to evaluate whether baseline child-specific and demographic characteristics and receipt of interventions are associated with ASD persistence. In this natural history cohort study, children who received a clinical ASD diagnosis at 12 to 36 months of age underwent a research diagnostic assessment at 5 to 7 years of age. Research assessments occurred from August 14, 2018, to January 8, 2022. Children received community-based interventions, and parents provided details about interventions received. The main outcome was persistence of ASD diagnosis based on current functioning. An experienced research psychologist assigned an ASD diagnosis (present or absent) according to criteria from the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) after the research assessment. The research assessment included administration of the Autism Diagnostic Observation Schedule-2, Autism Diagnostic Interview-Research, and a cognitive measure. Of the 213 participants diagnosed with ASD at initial clinical assessment (mean [SD] age, 24.6 [3.9] months; 177 boys [83.1%]), 79 (37.1%) did not continue to meet diagnostic criteria for ASD (nonpersistent ASD) at research assessment (mean [SD] age, 74.3 [7.1] months). All children with nonpersistent ASD had IQ of at least 70, while there was a bimodal distribution of IQ for those with persistent ASD (46 with IQ <70 and 88 with IQ ≥70). All children received some interventions, and 201 (94.4%) received ASD-specific intervention, mostly applied behavioral analysis. In a multilevel logistic regression model, the only variables associated with increased odds of being in the nonpersistent ASD group at 6 years of age were higher baseline adaptive skills (b coefficient = -0.287 [SE, 0.108]) and female sex (b = 0.239 [SE, 0.064]). The findings of this cohort study suggest that among toddlers diagnosed with ASD, baseline adaptive function and sex may be associated with persistence of ASD.

Measuring intelligence in Autism and ADHD: Measurement invariance of the-Binet 5th edition and impact of subtest scatter on abbreviated IQ accuracy.

Scatter and heterogeneity in cognitive profiles is thought to be common in autism spectrum disorder (ASD), which may indicate differences in the construct of IQ. However, less research has investigated IQ scatter in attention-deficit/hyperactivity disorder (ADHD). Scatter is also thought to negatively impact the predictive validity of IQ summary scores, although there is research refuting this notion. Abbreviated IQ tests, such as the Stanford-Binet fifth edition (SB-5) abbreviated battery IQ (ABIQ), may be especially susceptible to the influence of scatter. We tested the measurement invariance of the SB-5 as well as the predictive validity of the ABIQ in predicting FSIQ in 1679 youth (21% female) ages 2-16 years with a clinical diagnosis of ASD or ADHD. Results indicated the SB-5 is measuring IQ the same way in ASD and ADHD. There were no differences between diagnostic groups in scatter between ABIQ (i.e., routing) subtests. Additionally, scatter was not related to dimensional autistic traits. Higher degree of scatter was associated with poorer predictive validity of the ABIQ and a higher likelihood of overestimating FSIQ, regardless of diagnosis. Overall, we found more similarities than differences between the ASD and ADHD groups. Our results show that the SB-5 ABIQ is generally a strong predictor of FSIQ in youth with neurodevelopmental disorders. However, the use of the SB-5 ABIQ in research and clinical applications, without consideration of scatter on routing subtests, is potentially problematic.

Likelihood of identifying autistic traits with the autism spectrum quotient (AQ) in male juveniles with autism spectrum disorder (ASD) and severe behavioral problems (SBPs)

BackgroundWhen screening for autism spectrum disorders (ASD), the Autism Spectrum Quotient (AQ) is generally considered to be useful. Whether the AQ is also a suitable screener for ASD in juveniles with severe behavioral problems (SBPs) is unknown. Due to the overlap of symptoms between ASD and SBPs, particularly in juveniles low on empathy, the screening capacity of the AQ might be constrained. The aim of the present study was to investigate whether (comorbid) SBPs affect the screening capacity of the AQ. The hypothesis is that male juveniles with SBPs - but without a diagnosis of ASD - will score higher than male juveniles without both SBPs and ASD.MethodThe AQ was completed by 216 male juveniles aged 15–18 years treated at an outpatient department of child and adolescent psychiatry. The 216 participants were categorized into four groups according to a clinical diagnosis of ASD and SBPs (defined as disruptive behavior disorder and/or delinquent behavior). Using multinomial logistic regression, we investigated whether the four identified groups, based on a diagnosis of ASD and SBPs, scored differently for the total score and subscales of the AQ.ResultsParticipants in the group with ASD (ASD+) but without SBPs (SBP-) were more likely to report higher levels of autistic traits than the reference group without both ASD and SBPs (ASD-SBP-), except for the subscale on attention to detail (ASD+SBP- OR = 1.04; 95%CI = 0.98–1.11). Participants in the group with both ASD and SBPs were more likely to report higher levels for the total AQ score (ASD+SBP+ OR = 1.03; 95%CI = 1.00–1.05) and the communication subscale of the AQ (ASD+SBP+ OR = 1.18; 95%CI = 1.07–1.31) than the reference group without both ASD and SBPs.ConclusionIn outpatient male juveniles, SBPs do not affect the screening capacity of the AQ for autistic traits. In spite of the well-known overlap of symptoms between ASD and SBPs, male juveniles with SBPs but without a diagnosis of ASD do not score higher on the AQ than male juveniles without SBPs and without a diagnosis of ASD.

Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Correlations Between the Development of Social Anxiety and Individuals With Autism Spectrum Disorder: A Systematic Review.

It is well established that people with autism spectrum disorder (ASD) have significantly higher rates of social anxiety, given that most autistic individuals experience socio-communication impairments, a deficit in social competence, and their experience in social engagement situations often leads to discomfort in social settings. Literature also finds that individuals on the spectrum are often at a higher risk of developing social anxiety, which is often misinterpreted as social anxiety disorder (SAD) leading to delays in the clinical diagnosis of ASD. Hence, an improved understanding of specific factors that put ASD individuals at risk of developing social anxiety will aid research to differentiate between social anxiety among individuals with ASD compared to non-ASD individuals facing social anxiety in general. This systematic review study focuses on empirical literature that provides evidence for reasons contributing to social anxiety among individuals with ASD. Following the systematic review methodology, the study evaluates 10 research papers. The results revealed several correlations that can be useful in helping explain why individuals with ASD are at a higher risk of developing SAD. Individuals with ASD often suffer severe social anxiety because they struggle to understand social cues, maintain eye contact, interpret non-verbal cues like facial expressions or body language, or participate in reciprocal conversation. Other cognitive factors include a preference toward predictable situations, intolerance for uncertainty, and a tendency toward rigid thinking patterns. Unpredictability in social settings often heightens anxiety levels in ASD individuals, making them avoid such situations. Other risk factors include emotional recognition impairments and reduced social competence. These findings serve as a guide to developing better intervention strategies to help individuals with ASD to overcome social anxiety.

Association of Labor Epidural Analgesia, Oxytocin Exposure, and Risk of Autism Spectrum Disorders in Children

Maternal labor epidural analgesia (LEA) and oxytocin use for labor and delivery have been reported to be associated with child autism spectrum disorders (ASD). However, it remains unclear whether these 2 common medications used during labor and delivery have synergistic associations with ASD risk in children. To assess the independent associations of LEA and oxytocin during labor and delivery with ASD, as well as outcome modification associated with the concurrent use of both interventions. Data for this cohort study included 205 994 singleton births with vaginal deliveries in a single integrated health care system in Southern California from calendar years 2008 to 2017. Children were followed up to December 31, 2021. Data on use of LEA and oxytocin, covariates, and ASD outcome in children were obtained from electronic medical records. Cox proportional hazards regression was used to estimate the hazard ratios (HRs) adjusting for covariates. Labor epidural analgesia and/or oxytocin use during labor and delivery. A child's clinical diagnosis of ASD during follow-up and at age of diagnosis. Among the cohort, 153 880 children (74.7%) were exposed to maternal LEA and 117 808 children (57.2%) were exposed to oxytocin during labor and delivery. The population of children was approximately half boys and half girls. The median (IQR) age of the mothers was 30.8 (26.8-34.5) years for those not exposed to LEA, 30.0 (25.9-33.8) years for those exposed to LEA, 30.4 (26.5-34.1) years for those unexposed to oxytocin, and 30.0 (25.9-33.9) years for those exposed to oxytocin during labor and delivery. A total of 5146 children (2.5%) had ASD diagnosed during follow-up. Oxytocin exposure was higher among LEA-exposed (67.7%) than -unexposed (26.1%) children. The ASD risk associated with LEA was independent of oxytocin exposure (HR, 1.28; 95% CI, 1.18-1.38); however, the ASD risk associated with oxytocin was not significant after adjusting for LEA exposure (HR, 1.05; 95% CI, 0.99-1.12). A significant interaction of LEA and oxytocin on child ASD risk was found (P = .02 for interaction). Compared with no exposure, HRs were 1.20 (95% CI, 1.09-1.32) for LEA alone, 1.30 (95% CI, 1.20-1.42) for both LEA and oxytocin, and 0.90 (95% CI, 0.78-1.04) for oxytocin alone. The findings of this cohort study suggest an association between maternal LEA and ASD risk in children, and the risk appeared to be further increased if oxytocin was also administered. Oxytocin exposure without LEA exposure was not associated with ASD risk in children. These findings must be interpreted with caution. Further studies are needed to replicate or refute the study results and examine biological plausibility.

Maturation of nucleus accumbens synaptic transmission signals a critical period for the rescue of social deficits in a mouse model of autism spectrum disorder

Social behavior emerges early in development, a time marked by the onset of neurodevelopmental disorders featuring social deficits, including autism spectrum disorder (ASD). Although social deficits are at the core of the clinical diagnosis of ASD, very little is known about their neural correlates at the time of clinical onset. The nucleus accumbens (NAc), a brain region extensively implicated in social behavior, undergoes synaptic, cellular and molecular alterations in early life, and is particularly affected in ASD mouse models. To explore a link between the maturation of the NAc and neurodevelopmental deficits in social behavior, we compared spontaneous synaptic transmission in NAc shell medium spiny neurons (MSNs) between the highly social C57BL/6J and the idiopathic ASD mouse model BTBR T+Itpr3tf/J at postnatal day (P) 4, P6, P8, P12, P15, P21 and P30. BTBR NAc MSNs display increased spontaneous excitatory transmission during the first postnatal week, and increased inhibition across the first, second and fourth postnatal weeks, suggesting accelerated maturation of excitatory and inhibitory synaptic inputs compared to C57BL/6J mice. BTBR mice also show increased optically evoked medial prefrontal cortex-NAc paired pulse ratios at P15 and P30. These early changes in synaptic transmission are consistent with a potential critical period, which could maximize the efficacy of rescue interventions. To test this, we treated BTBR mice in either early life (P4-P8) or adulthood (P60-P64) with the mTORC1 antagonist rapamycin, a well-established intervention for ASD-like behavior. Rapamycin treatment rescued social interaction deficits in BTBR mice when injected in infancy, but did not affect social interaction in adulthood.

Estimated Therapy Costs and Downstream Cost Consequences of iBASIS–Video Interaction to Promote Positive Parenting Intervention vs Usual Care Among Children Displaying Early Behavioral Signs of Autism in Australia

The growing global prevalence of autism spectrum disorder (ASD) is associated with increasing costs for support services. Ascertaining the effects of a successful preemptive intervention for infants showing early behavioral signs of autism on human services budgets is highly policy relevant. To estimate the net cost impact of the iBASIS-Video Interaction to Promote Positive Parenting (iBASIS-VIPP) intervention on the Australian government. Infants (aged 12 months) showing early behavioral indicators of autism were recruited through community settings into the multicenter Australian iBASIS-VIPP randomized clinical trial (RCT), a 5- to 6-month preemptive parent-mediated intervention, between June 9, 2016, and March 30, 2018, and were followed up for 18 months to age 3 years. This economic evaluation, including cost analysis (intervention and cost consequences) and cost-effectiveness analyses of iBASIS-VIPP compared with usual care (treatment as usual [TAU]), modeled outcomes observed at age 3 through to 12 years (13th birthday) and was conducted from April 1, 2021, to January 30, 2023. Data analysis was conducted from July 1, 2021, to January 29, 2023. iBASIS-VIPP intervention. To project the diagnostic trajectory and associated disability support costs drawing on the Australian National Disability Insurance Scheme (NDIS), the main outcome was the differential treatment cost of iBASIS-VIPP plus TAU vs TAU and disability-related government costs modeled to age 12 years, using a clinical diagnosis of ASD and developmental delay (with autism traits) at 3 years. Costs were calculated in Australian dollars and converted to US dollars. Economic performance was measured through the following: (1) differential net present value (NPV) cost (iBASIS-VIPP less TAU), (2) investment return (dollars saved for each dollar invested, taking a third-party payer perspective), (3) break-even age when treatment cost was offset by downstream cost savings, and (4) cost-effectiveness in terms of the differential treatment cost per differential ASD diagnosis at age 3 years. Alternate values of key parameters were modeled in 1-way and probabilistic sensitivity analysis, the latter identifying the likelihood of an NPV cost savings. Of the 103 infants enrolled in the iBASIS-VIPP RCT, 70 (68.0%) were boys. Follow-up data at age 3 years were available for 89 children who received TAU (44 [49.4%]) or iBASIS-VIPP (45 [50.6%]) and were included in this analysis. The estimated mean differential treatment cost was A $5131 (US $3607) per child for iBASIS-VIPP less TAU. The best estimate of NPV cost savings was A $10 695 (US $7519) per child (discounted at 3% per annum). For each dollar invested in treatment, a savings of A $3.08 (US $3.08) was estimated; the break-even cost occurred at age 5.3 years (approximately 4 years after intervention delivery). The mean differential treatment cost per lower incident case of ASD was A $37 181 (US $26 138). We estimated that there was an 88.9% chance that iBASIS-VIPP would deliver a cost savings for the NDIS, the dominant third-party payer. The results of this study suggest that iBASIS-VIPP represents a likely good-value societal investment for supporting neurodivergent children. The estimated net cost savings were considered conservative, as they covered only third-party payer costs incurred by the NDIS and outcomes were modeled to just age 12 years. These findings further suggest that preemptive interventions may be a feasible, effective, and efficient new clinical pathway for ASD, reducing disability and the costs of support services. Long-term follow-up of children receiving preemptive intervention is needed to confirm the modeled results.

Gut Microbiota Analysis and In Silico Biomarker Detection of Children with Autism Spectrum Disorder across Cohorts.

The study of human gut microbiota has attracted increasing interest in the fields of life science and healthcare. However, the complicated and interconnected associations between gut microbiota and human diseases are still difficult to determine in a predictive fashion. Artificial intelligence such as machine learning (ML) and deep learning can assist in processing and interpreting biological datasets. In this study, we aggregated data from different studies based on the species composition and relative abundance of gut microbiota in children with autism spectrum disorder (ASD) and typically developed (TD) individuals and analyzed the commonalities and differences of ASD-associated microbiota across cohorts. We established a predictive model using an ML algorithm to explore the diagnostic value of the gut microbiome for the children with ASD and identify potential biomarkers for ASD diagnosis. The results indicated that the Shenzhen cohort achieved a higher area under the receiver operating characteristic curve (AUROC) value of 0.984 with 97% accuracy, while the Moscow cohort achieved an AUROC value of 0.81 with 67% accuracy. For the combination of the two cohorts, the average prediction results had an AUROC of 0.86 and 80% accuracy. The results of our cross-cohort analysis suggested that a variety of influencing factors, such as population characteristics, geographical region, and dietary habits, should be taken into consideration in microbial transplantation or dietary therapy. Collectively, our prediction strategy based on gut microbiota can serve as an enhanced strategy for the clinical diagnosis of ASD and assist in providing a more complete method to assess the risk of the disorder.

Higher scores on autonomic symptom scales in pediatric patients with neurodevelopmental disorders of known genetic etiology.

Features of underlying autonomic dysfunction, including sleep disturbances, gastrointestinal problems, and atypical heart rate, have been reported in neurodevelopmental conditions, including autism spectrum disorder (ASD). The current cross-sectional, between-groups study aimed to quantify symptoms of autonomic dysfunction in a neurodevelopmental pediatric cohort characterized by clinical diagnoses as well as genetic etiology. The Pediatric Autonomic Symptom Scales (PASS) questionnaire was used to assess autonomic features across a group of patients with clinical neurodevelopmental diagnoses (NPD; N=90) and genetic etiologies. Patients were subdivided based on either having a clinical ASD diagnosis (NPD-ASD; n=37) or other non-ASD neurodevelopmental diagnoses, such as intellectual disability without ASD, speech and language disorders, and/or attention deficit hyperactivity disorder (NPD-OTHER; n=53). Analyses focused on characterizing differences between the NPD group compared to previously published reference samples, as well as differences between the two NPD subgroups (NPD-ASD and NPD-OTHER). Our results indicate higher PASS scores in our NPD cohort relative to children with and without ASD from a previously published cohort. However, we did not identify significant group differences between our NPD-ASD and NPD-OTHER subgroups. Furthermore, we find a significant relationship between quantitative ASD traits and symptoms of autonomic function. This work demonstrates the utility of capturing quantitative estimates of autonomic trait dimensions that may be significantly linked with psychosocial impairments and other core clinical features of ASD.

From eligibility assessment to intervention for students with autism spectrum disorder

AbstractThe mean age of identification for autism spectrum disorder (ASD) occurs during the months before children enter kindergarten. The incidence and prevalence of “autistic behaviors” in the general population are not fully understood, however, census‐matched studies of these behaviors have yielded important new data. As children presenting with behaviors characteristic of ASD enter school, school psychologists are increasingly faced with the assessment and determination of individuals with disabilities improvement act (IDEIA) eligibility, often with students yet to be formally diagnosed with ASD. In many US states, a clinical diagnosis of ASD is not required for a child's eligibility to be served under the category of other health impaired as autism. Nor does a diagnosis of ASD automatically qualify a student as eligible under IDEIA. Eligibility hinges on educational impact. This article briefly reviews the current state of knowledge as it relates to understanding and evaluating children with suspected ASD in the schools, offers a framework for assessment, and discusses a set of proposed guidelines to assist in effectively utilizing assessment data in the development of individualized education plan goals.

Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study.

Previous studies have explored resting-state functional connectivity (rs-FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency-specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age-matched and sex-matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed-based FC analyses in the conventional, slow-5, and slow-4 frequency bands at each site. Image-based meta-analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta-analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow-5 frequency band instead of the conventional and slow-4 frequency bands. The deep learning results showed that, compared with the conventional and slow-4 frequency bands, the slow-5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.