Identification Of Autism Spectrum Disorder Research Articles

The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification.

ObjectiveAutism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance.MethodsWe propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information.ResultsThe experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network.ConclusionThis work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.

Analysis of the 2000 to 2018 autism and developmental disabilities monitoring network surveillance reports: Implications for primary care clinicians

Autism Spectrum Disorder (ASD), with the current prevalence at one in 44 children, is the most rapidly escalating neurodevelopmental disorder in the United States. While the 2000 to 2018 Autism and Developmental Disabilities Monitoring (ADDM) Network reports indicate progress toward identifying children with ASD by age 24 months, the actual age at which most children receive a diagnosis, ranging between age 51 to 53 months, has not significantly changed since 2000. Racial and gender disparities further complicate ASD identification. This article explores past ADDM findings to highlight ASD identification practices and provide relevant care management recommendations for primary care clinicians. Armed with this information, clinicians can improve ASD identification in their practice and advocate for beneficial, evidence-based health policies that decrease known disparities and enhance ASD care management for all.

Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification.

Brain functional network (BFN) has become an increasingly important tool to understand the inherent organization of the brain and explore informative biomarkers of neurological disorders. Pearson’s correlation (PC) is the most widely accepted method for constructing BFNs and provides a basis for designing new BFN estimation schemes. Particularly, a recent study proposes to use two sequential PC operations, namely, correlation’s correlation (CC), for constructing the high-order BFN. Despite its empirical effectiveness in identifying neurological disorders and detecting subtle changes of connections in different subject groups, CC is defined intuitively without a solid and sustainable theoretical foundation. For understanding CC more rigorously and providing a systematic BFN learning framework, in this paper, we reformulate it in the Bayesian view with a prior of matrix-variate normal distribution. As a result, we obtain a probabilistic explanation of CC. In addition, we develop a Bayesian high-order method (BHM) to automatically and simultaneously estimate the high- and low-order BFN based on the probabilistic framework. An efficient optimization algorithm is also proposed. Finally, we evaluate BHM in identifying subjects with autism spectrum disorder (ASD) from typical controls based on the estimated BFNs. Experimental results suggest that the automatically learned high- and low-order BFNs yield a superior performance over the artificially defined BFNs via conventional CC and PC.

Understanding the Barriers to Receiving Autism Diagnoses for Hispanic and Latinx Families.

Significant disparities exist in early identification of autism spectrum disorder (ASD) for Hispanic and Latinx children. ASD prevalence estimates are approximately identical for White and Black children but lower for Hispanic and Latinx children. Reasons for these racial and ethnic variations are likely multifactorial. This review sought to understand previously described barriers and limitations to accessing ASD diagnostic services in the Latinx and Hispanic communities. Three main categories of existing barriers were identified: (1) parent/family, (2) community, and (3) systemic. These barriers are complex and multifactorial in nature, including circumstantial limitations such as limited English proficiency, noncitizenship, and low-income status. These can limit health care access, and can lead to family and community cultural barriers, poor knowledge about ASD, and social stigma related to disabilities. Understanding and mitigating barriers is essential to reduce disparities to ASD diagnosis in the Hispanic and Latinx community. [Pediatr Ann. 2022;51(4):e167-e171.].

Bayley‐4 performance of very young children with autism, developmental delay, and language impairment

AbstractEarly identification of autism spectrum disorder (ASD) provides the best opportunity for children to receive evidence‐based early intervention. Research indicates that accurate diagnosis can be made as early as the toddler period when an assessment is completed by experienced clinicians. Typical ASD evaluations involve an assessment of developmental skills, such as those measured with the Bayley Scales of Infant and Toddler Development, Fourth Edition. There is limited research, however, on the use of the newest iteration, the Bayley‐4, in discriminating between various developmental disorders and delays. This study compared the performance of three groups of toddlers on the Bayley‐4: (1) ASD (n = 31), (2) developmental delay (n = 26) and (3) problems specific to language (n = 45). Results of a multivariate analysis of variance indicated mean differences existed between the ASD and language groups on the five Bayley‐4 subtests that comprise the Cognitive Motor, and Language Scales; however, logistic regression analysis indicated group membership could not be predicted by performance on the subtests. Our findings suggest children who are identified with ASD at a very young age exhibit global developmental deficits, which may help distinguish them from other groups who exhibit more targeted delays.

Patterns of Special Education Eligibility and Age of First Autism Spectrum Disorder (ASD) Identification Among US Children with ASD.

The study examined timing of autism spectrum disorder (ASD) identification in education versus health settings for 8-year-old children with ASD identified through records-based surveillance. The study also examined type of ASD symptoms noted within special education evaluations. Results indicated that children with records from only education sources had a median time to identification of ASD over a year later than children with records from health sources. Black children were more likely than White children to have records from only education sources. Restricted and repetitive behaviors were less frequently documented in educational evaluations resulting in developmental delay eligibility compared to specific ASD eligibility among children with ASD. Future research could explore strategies reduce age of identification in educational settings and increase equitable access to health evaluations.

Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach.

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.

Identification of autism spectrum disorder and interprofessional collaboration between school and clinical settings

AbstractProviding high‐quality services to autistic students and their caregivers requires interprofessional collaboration. This paper highlights the importance of collaboration between clinical and school settings to promote integrated and comprehensive care for autistic students and their caregivers. First, we present how autistic students are identified and diagnosed in school and clinical settings, with particular attention to how these processes impact access to services. Next, we present interprofessional collaborative practice (IPC) as a framework to facilitate increased collaboration between school and clinical providers working with autistic students. Key tenets of IPC that address barriers to collaboration and several examples of IPC are outlined. To assure readiness to engage in collaborative practice, we discuss graduate interprofessional education (IPE) as a means to train emerging professionals in IPC for autistic students. Finally, we highlight several examples of IPE training programs specific to autism and related developmental disabilities.

Autism spectrum disorder identification in schools: Impact of criteria, assessments, and student data for identification decisions

AbstractAutism spectrum disorder (ASD) is\ often diagnosed by clinical practitioners with criteria from the American Psychiatric Association, but an educational diagnosis is usually based on state special education law and is geared towards addressing concerns within schools. The current study aims to examine the extent to which the clinical diagnosis of ASD corresponds to the educational diagnosis of Autism, and what factors and assessments influenced the diagnosis. Data consisted of 32 randomly selected deidentified initial school evaluation reports (autism = 16; non‐autism = 16) from one midwestern state. The reports were coded based on identification methods and student data. Results indicate that the Autism Diagnostic Observation Schedule, Second Edition assessment was the strongest predictor of Autism eligibility within schools, but students with a cognitive ability score that was one standard deviation below the mean (i.e., standard score £84) were more frequently identified with educational eligibility of ASD (AU) by a factor of 14.67 as compared with individuals with a higher cognitive score. Implications for future research and practice are included.

A Nine-Item Red Flag Sign Card for Identification of Autism Spectrum Disorder among Toddlers Aged 12 to 18 Months.

There is a dearth of validated red flags measures for early identification of autism spectrum disorder (ASD) among toddlers. Hence, a new screening measure was developed. Item generation was done through literature search. Content validity (CVI) assessment was done. Criterion validity was done using Diagnostic and Statistical Manual 5 (DSM-5) as reference standard, data were collected from the case records of children with ASD diagnosis at 2 y, and evaluated for developmental milestones between 12 and 18 mo in a tertiary care setting. Item reduction of the measure from 18 to 9 was done. The area under the curve in the receiver operating characteristic (ROC)curve for new measure was 0.81 (95% CI = 0.73, 0.87); z = 7.874; p < 0.001 against DSM-5 score of ≥ 2 in the new measure; achieved sensitivity of 93.42% (95% CI = 85.3, 97.8) and specificity of 60% (95% CI = 45.9, 73.0). Thus, new validated red flag sign card (Concern-9) can be used effectively for early screening and identification of ASD among children aged 12-18 mo.

Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI.

Resting-state functional MRI (rs-fMRI) has been widely used for the early diagnosis of autism spectrum disorder (ASD). With rs-fMRI, the functional connectivity networks (FCNs) are usually constructed for representing each subject, with each element representing the pairwise relationship between brain region-of-interests (ROIs). Previous studies often first extract handcrafted network features (such as node degree and clustering coefficient) from FCNs and then construct a prediction model for ASD diagnosis, which largely requires expert knowledge. Graph convolutional networks (GCNs) have recently been employed to jointly perform FCNs feature extraction and ASD identification in a data-driven manner. However, existing studies tend to focus on the single-scale topology of FCNs by using one single atlas for ROI partition, thus ignoring potential complementary topology information of FCNs at different spatial scales. In this paper, we develop a multi-scale graph representation learning (MGRL) framework for rs-fMRI based ASD diagnosis. The MGRL consists of three major components: (1) multi-scale FCNs construction using multiple brain atlases for ROI partition, (2) FCNs representation learning via multi-scale GCNs, and (3) multi-scale feature fusion and classification for ASD diagnosis. The proposed MGRL is evaluated on 184 subjects from the public Autism Brain Imaging Data Exchange (ABIDE) database with rs-fMRI scans. Experimental results suggest the efficacy of our MGRL in FCN feature extraction and ASD identification, compared with several state-of-the-art methods.

Clinical considerations when conducting diagnostic evaluations to identify autism spectrum disorder in young children

Objective This paper provides clinical guidance supported by the literature on conducting diagnostic evaluations for young children suspected of having autism spectrum disorder (ASD). Method Authors provided a summary of clinical recommendations and evidence-based strategies for providing diagnostic evaluations for young children suspected of having ASD that are supported by the existing literature. Results ASD is a complex condition that typically presents in the first couple years of a child’s life. While many children can be accurately diagnosed by age 2, the majority of children are diagnosed much later. This article provides recommendations for evidence-based clinical practices to support accurate early identification and diagnosis in young children with autism. Recommended practices and measures for screening, comprehensive diagnostic evaluation, and differential diagnosis of ASD in early childhood are discussed. Conclusions Early identification of ASD is important to promote improved outcomes related to early intervention. There are a variety of tools and practices available to support neuropsychologists in providing accurate early diagnosis and appropriate developmental monitoring for children suspected of ASD and related neurodevelopmental conditions. There is a need for continued efforts regarding awareness of ASD in early childhood and targeted training in ASD for neuropsychologists and other disciplines who support families of young children with disabilities.

A Multimodal Approach for Identifying Autism Spectrum Disorders in Children.

Identification of autism spectrum disorder (ASD) in children is challenging due to the complexity and heterogeneity of ASD. Currently, most existing methods mainly rely on a single modality with limited information and often cannot achieve satisfactory performance. To address this issue, this paper investigates from internal neurophysiological and external behavior perspectives simultaneously and proposes a new multimodal diagnosis framework for identifying ASD in children with fusion of electroencephalogram (EEG) and eye-tracking (ET) data. Specifically, we designed a two-step multimodal feature learning and fusion model based on a typical deep learning algorithm, stacked denoising autoencoder (SDAE). In the first step, two SDAE models are designed for feature learning for EEG and ET modality, respectively. Then, a third SDAE model in the second step is designed to perform multimodal fusion with learned EEG and ET features in a concatenated way. Our designed multimodal identification model can automatically capture correlations and complementarity from behavior modality and neurophysiological modality in a latent feature space, and generate informative feature representations with better discriminability and generalization for enhanced identification performance. We collected a multimodal dataset containing 40 ASD children and 50 typically developing (TD) children to evaluate our proposed method. Experimental results showed that our proposed method achieved superior performance compared with two unimodal methods and a simple feature-level fusion method, which has promising potential to provide an objective and accurate diagnosis to assist clinicians.

A Deep Learning Method for Autism Spectrum Disorder Identification Based on Interactions of Hierarchical Brain Networks

A Deep Learning Method for Autism Spectrum Disorder Identification Based on Interactions of Hierarchical Brain Networks

Development and initial testing of the BABY-BISCUIT in an at-risk population

ABSTRACT Purpose: Early identification of autism spectrum disorder (ASD) via screeners for diagnostic measures are a high priority. At present, there is no consensus on one screener due to the need for better sensitivity and specificity. In this study, we report on the development and utility of the BABY-BISCUIT, a six-item screener based on a modified subset of items from the Baby and Infant Screen for Children with aUtIsm Traits (BISCUIT). Methods: A sample of 504 children between 17–3 9months of age, who consisted of toddlers identified as at-risk for neurodevelopmental and other health disorders, were tested during an annual screening through the Louisiana EarlySteps program. Results: An exploratory factor analysis yielded a one-factor solution (X2 = 48.62, df = 9, p = <.001). High sensitivity (i.e., 100.0%) at the cost of reduced specificity (i.e., 33.3%, AUC = 0.957) was found for an optimal screening cutoff score of 1. Conclusions: Findings from this study suggest that the BABY-BISCUIT has the potential to be a short and easily administered screener for ASD to inform whether further ASD assessment is necessary. Further investigation of convergent validity with established ASD measures is recommended.

Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018.

Problem/ConditionAutism spectrum disorder (ASD).Period Covered2018.Description of SystemThe Autism and Developmental Disabilities Monitoring Network is an active surveillance program that estimates ASD prevalence and monitors timing of ASD identification among children aged 4 and 8 years. This report focuses on children aged 4 years in 2018, who were born in 2014 and had a parent or guardian who lived in the surveillance area in one of 11 sites (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin) at any time during 2018. Children were classified as having ASD if they ever received 1) an ASD diagnostic statement (diagnosis) in an evaluation, 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code. Suspected ASD also was tracked among children aged 4 years. Children who did not meet the case definition for ASD were classified as having suspected ASD if their records contained a qualified professional’s statement indicating a suspicion of ASD.ResultsFor 2018, the overall ASD prevalence was 17.0 per 1,000 (one in 59) children aged 4 years. Prevalence varied from 9.1 per 1,000 in Utah to 41.6 per 1,000 in California. At every site, prevalence was higher among boys than girls, with an overall male-to-female prevalence ratio of 3.4. Prevalence of ASD among children aged 4 years was lower among non-Hispanic White (White) children (12.9 per 1,000) than among non-Hispanic Black (Black) children (16.6 per 1,000), Hispanic children (21.1 per 1,000), and Asian/Pacific Islander (A/PI) children (22.7 per 1,000). Among children aged 4 years with ASD and information on intellectual ability, 52% met the surveillance case definition of co-occurring intellectual disability (intelligence quotient ≤70 or an examiner’s statement of intellectual disability documented in an evaluation). Of children aged 4 years with ASD, 72% had a first evaluation at age ≤36 months. Stratified by census-tract–level median household income (MHI) tertile, a lower percentage of children with ASD and intellectual disability was evaluated by age 36 months in the low MHI tertile (72%) than in the high MHI tertile (84%). Cumulative incidence of ASD diagnosis or eligibility received by age 48 months was 1.5 times as high among children aged 4 years (13.6 per 1,000 children born in 2014) as among those aged 8 years (8.9 per 1,000 children born in 2010). Across MHI tertiles, higher cumulative incidence of ASD diagnosis or eligibility received by age 48 months was associated with lower MHI. Suspected ASD prevalence was 2.6 per 1,000 children aged 4 years, meaning for every six children with ASD, one child had suspected ASD. The combined prevalence of ASD and suspected ASD (19.7 per 1,000 children aged 4 years) was lower than ASD prevalence among children aged 8 years (23.0 per 1,000 children aged 8 years).InterpretationGroups with historically lower prevalence of ASD (non-White and lower MHI) had higher prevalence and cumulative incidence of ASD among children aged 4 years in 2018, suggesting progress in identification among these groups. However, a lower percentage of children with ASD and intellectual disability in the low MHI tertile were evaluated by age 36 months than in the high MHI group, indicating disparity in timely evaluation. Children aged 4 years had a higher cumulative incidence of diagnosis or eligibility by age 48 months compared with children aged 8 years, indicating improvement in early identification of ASD. The overall prevalence for children aged 4 years was less than children aged 8 years, even when prevalence of children suspected of having ASD by age 4 years is included. This finding suggests that many children identified after age 4 years do not have suspected ASD documented by age 48 months.Public Health ActionChildren born in 2014 were more likely to be identified with ASD by age 48 months than children born in 2010, indicating increased early identification. However, ASD identification among children aged 4 years varied by site, suggesting opportunities to examine developmental screening and diagnostic practices that promote earlier identification. Children aged 4 years also were more likely to have co-occurring intellectual disability than children aged 8 years, suggesting that improvement in the early identification and evaluation of developmental concerns outside of cognitive impairments is still needed. Improving early identification of ASD could lead to earlier receipt of evidence-based interventions and potentially improve developmental outcomes.

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2018.

Problem/ConditionAutism spectrum disorder (ASD).Period Covered2018.Description of SystemThe Autism and Developmental Disabilities Monitoring (ADDM) Network conducts active surveillance of ASD. This report focuses on the prevalence and characteristics of ASD among children aged 8 years in 2018 whose parents or guardians lived in 11 ADDM Network sites in the United States (Arizona, Arkansas, California, Georgia, Maryland, Minnesota, Missouri, New Jersey, Tennessee, Utah, and Wisconsin). To ascertain ASD among children aged 8 years, ADDM Network staff review and abstract developmental evaluations and records from community medical and educational service providers. In 2018, children met the case definition if their records documented 1) an ASD diagnostic statement in an evaluation (diagnosis), 2) a special education classification of ASD (eligibility), or 3) an ASD International Classification of Diseases (ICD) code.ResultsFor 2018, across all 11 ADDM sites, ASD prevalence per 1,000 children aged 8 years ranged from 16.5 in Missouri to 38.9 in California. The overall ASD prevalence was 23.0 per 1,000 (one in 44) children aged 8 years, and ASD was 4.2 times as prevalent among boys as among girls. Overall ASD prevalence was similar across racial and ethnic groups, except American Indian/Alaska Native children had higher ASD prevalence than non-Hispanic White (White) children (29.0 versus 21.2 per 1,000 children aged 8 years). At multiple sites, Hispanic children had lower ASD prevalence than White children (Arizona, Arkansas, Georgia, and Utah), and non-Hispanic Black (Black) children (Georgia and Minnesota). The associations between ASD prevalence and neighborhood-level median household income varied by site. Among the 5,058 children who met the ASD case definition, 75.8% had a diagnostic statement of ASD in an evaluation, 18.8% had an ASD special education classification or eligibility and no ASD diagnostic statement, and 5.4% had an ASD ICD code only. ASD prevalence per 1,000 children aged 8 years that was based exclusively on documented ASD diagnostic statements was 17.4 overall (range: 11.2 in Maryland to 29.9 in California). The median age of earliest known ASD diagnosis ranged from 36 months in California to 63 months in Minnesota.Among the 3,007 children with ASD and data on cognitive ability, 35.2% were classified as having an intelligence quotient (IQ) score ≤70. The percentages of children with ASD with IQ scores ≤70 were 49.8%, 33.1%, and 29.7% among Black, Hispanic, and White children, respectively. Overall, children with ASD and IQ scores ≤70 had earlier median ages of ASD diagnosis than children with ASD and IQ scores >70 (44 versus 53 months).InterpretationIn 2018, one in 44 children aged 8 years was estimated to have ASD, and prevalence and median age of identification varied widely across sites. Whereas overall ASD prevalence was similar by race and ethnicity, at certain sites Hispanic children were less likely to be identified as having ASD than White or Black children. The higher proportion of Black children compared with White and Hispanic children classified as having intellectual disability was consistent with previous findings.Public Health ActionThe variability in ASD prevalence and community ASD identification practices among children with different racial, ethnic, and geographical characteristics highlights the importance of research into the causes of that variability and strategies to provide equitable access to developmental evaluations and services. These findings also underscore the need for enhanced infrastructure for diagnostic, treatment, and support services to meet the needs of all children.

Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

BackgroundEarly identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis.MethodsUsing EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD).ResultsUsing a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample.ConclusionsThese results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

Family Impact During the Time Between Autism Screening and Definitive Diagnosis

Over the past 10years, identification of Autism Spectrum Disorder (ASD) risk has dramatically increased due to the wide-spread implementation of screening programs; yet, there is limited understanding about parent perceptions and experiences during the time period when risk is identified, but prior to receiving a formal diagnosis-a period that can last months to years given the long wait-lists for formal ASD evaluations. The current study aimed to examine parent perceptions of family impact (i.e., the impact their child's behaviors have on the family) between the time of risk-identification and formal diagnosis among 277 children identified as at-risk for ASD through screening positive in primary care. We aimed to compare family impact among those whose child met diagnostic criteria for ASD and those who did not. Parents of children who received a non-ASD diagnosis reported a higher baseline level of family impact (F[1, 274] = 5.82, p = .017); however, perceived difficult child behavior was a stronger predictor of family impact (t[6] = 13.11, p < .001) than later diagnostic group (t[6] = -2.10, p = .037), and child functioning did not predict family impact (t[6] = -0.31, p = .76). These results suggest that in this population, perceived difficult child behavior is a stronger predictor of family impact than later diagnostic category and should be considered an important factor in family support.

Considerations for the identification of autism spectrum disorder in children with vision or hearing impairment: A critical review of the literature and recommendations for practice

Objective: There is higher risk for autism spectrum disorder (ASD) across many pediatric neurological conditions characterized by vision impairment or hearing loss. Early and accurate identification of ASD is imperative in promoting access to appropriate and early evidenced-based intervention; however, differential diagnosis can be particularly challenging in children with sensory impairment given the heterogeneity of ASD combined with the impact of vision impairment or hearing loss/deafness on development and behavior. A neuropsychologist’s unique expertise and appreciation of the interplay between sensory and behavioral manifestations can be valuable for making an early and accurate ASD diagnosis in children who are blind/visually impaired or deaf/hard-of-hearing. This article highlights clinical considerations when identifying ASD within the context of vision impairment or hearing loss/deafness. Method: We discuss clinical considerations for the early identification of ASD in children who are blind/visually impaired and deaf/hard-of-hearing. Information presented in the article is based on a critical review of the literature and the expertise of the author group. Conclusion: Ongoing development of clinical expertise and evidence-based assessment methods are important when informing the early differential diagnosis of ASD in individuals with sensory impairment. Accurate identification is also vital for the development of targeted interventions across the lifespan.