Use of the Autism Diagnostic Observation Schedule (ADOS) in a clinical setting

Autism Spectrum Disorder Symptom Profiles in Fragile X Syndrome, Angelman Syndrome, Tuberous Sclerosis Complex and Neurofibromatosis Type 1

Studying Autism Spectrum Disorder (ASD) heterogeneity in biologically homogeneous samples may increase our knowledge of ASD etiology. Fragile X syndrome (FXS), Angelman syndrome (AS), Tuberous Sclerosis Complex (TSC), and Neurofibromatosis type 1 (NF1) are monogenic disorders with high a prevalence of ASD symptomatology. This study aimed to identify ASD symptom profiles in a large group of children and adolescents (0;9-28 years) with FXS, AS, TSC, and NF1. Data on ASD symptomatology (Autism Diagnostic Observation Scale (ADOS-2) & Social Responsiveness Scale (SRS-2)) were collected from children and adolescents with FXS (n = 54), AS (n = 93), TSC (n = 112), and NF1 (n = 278). To identify groups of individuals with similar ASD profiles, we performed two latent profile analyses. We identified a four-profile model based on the ADOS-2, with a (1) 'Non-spectrum symptom profile', (2) 'Social Affect symptom profile', (3)'Restricted/Repetitive Behaviors symptom profile', and (4)'ASD symptom profile'. We also identified a four-profile model based on the SRS, with a (1)'Non-clinical symptom profile', (2)'Mild symptom profile', (3)'Moderate symptom profile', and (4)'Severe symptom profile'. Although each syndrome group exhibited varying degrees of severity, they also displayed heterogeneity in the profiles in which they were classified. We found distinct ASD symptom profiles in a population consisting of children and adolescents with FXS, AS, TSC, and NF1. Our study highlights the importance of a personalized approach to the identification and management of ASD symptoms in rare genetic syndromes. Future studies should aim to include more domains of functioning and investigate the stability of latent profiles over time.

Creating a diagnostic assessment model for autism spectrum disorder by differentiating lexicogrammatical choices through machine learning

This study explores the challenge of differentiating autism spectrum (AS) from non-AS conditions in adolescents and adults, particularly considering the heterogeneity of AS and the limitations ofssss diagnostic tools like the ADOS-2. In response, we advocate a multidimensional approach and highlight lexicogrammatical analysis as a key component to improve diagnostic accuracy. From a corpus of spoken language we developed, interviews and story-recounting texts were extracted for 64 individuals diagnosed with AS and 71 non-AS individuals, all aged 14 and above. Utilizing machine learning techniques, we analyzed the lexicogrammatical choices in both interviews and story-recounting tasks. Our approach led to the formulation of two diagnostic models: the first based on annotated linguistic tags, and the second combining these tags with textual analysis. The combined model demonstrated high diagnostic effectiveness, achieving an accuracy of 80%, precision of 82%, sensitivity of 73%, and specificity of 87%. Notably, our analysis revealed that interview-based texts were more diagnostically effective than story-recounting texts. This underscores the altered social language use in individuals with AS, a csrucial aspect in distinguishing AS from non-AS conditions. Our findings demonstrate that lexicogrammatical analysis is a promising addition to traditional AS diagnostic methods. This approach suggests the possibility of using natural language processing to detect distinctive linguistic patterns in AS, aiming to enhance diagnostic accuracy for differentiating AS from non-AS in adolescents and adults.

Enhanced interest in letters and numbers in autistic children

BACKGROUND

An intense and precocious interest in written material, together with a discrepancy between decoding and reading comprehension skills are defining criteria for hyperlexia, which is found in up to 20% of autistic individuals. It may represent the extreme end of a broader interest in written material in autism. This study examines the magnitude and nature of the interest in written material in a large population of autistic and non-autistic children.

METHODS

All 701 children (391 autistic, 310 non-autistic) under the age of 7 referred to an autism assessment clinic over a span of 4 years were included. Ordinal logistic regressions assessed the association between diagnosis and the level of interest in letters and numbers. A nested sample of parents of 138 autistic, 99 non-autistic clinical, and 76 typically developing (TD) children completed a detailed questionnaire. Cox proportional hazards models analyzed the age of emergence of these interests. Linear regressions evaluated the association between diagnosis and interest level. The frequency of each behaviour showing interest and competence with letters and numbers were compared.

RESULTS

In the two studies, 22 to 37% of autistic children had an intense or exclusive interest in letters. The odds of having a greater interest in letters was 2.78 times higher for autistic children than for non-autistic clinical children of the same age, and 3.49 times higher for the interest in numbers, even if 76% of autistic children were minimally or non-verbal. The age of emergence of these interests did not differ between autistic and TD children and did not depend on their level of oral language. Non-autistic children showed more interest in letters within a social context.

LIMITATIONS

The study holds limitations inherent to the use of a phone questionnaire with caregivers and missing sociodemographic information.

CONCLUSIONS

The emergence of the interest of autistic children toward written language is contemporaneous to the moment in their development where they display a strong deficit in oral language. Together with recent demonstrations of non-social development of oral language in some autistic children, precocious and intense interest in written material suggests that language acquisition in autism may follow an alternative developmental pathway.

Decoding Autism: Uncovering patterns in brain connectivity through sparsity analysis with rs-fMRI data

BACKGROUND

In the realm of neuro-disorders, precise diagnosis and treatment rely heavily on objective imaging-based biomarker identification. This study employs a sparsity approach on resting-state fMRI to discern relevant brain region connectivity for predicting Autism.

NEW METHOD

The proposed methodology involves four key steps: (1) Utilizing three probabilistic brain atlases to extract functionally homogeneous brain regions from fMRI data. (2) Employing a hybrid approach of Graphical Lasso and Akaike Information Criteria to optimize sparse inverse covariance matrices for representing the brain functional connectivity. (3) Employing statistical techniques to scrutinize functional brain structures in Autism and Control subjects. (4) Implementing both autoencoder-based feature extraction and entire feature-based approach coupled with AI-based learning classifiers to predict Autism.

RESULTS

The ensemble classifier with the extracted feature set achieves a classification accuracy of 84.7% ± 0.3% using the MSDL atlas. Meanwhile, the 1D-CNN model, employing all features, exhibits superior classification accuracy of 88.6% ± 1.7% with the Smith 2009 (rsn70) atlas.

COMPARISON WITH EXISTING METHOD (S)

The proposed methodology outperforms the conventional correlation-based functional connectivity approach with a notably high prediction accuracy of more than 88%, whereas considering all direct and noisy indirect region-based functional connectivity, the traditional methods bound the prediction accuracy within 70% to 79%.

CONCLUSIONS

This study underscores the potential of sparsity-based FC analysis using rs-fMRI data as a prognostic biomarker for detecting Autism.

An Internal Consistency Reliability Study of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application With Autistic Individuals

Introduction Many psychometric studies have scrutinized the dependability of different instruments for evaluating and treating autism using applied behavior analysis (ABA). However, there has been no exploration into the psychometric attributes of the Catalyst Datafinch Applied Behavior Analysis Data Collection Application, namely, internal consistency reliability measures. Materials and methods  Four datasets were extracted (n=100, 98, 103, and 62) from published studies at The Oxford Center, Brighton, MI, ranging from March 19, 2023, through January 8, 2024, using Catalyst Datafinch as the data collection tool. All data were gathered by Board Certified Behavior Analysts (BCBAs) and behavioral technicians and designed to replicate how practitioners collect traditional paper and pencil data. SPSS Statistics (v. 29.0) computed internal consistency reliability measures, including Cronbach's alpha, inter-item, split-half, and interclass correlation coefficients. Results  Dataset #1: Cronbach's alpha was 0.916 with seven items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.777, indicating good reliability, and for Part 2 was 0.972, indicating excellent reliability. Guttman split-half coefficient was 0.817, indicating good reliability. Inter-item correlation coefficients ranged from 0.474 to 0.970. The average measures interclass correlation (ICC) was 0.916, indicating excellent reliability. Single measures (ICC) reliability was 0.609, indicating acceptable reliability. Dataset #2: Cronbach's alpha was 0.954 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.912, indicating excellent reliability, and for Part 2 was 0.975, indicating excellent reliability. Guttman split-half coefficient was 0.917, indicating excellent reliability. Inter-item correlation coefficients ranged from 0.827 to 0.977. Average measures (ICC) was 0.954, indicating excellent reliability. Single measures (ICC) reliability was 0.875, indicating good reliability. Dataset #3: Cronbach's alpha was 0.974 with three items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.978, indicating excellent reliability. Split-half reliability for Part 2 was 0.970, indicating excellent reliability. Guttman split-half coefficient was 0.935, indicating excellent reliability. Inter-item correlation coefficients ranged from 0.931 to 0.972. The average measures (ICC) was 0.974, indicating excellent reliability. Single measures (ICC) reliability was 0.926, indicating excellent reliability. Dataset #4: Cronbach's alpha was 0.980 with 12 items, indicating excellent reliability. Cronbach's split-half reliability for Part 1 was 0.973, indicating excellent reliability. Split-half reliability for Part 2 was 0.996, indicating excellent reliability. Guttman split-half coefficient was 0.838, indicating good reliability. Inter-item correlation coefficients ranged from 0.692 to 0.999. The average measures (ICC) was 0.980, indicating excellent reliability. Single measures (ICC) reliability was 0.804, indicating good reliability. Conclusions These results suggest that Catalyst Datafinch demonstrates high internal consistency reliability when used with individuals with autism. This indicates that the application is reliable for collecting and analyzing behavioral data in this population. The ratings ranged from good to excellent, indicating a high consistency in the measurements.

Policies for Individuals With Autism: Gaps, Research, and Recommendations

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social, behavioral, and learning challenges. Individuals with autism and their families often struggle to get an appropriate diagnosis and continuation of specialty services, including general healthcare, mental health and transition services, special education, employment, and social and emotional support. This paper presents information about the current policies and support mechanisms that exist to help these individuals and their families. This paper identifies the gaps and recommends areas of improvement based on evidence-based research and current data. ASD is a lifelong disability without a cure, but by constructing robust policies and providing enhanced support, the quality of lives of those with ASD and their families can be improved.

Language Skill Differences Further Distinguish Social Sub-types in Children with Autism

This study investigated heterogeneity in language skills of children with autism and their relationship with different autistic social subtypes. Data from 90 autistic and 30 typically developing children were analyzed. Results showed that autistic social subtypes varied in language skill problems (aloof > passive > active-but-odd). There was a negative association between aloof dimension scores and language performance but positive for the active-but-odd dimension and no association in the passive one. Moreover, aloof dimension score was the main contributor to language performance. A receiver operating characteristic analysis suggested language vocabulary as an additional component in differentiating autistic social subtypes. These findings demonstrate that variations in language skills in autistic children provide additional information for discriminating their social subtype.

Language acquisition can be truly atypical in autism: Beyond joint attention

Language profiles in autism are variable and atypical, with frequent speech onset delays, but also, in some cases, unusually steep growth of structural language skills. Joint attention is often seen as a major predictor of language in autism, even though low joint attention is a core characteristic of autism, independent of language levels. In this systematic review of 71 studies, we ask whether, in autism, joint attention predicts advanced or only early language skills, and whether it may be independent of language outcomes. We consider only conservative estimates, and flag studies that include heterogenous samples or no control for non-verbal cognition. Our review suggests that joint attention plays a pivotal role for the emergence of language, but is also consistent with the idea that some autistic children may acquire language independently of joint attention skills. We propose that language in autism should not necessarily be modelled as a quantitative or chronological deviation from typical language development, and outline directions to bring autistic individuals' atypicality within the focus of scientific inquiry.

Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping

Autistic spectrum disorder (ASD) is a neurodevelopmental condition that characterises a range of people, from individuals who are not able to speak to others who have good verbal communications. The disorder affects the way people see, think, and behave, including their communications and social interactions. Identifying autistic traits, preferably in the early stages, is fundamental for clinicians in expediting referrals, and hence enabling patients to access to required healthcare services. This article investigates various ASD behavioral features in toddlers and proposes a data process using machine-learning techniques. The aims of this study were to identify early behavioral features that can help detect ASD in toddlers and to map these features to the neurodevelopment behavioral areas of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). To achieve these aims, the proposed data process assesses several behavioral features using feature selection techniques, then constructs a classification model based on the chosen features. The empirical results show that during the screening process of toddlers, cognitive features related to communications, social interactions, and repetitive behaviors were most relevant to ASD. For the machine-learning algorithms, the predictive accuracy of Bayesian network (Bayes Net) and logistic regression (LR) models derived from ASD behavioral data subsets were consistent pinpointing to the suitability of ML techniques in predicting ASD.

Why are only some children with autism spectrum disorder misclassified by the social communication questionnaire? An empirical investigation of individual differences in sensitivity and specificity in a clinic-referred sample

BACKGROUND

The Social Communication Questionnaire (SCQ) is a checklist for autism spectrum disorder (ASD) commonly used in research and clinical practice. While the original validation study suggested that the SCQ was an accurate ASD screener with satisfactory sensitivity and specificity, subsequent studies have yielded mixed results, with some revealing low sensitivity, low specificity, and low utility in some settings.

METHOD

The present study examined the psychometric properties of the SCQ as well as the individual difference characteristics of 187 individuals with and without autism spectrum disorder (ASD) who were misclassified or accurately classified by the SCQ in a clinic-referred sample.

RESULTS

The SCQ showed suboptimal sensitivity and specificity, regardless of age and sex. Compared to true positives, individuals classified as false positives displayed greater externalizing and internalizing problems, whereas individuals classified as false negatives displayed better social communication and adaptive skills.

CONCLUSIONS

The findings suggest that non-autistic developmental and behavioral individual difference characteristics may explain high rates of misclassification using the SCQ. Clinicians and researchers could consider using the SCQ in combination with other tools for young children with internalizing and externalizing symptoms and other more complex clinical presentations.

Genesis, modelling and methodological remedies to autism heterogeneity

Diagnostic criteria used in autism research have undergone a shift towards the inclusion of a larger population, paralleled by increasing, but variable, estimates of autism prevalence across clinical settings and continents. A categorical diagnosis of autism spectrum disorder is now consistent with large variations in language, intelligence, comorbidity, and severity, leading to a heterogeneous sample of individuals, increasingly distant from the initial prototypical descriptions. We review the history of autism diagnosis and subtyping, and the evidence of heterogeneity in autism at the cognitive, neurological, and genetic levels. We describe two strategies to address the problem of heterogeneity: clustering, and truncated-compartmentalized enrollment strategy based on prototype recognition. The advances made using clustering methods have been modest. We present an alternative, new strategy for dissecting autism heterogeneity, emphasizing incorporation of prototypical samples in research cohorts, comparison of subgroups defined by specific ranges of values for the clinical specifiers, and retesting the generality of neurobiological results considered to be acquired from the entire autism spectrum on prototypical cohorts defined by narrow specifiers values.

Replication study for ADOS-2 cut-offs to assist evaluation of autism spectrum disorder

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) has been widely used for ASD assessment. While prior studies investigated sensitivity and specificity of ADOS-2 Modules 1-3, there has been limited research addressing algorithm cut-off scores to optimize ADOS-2 classification. The goal of this study was to assess algorithm cut-off scores for diagnosing ASD with Modules 1-3, and to evaluate alignment of the ADOS-2 classification with the best estimate clinical diagnosis. Participants included 3144 children aged 31 months or older who received ADOS-2 Modules 1-3, as well as the best estimate clinical diagnosis. Five classification statistics were reported for each module: sensitivity, specificity, positive predictive value, negative predictive value, and accuracy (i.e., Receiver Operator Classification Statistic), and these statistics were calculated for the optimal cut-off score. Frequency tables were used to compare ADOS-2 classification and the best estimate clinical diagnosis. Half of the sample received Module 3, 21% received Module 2, and 29% received Module 1. The overall prevalence of ASD was 60%; the male-to-female ratio was 4:1, and half of the sample was non-White. Across all modules, the autism spectrum cut-off score from the ADOS-2 manual resulted in high sensitivity (95%+) and low specificity (63%-73%). The autism cut-off score resulted in better specificity (76%-86%) with favorable sensitivity (81%-94%). The optimal cut-off scores for all modules based on the current sample were within the autism spectrum classification range except Module 2 Algorithm 2. In the No ASD group, 29% had false positives (ADOS-2 autism spectrum classification or autism classification). The ADOS-2 autism spectrum classification did not indicate directionality for diagnostic outcome (ASD 56% vs. No ASD 44%). While cut-off scores of ADOS-2 Modules 1-3 in the manual yielded good clinical utility in ASD assessment, false positives and low predictability of the autism spectrum classification remain challenging for clinicians.

Autism as emergent and transactional

The current epistemology of autism as a phenotype derives from the consistency of historical accounts and decades of work within the tradition of descriptive epidemiology, culminating in current categorical descriptions within DSM and ICD nosologies and the concept of "prototypical autism." The demonstrated high heritability of this phenotype has led to an essentialist theory of autism as a biological entity and the concerted search within the developmental brain and genetic science for discrete biological markers. This search has not revealed simple markers explaining autistic outcomes and has led to moves towards a more dimensional account. This article proposes an alternative transactional approach. It proposes to understand autistic states as an emergent property within a complex developmental system; as the neurodivergent brain, and mind and body, encounter their social and physical environment within early development. Key evidence in support of this approach comes from random allocation intervention trials based on such transactional development theory, both in the infancy pre-diagnostic prodrome and the early post-diagnostic period. In replicated evidence, these intervention trials show that a targeted alteration in the quality of social transactional environment available for the child leads to significant, predictable, and sustained alterations in the outcome dimensional autistic phenotype over time; and further, in one prodromal trial, to a significant reduction in later categorical classification status. The inference from this evidence is that the prototypical autistic phenotype is to a degree malleable with a changed experienced social environment and that it is emergent from its constituent traits. Such a transactional approach enlarges our notion of the phenotype and brings the study of autism within mainstream individual difference developmental science. It challenges essentialist views, for instance as to intrinsic autistic "social avoidance" or theory of mind empathy deficits, integrates dimensional and categorical perspectives, and is consistent with the lived experience of autistic people and their advocacy for improved understanding within a social model.

Diagnostic validity of Autism Diagnostic Observation Schedule, second edition (K-ADOS-2) in the Korean population

Background

Although the Korean version of the Autism Diagnostic Observation Schedule-2 (K-ADOS‐2) is widely being used to diagnose autism spectrum disorder (ASD) in South Korea, no previous study has examined the validity and reliability of all modules of K-ADOS-2 across a wide age range, particularly older children, adolescents, and adults.

Method

Data from 2,158 participants were included (mean age = 79.7 months; 73.6% male): 1473 participants with ASD and 685 participants without ASD (Toddler Module, n = 289; Module 1, n = 642; Module 2 n = 574; Module 3 n = 411; Module 4, n = 242). Participants completed a battery of tests, including the K-ADOS or K-ADOS-2 and other existing diagnostic instruments. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve, positive predictive value (PPV), negative predictive value (NPV), Cohen’s kappa (k), and agreement with existing diagnostic instruments were computed. Cronbach’s α values were also calculated.

Results

All developmental cells of the K-ADOS-2 showed sufficient ranges of sensitivity 85.4–100.0%; specificity, 80.4–96.8%; area under the ROC curve, .90-.97; PPV, 77.8–99.3%; NPV, 80.6–100.0%; and k values, .83–.92. The kappa agreements of developmental cells with existing diagnostic instruments ranged from .20 to .90. Cronbach’s α values ranged from .82 to .91 across all developmental cells.

Limitation

The best-estimate clinical diagnoses made in this study were not independent of the K-ADOS-2 scores. Some modules did not include balanced numbers of participants in terms of gender and diagnostic status.

Conclusion

The K-ADOS-2 is a valid and reliable instrument in diagnosing ASD in South Korea. Future studies exploring the effectiveness of the K-ADOS-2 in capturing restricted, repetitive behaviors and differentiating ASD from other developmental disabilities are needed.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-022-00506-5.

Toward mapping pragmatic impairment of autism spectrum disorder individuals through the development of a corpus of spoken Japanese

The central symptom of autism spectrum disorder (ASD) is deficiency in social communication, which is generally viewed as being caused by pragmatic impairment (PI). PI is difficulty in using language appropriately in social situations. Studies have confirmed that PI is the result of neurological, cognitive, linguistic, and sensorimotor dysfunctions involving intricately intertwined factors. To elucidate the whole picture of this impairment, an approach from a multifaceted perspective fusing those factors is necessary. To this end, comprehensive PI mapping is a must, since no comprehensive mapping has yet been developed. The aim of this research is to present a model of annotation scheme development and corpus construction to efficiently visualize and quantify for statistical investigation occurrences of PI, which enables comprehensive mapping of PI in the spoken language of Japanese ASD individuals. We constructed system networks (lexicogrammatical option systems speakers make choices from) in the theoretical framework of Systemic Functional Linguistics, from which we developed an annotation scheme to comprehensively cover PI. Since system network covers all possible lexicogrammatical choices in linguistic interaction, it enables a comprehensive view of where and in what lexicogrammar PI occurs. Based on this annotation scheme, we successfully developed the Corpus of ASD + Typically Developed Spoken Language consisting of texts from 1,187 audiotaped tasks performed by 186 ASD and 106 typically developed subjects, accommodating approximately 1.07 million morphemes. Moreover, we were successful in the automatization of the annotation process by machine learning, accomplishing a 90 percent precision rate. We exemplified the mapping procedure with a focus on the spoken use of negotiating particles. Our model corpus is applicable to any language by incorporating our method of constructing the annotation scheme, and would give impetus to defining PI from a cross-linguistic point of view, which is needed because PI of ASD reflects cross-linguistic differences.

Autism Symptoms in Children and Young Adults With Fragile X Syndrome, Angelman Syndrome, Tuberous Sclerosis Complex, and Neurofibromatosis Type 1: A Cross-Syndrome Comparison

OBJECTIVE

The etiology of autism spectrum disorder (ASD) remains unclear, due to genetic heterogeneity and heterogeneity in symptoms across individuals. This study compares ASD symptomatology between monogenetic syndromes with a high ASD prevalence, in order to reveal syndrome specific vulnerabilities and to clarify how genetic variations affect ASD symptom presentation.

METHODS

We assessed ASD symptom severity in children and young adults (aged 0-28 years) with Fragile X Syndrome (FXS, n = 60), Angelman Syndrome (AS, n = 91), Neurofibromatosis Type 1 (NF1, n = 279) and Tuberous Sclerosis Complex (TSC, n = 110), using the Autism Diagnostic Observation Schedule and Social Responsiveness Scale. Assessments were part of routine clinical care at the ENCORE expertise center in Rotterdam, the Netherlands. First, we compared the syndrome groups on the ASD classification prevalence and ASD severity scores. Then, we compared individuals in our syndrome groups with an ASD classification to a non-syndromic ASD group (nsASD, n = 335), on both ASD severity scores and ASD symptom profiles. Severity scores were compared using MANCOVAs with IQ and gender as covariates.

RESULTS

Overall, ASD severity scores were highest for the FXS group and lowest for the NF1 group. Compared to nsASD, individuals with an ASD classification in our syndrome groups showed less problems on the instruments' social domains. We found a relative strength in the AS group on the social cognition, communication and motivation domains and a relative challenge in creativity; a relative strength of the NF1 group on the restricted interests and repetitive behavior scale; and a relative challenge in the FXS and TSC groups on the restricted interests and repetitive behavior domain.

CONCLUSION

The syndrome-specific strengths and challenges we found provide a frame of reference to evaluate an individual's symptoms relative to the larger syndromic population and to guide treatment decisions. Our findings support the need for personalized care and a dimensional, symptom-based diagnostic approach, in contrast to a dichotomous ASD diagnosis used as a prerequisite for access to healthcare services. Similarities in ASD symptom profiles between AS and FXS, and between NF1 and TSC may reflect similarities in their neurobiology. Deep phenotyping studies are required to link neurobiological markers to ASD symptomatology.

Heterogeneity thwarts autism explanatory power: A proposal for endophenotypes

Many researchers now believe that autism heterogeneity is likely to include many disorders, but most research is based on samples defined by the DSM-5 Autism Spectrum Disorder (ASD) criteria. However, individuals diagnosed with autism have complex and varied biological causes for their symptoms. Therefore, autism is not a unitary biological entity. And although autism is significantly different from typical development, autism is not a unitary clinical disorder because diagnosed individuals vary in symptom patterns, comorbidities, biomarkers, and gene variants. The DSM-5 ASD criteria were designed to reduce heterogeneity, and there have been many other efforts to reduce autism heterogeneity including using more stringent clinical criteria, dividing autism into low and high functioning groups, creating subgroups, and by studying larger samples. However, to date these efforts have not been successful. Heterogeneity is extensive and remains unexplained, and no autism pathophysiology has been discovered. Most importantly, heterogeneity has hindered the explanatory power of the autism diagnosis to discover drug regimens and effective behavioral treatments. The paper proposes that possible transdiagnostic endophenotypes may reduce autism heterogeneity. Searching for transdiagnostic endophenotypes requires exploring autism symptoms outside of the framework of the DSM-5 autism diagnosis. This paper proposes that researchers relax diagnostic criteria to increase the range of phenotypes to support the search for transdiagnostic endophenotypes. The paper proposes possible candidates for transdiagnostic endophenotypes. These candidates are taken from DSM-5 ASD criteria, from concepts that have resulted from researched theories, and from symptoms that are the result of subtyping. The paper then sketches a possible basis for a future transdiagnostic endophenotypes screening tool that includes symptoms of autism and other neurodevelopmental disorders.

Autism comorbidities show elevated female-to-male odds ratios and are associated with the age of first autism diagnosis

OBJECTIVE

To investigate the association between the comorbidity rates in autism and sex, birth year and the age at which autism was first diagnosed and compare the relative impact of each.

METHOD

Using the Danish National Patient Registry, cumulative incidences up to the age of 16 for 11 comorbid conditions (psychosis, affective disorders, anxiety disorders, conduct disorder, eating disorders, obsessive-compulsive disorder, attention-deficit hyperactivity disorder, epilepsy, tic disorders, sleep disorders or intellectual disability) were calculated for individuals with autism (N = 16,126) and non-autism individuals (N = 654,977). Individuals were further stratified based on the age at the first autism diagnoses and comorbid diagnoses up to the age of 16 were compared.

RESULTS

Most comorbidities were significantly associated with birth year and sex. Female/male odds ratios for 8 of 11 comorbid conditions were up to 67% higher than the corresponding odds ratios in the non-autism population, including conditions that are generally more common in males than in females as well as conditions that are more common in females. All comorbidity rates were significantly associated with the age at the first autism diagnosis, which was a stronger predictor than sex and birth year for 8 conditions.

CONCLUSIONS

Comorbidity rates for females exceed what would be expected based on the sex ratios among non-autistic individuals, indicating that the association between autism and comorbidity is stronger in females. Comorbidity rates are also highly dependent on the age at the first autism diagnosis, which may contribute to autism heterogeneity in research and clinical practice.

AAA screening in adults with ASD: a retrospective cohort study

Purpose

The Adult Asperger Assessment (AAA), comprising the Autism Questionnaire, the Empathy Quiotient and the Relatives Questionnaire is a commonly used screening tool designed to identify adults who may benefit from a further clinical assessment for autism spectrum disorder. The purpose of this paper is to investigate the usefulness of this screening measure in a clinical setting.

Design/methodology/approach

This retrospective cohort study comprised of 192 service users referred for diagnostic assessment of Autism by a specialist service of the National Health Service. The authors evaluated the diagnostic accuracy of the AAA by investigating if the Autism Questionnaire, the Empathy Quiotient and the Relatives Questionnaire were able to predict the diagnostic outcome of Autism in a clinical setting.

Findings

Scores from the Relatives Questionnaire can accurately predict diagnostic outcome. No evidence of accuracy for the Autism Questionnaire or the Empathy Quotient was apparent. Based on the findings, the authors recommend clinicians are cautious when interpreting results of the AAA.

Research limitations/implications

It should be acknowledged that the results may not be generalisable to whole populations. Also, the authors used the full item versions of the scales; therefore, the findings are most applicable to studies which did similar.

Originality/value

This study highlights the need for investigation into the lack of validation of commonly used screening measures in autistic populations.

Autism Diagnostic Observation Schedule (ADOS-2) elevations in a clinical sample of children and adolescents who do not have autism: Phenotypic profiles of false positives

OBJECTIVE

While the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) shows high sensitivity for detecting autism spectrum disorder (ASD) when present (i.e. true positives), scores on the ADOS-2 may be falsely elevated for individuals with cognitive impairments or psychological concerns other than ASD (i.e. false positives). This study examined whether demographic, psychological, cognitive, and/or adaptive factors predict ADOS-2 false positives and which psychiatric diagnoses most often result in false positives.

METHOD

Sensitivity, specificity, false positive, and false negative rates were calculated among 214 5- to 16-year-old patients who completed an ADOS-2 (module 3) as part of an ASD diagnostic evaluation. Additional analyses were conducted with the 101 patients who received clinically elevated ADOS-2 scores (i.e. 56 true positives and 45 false positives).

RESULTS

Results revealed a 34% false positive rate and a 1% false negative rate. False positives were slightly more likely to be male, have lower restricted and repetitive behavior (RRB) severity scores on the ADOS-2, and demonstrate elevated anxiety during the ADOS-2. Neither IQ, adaptive functioning, nor caregiver-reported emotional functioning was predictive of false positive status. Trauma-related psychiatric diagnoses were more common among false positives.

CONCLUSIONS

The ADOS-2 should not be used in isolation to assess for ASD, and, in psychiatrically-complex cases, RRB symptom severity may be particularly helpful in differentiating ASD from other psychiatric conditions. Additionally, heightened levels of anxiety, more so than overactivity or disruptive behavior, may lead to non-ASD specific elevations in ADOS-2 scores.

The utility of parent-report screening tools in differentiating autism versus attention-deficit/hyperactivity disorder in school-age children

LAY ABSTRACT

We tested the ability of a short, recently developed parent interview and two widely used parent-report questionnaires to discriminate school-age verbal children with autism spectrum disorder from those with attention-deficit/hyperactivity disorder without autism spectrum disorder (ADHD_w/oASD). These measures included the Autism Symptom Interview - School-Age, the Social Responsiveness Scale - 2nd Edition, and the Social Communication Questionnaire - Lifetime. The classification accuracy of all three parent screeners fell in the moderate range. Accuracy varied by instrument, and the Social Communication Questionnaire - Lifetime questionniare showed the highest accuracy. Children with autism spectrum disorder who were incorrectly classified by all parent screeners did not differ from those correctly classified in regard to demographics, intellectual abilities, nor in any specific clinical area beyond general parent concerns. These findings showed that there are valid screening options for assessing school-age verbal children with autism spectrum disorder versus ADHD_w/oASD. They also underscore the need to assess multiple sources of information for increased accuracy.

Identification of the most indicative and discriminative features from diagnostic instruments for children with autism

Background

Diagnosing autism spectrum disorder (ASD) is complex and time-consuming. The present work systematically examines the importance of items from the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) in discerning children with and without ASD. Knowledge of the most discriminative features and their underlying concepts may prove valuable for the future training tools that assist clinicians to substantiate or extenuate a suspicion of ASD in nonverbal and minimally verbal children.

Methods

In two samples of nonverbal (N = 466) and minimally verbal (N = 566) children with ASD (N = 509) and other mental disorders or developmental delays (N = 523), we applied random forests (RFs) to (i) the combination of ADI-R and ADOS data versus (ii) ADOS data alone. We compared the predictive performance of reduced feature models against outcomes provided by models containing all features.

Results

For nonverbal children, the RF classifier indicated social orientation to be most powerful in differentiating ASD from non-ASD cases. In minimally verbal children, we find language/speech peculiarities in combination with facial/nonverbal expressions and reciprocity to be most distinctive.

Conclusion

Based on machine learning strategies, we carve out those symptoms of ASD that prove to be central for the differentiation of ASD cases from those with other developmental or mental disorders (high specificity in minimally verbal children). These core concepts ought to be considered in the future training tools for clinicians.

A radical change in our autism research strategy is needed: Back to prototypes

The evolution of autism diagnosis, from its discovery to its current delineation using standardized instruments, has been paralleled by a steady increase in its prevalence and heterogeneity. In clinical settings, the diagnosis of autism is now too vague to specify the type of support required by the concerned individuals. In research, the inclusion of individuals categorically defined by over-inclusive, polythetic criteria in autism cohorts results in a population whose heterogeneity runs contrary to the advancement of scientific progress. Investigating individuals sharing only a trivial resemblance produces a large-scale type-2 error (not finding differences between autistic and dominant population) rather than detecting mechanistic differences to explain their phenotypic divergences. The dimensional approach of autism proposed to cure the disease of its categorical diagnosis is plagued by the arbitrariness of the dimensions under study. Here, we argue that an emphasis on the reliability rather than specificity of diagnostic criteria and the misuse of diagnostic instruments, which ignore the recognition of a prototype, leads to confound autism with the entire range of neurodevelopmental conditions and personality variants. We propose centering research on cohorts in which individuals are selected based on their expert judged prototypicality to advance the theoretical and practical pervasive issues pertaining to autism diagnostic thresholds. Reversing the current research strategy by giving more weight to specificity than reliability should increase our ability to discover the mechanisms of autism. LAY SUMMARY: Scientific research into the causes of autism and its mechanisms is carried out on large cohorts of people who are less and less different from the general population. This historical trend may explain the poor harvest of results obtained. Services and intervention are provided according to a diagnosis that now encompasses extremely different individuals. Last, we accept as a biological reality the constant increase over the years in the proportion of autistic people among the general population. These drifts are made possible by the attribution of a diagnosis of autism to people who meet vague criteria, rather than to people who experienced clinicians recognize as autistic. We propose to change our research strategy by focusing on the study of the latter, fewer in number, but more representative of the "prototype" of autism. To do this, it is necessary to clearly distinguish the population on which the research is carried out from that to which we provide support. People must receive services according to their needs, and not according to the clarity of their diagnosis.

Replication study of ADOS-2 Toddler Module cut-off scores for autism spectrum disorder classification

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) has been regarded as the gold standard assessment of autism spectrum disorder (ASD). While clinical validity of ADOS-2 Modules 1-4 have been extensively studied, there has been very limited research examining the clinical validity of ADOS-2 Toddler Module. The goal of this study was to examine alignment of the ADOS-2 Toddler Module classification with clinicians' determination of ASD, with assessing cut-off scores for diagnosing ASD in toddlers. A total of 412 toddlers ages 12-30 months who received ADOS-2 Toddler Module as well as a best estimate clinical (BEC) diagnosis, were included in this study. ADOS-2 Toddler Module cut-off scores were determined using the appropriate algorithms (Algorithm 1 for toddlers aged 12-20 months and those aged 21-30 months with <5 words, Algorithm 2 for toddlers aged 21-30 months with 5 words or more). Receiver operating characteristic (ROC) curves were used to assess cut-off scores that optimized sensitivity and specificity when compared against BEC diagnosis. The selected cut-off scores were examined using frequency tables to compare ADOS-2 classification against BEC diagnosis. For Algorithm 1, classification statistics were optimized at the cut-off score of 12 with an area under the curve (AUC) of 0.92. For Algorithm 2, classification statistics were optimized at the cut-off score of 10 with an AUC of 0.96. The ADOS-2 Toddler Module classification is strongly aligned with BEC diagnosis. The optimal cut-off scores identified in the current study reflect the same results configured by the prior study. LAY SUMMARY: ADOS-2 Toddler Module has been widely used for the ASD assessment, but there have been limited research on its clinical validity. This study is the first replication of the ADOS-2 Toddler Module with a large independent sample. We examined alignment of the ADOS-2 Toddler Module classification with clinicians' determination of ASD, with assessing cut-off scores, and confirmed the clinical validity of ADOS-2 Toddler Module. Cut-off scores of ADOS-2 Toddler Module cited in the manual yielded best clinical utility for diagnosing ASD in toddlers.

A Longitudinal Study of Language Trajectories and Treatment Outcomes of Early Intensive Behavioral Intervention for Autism

The present study examined language trajectories and placement outcomes for children with autism spectrum disorder (ASD) receiving early intensive behavioral intervention (EIBI). Language measures were collected at baseline and 6, 12, 18, 24, and 36 months or until exit from EIBI in 131 children with ASD. Growth models estimated overall and subgroup language trajectories. Overall, children receiving EIBI showed substantial increases in language relative to normative expectations. Earlier age at EIBI start, higher baseline cognitive function, and lower baseline ASD severity predicted better language trajectories. Although there was significant variability in language trajectories and educational outcomes, most children showed significant increases in language scores, relative to normative expectations. Additional research, in more representative samples, is needed to understand this variability.

Systematic Review and Meta-Analysis of the Clinical Utility of the ADOS-2 and the ADI-R in Diagnosing Autism Spectrum Disorders in Children

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) and the Autism Diagnostic Interview, Revised (ADI-R) have high accuracy as diagnostic instruments in research settings, while evidence of accuracy in clinical settings is less robust. This meta-analysis focused on efficacy of these measures in research versus clinical settings. Articles (n = 22) were analyzed using a hierarchical summary receiver operating characteristics (HSROC) model. ADOS-2 performance was stronger than the ADI-R. ADOS-2 sensitivity and specificity ranged from .89-.92 and .81-.85, respectively. ADOS-2 accuracy in research compared with clinical settings was mixed. ADI-R sensitivity and specificity were .75 and .82, respectively, with higher specificity in research samples (Research = .85, Clinical = .72). A small number of clinical studies were identified, indicating ongoing need for investigation outside research settings.

Predicting diagnostic outcome in adult autism spectrum disorder using the autism diagnostic observation schedule, second edition

BACKGROUND

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) module four assessment for diagnosing autism spectrum disorder in adults has shown good sensitivity and specificity in research settings.

METHOD

This study aimed to evaluate the predictive accuracy of the ADOS-2 module four by investigating the components of the assessment, in relation to diagnostic outcome in a clinical setting. Data from 88 service users referred to a Specialist Adult Autism Service was explored.

RESULTS

ADOS-2 scores failed to predict the diagnostic outcome (overall sensitivity = 92%, specificity = 57%). Interestingly, scores from the 'restricted interests' component of the ADOS-2 have the potential to predict diagnostic outcome, despite this domain not been included in the scoring algorithm.

CONCLUSIONS

Based on our findings, we recommend clinicians are cautious when interpreting results of the ADOS-2 assessment.

Autism screening and conditional cash transfers in Chile: Using the Quantitative Checklist (Q-CHAT) for early autism detection in a low resource setting

LAY ABSTRACT

Getting a diagnosis of autism can take long, because autism is different across people, but also because it depends on the way it gets diagnosed. This is especially important in poorer countries or in the case of poor people living in wealthier countries that have significant groups of disadvantaged communities. We adapted a 10-item version of the Q-CHAT-25 questionnaire for use in routine health check-ups programme in Chile and recruited 287 participants under the age of three divided into three groups: Controls (125), Developmental Delay (149) and Autism Spectrum Condition (13). Our results show that a short questionnaire for autism screening can be successfully applied in a health-check programme in poor resource settings. Our results show that our questionnaire had good overall performance, not different to its longer version, the Q-CHAT-25. Our questionnaire was autism specific, with good sensitivity and reliability, and is suitable to be used in a screening setting. This study provides evidence that the implementation of Autism Spectrum Condition screening programmes using the Q-CHAT-10 provides value for money and improves diagnosis of Autism Spectrum Condition in those participating in routine health check-up programmes in developing countries or poor areas of wealthy countries.

Improving the Diagnosis of Autism Spectrum Disorder in Fragile X Syndrome by Adapting the Social Communication Questionnaire and the Social Responsiveness Scale-2

We carried out a psychometric assessment of the Social Communication Questionnaire (SCQ) and the Social Responsiveness Scale (SRS-2) in fragile X syndrome (FXS), relative to clinician DSM5-based diagnosis of autism spectrum disorder (ASD) in FXS. This was followed by instrument revisions that included: removal of non-discriminating and/or low face validity items for FXS; use of receiver operating characteristic (ROC) curves to determine optimal cut points for the original and revised measures; an exploratory factor analysis to outline subscales better representing ASD in FXS; and creation of a "triple criteria" diagnosis to better delineate ASD subgroups in FXS. These methods improved the sensitivity and/or specificity of the SCQ and SRS-2, but diagnostic accuracy of ASD remains problematic in FXS.

A Pilot Investigation of the Social Attention and Communication Surveillance (SACS) Tool for the Early Identification of Autism in Tianjin, China (SACS-C)

Introduction: Autism spectrum disorder (ASD) comprises difficulties in social communication and restrictive and repetitive behaviors. Despite an increased global prevalence, little remains known about early detection and diagnosis of autism in Mainland China. Our aim was to conduct a pilot investigation of the implementation of an Australian tool, Social Attention and Communication Surveillance (SACS), in Tianjin, China (SACS-C) by trained professionals to identify autism early compared to the Checklist for Autism in Toddlers-23 (CHAT-23) completed by parents and professionals. Materials and Methods: A total of 10,514 children were monitored across 61 Community Health Service Centres in six Tianjin districts on the SACS-C at 12, 18, and 24 months of age following a half-day training of 225 child health practitioners. Children deemed at "high likelihood" for autism on either the SACS, CHAT-23, or both, were referred for developmental assessments at the Tianjin Women and Children's Health Centre (TWCHC). Results: A total of 87 children (0.8%) were identified at "high likelihood" on the SACS-C, of whom 57 (66%) were assessed for autism; 24 children were subsequently diagnosed with autism (42.1%), and the remaining 33 (57.9%) were diagnosed with developmental and/or language delays. The SACS-C had a positive predictive value (PPV) of 42.1%, a negative predictive value (NPV) of 99.8%, and sensitivity and specificity of 53.3 and 99.7%, respectively. Only 21 children were identified at "high risk" for autism on the CHAT-23 (0.2%), over four times fewer children than the SACS-C, with 14 children assessed for autism (66%); nine were diagnosed with autism (64.3%) and the remaining five children were diagnosed with developmental and/or language delays. The CHAT-23 had an overall PPV of 64.3%, NPV of 99.6%, sensitivity of 27.3%, and specificity of 99.9%. Conclusion: This was the first large-scale study identifying autism in 12-24-month-old children in China. We ascertained the feasibility of training community health practitioners to monitor infants and toddlers for the early signs of autism, and determined the effectiveness of their use of SACS-C which had a better balance between accuracy and sensitivity in detecting autism in contrast to the CHAT-23 which missed the majority of children with autism (72.7%) vs. the SACS-C (46.7%). Given the emphasis on identifying as many children with autism as possible in Mainland China, SACS-C was identified as the tool of choice by the TWCHC. However, more work is needed to improve the psychometric properties in using the SACS-C in Mainland China so that it is comparable to its use in Australia.

Utility of the ADOS-2 in children with psychiatric disorders

The Autism Diagnostic Observation Schedule (ADOS-2) is commonly used in the diagnosis of autism spectrum disorder (ASD). Although it has demonstrated good sensitivity and specificity in research settings, relatively little is known about its utility and accuracy in children and adolescents with co-occurring psychiatric disorders. We investigated this topic in children with acute psychiatric disorders. Our sample consisted of 58 patients, aged 9-18 years, admitted to a child and adolescent psychiatric inpatient unit with a suspected diagnosis of ASD. Both Modules 3 and 4 demonstrated low sensitivity (Module 3: 58.3%; Module 4: 55.6%) and specificity (Module 3: 56.5%; Module 4: 59.5%). These findings suggest that the ADOS-2 should be interpreted with caution while screening for autism in children with complex psychiatric disorders.

Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis.

Autism spectrum heterogeneity: fact or artifact?

The current diagnostic practices are linked to a 20-fold increase in the reported prevalence of ASD over the last 30 years. Fragmenting the autism phenotype into dimensional "autistic traits" results in the alleged recognition of autism-like symptoms in any psychiatric or neurodevelopemental condition and in individuals decreasingly distant from the typical population, and prematurely dismisses the relevance of a diagnostic threshold. Non-specific socio-communicative and repetitive DSM 5 criteria, combined with four quantitative specifiers as well as all their possible combinations, render limitless variety of presentations consistent with the categorical diagnosis of ASD. We propose several remedies to this problem: maintain a line of research on prototypical autism; limit the heterogeneity compatible with a categorical diagnosis to situations with a phenotypic overlap and a validated etiological link with prototypical autism; reintroduce the qualitative properties of autism presentations and of current dimensional specifiers, language, intelligence, comorbidity, and severity in the criteria used to diagnose autism in replacement of quantitative "social" and "repetitive" criteria; use these qualitative features combined with the clinical intuition of experts and machine-learning algorithms to differentiate coherent subgroups in today's autism spectrum; study these subgroups separately, and then compare them; and question the autistic nature of "autistic traits".

An inquiry into how parents of children with autism spectrum disorder interact with their children in a motor skill-based play setting

BACKGROUND

Most studies examining parental behaviors of parents of young children with autism spectrum disorder (ASD) and typically developing (TD) children have taken place in free play settings and have primarily focused on examining social behaviors. Motor skill-based play settings, which are distinctly different from free play settings, have not been explicitly studied as it relates to parental behaviors in these environments.

AIMS

The purpose of this study was to examine parental behaviors of parents of children with and without ASD in two distinctly different play settings.

METHODS

Parental behaviors of eighteen parents of children with (n = 9) and without ASD (n = 9) were examined by observation in different play settings (free play [a social-play/traditional play based setting] and a motor skill-based play setting). The examined parental behaviors included parental encouragement, negativity, sensitivity, detachment, and intrusiveness. A 2 × 2 (group × play setting) repeated measures of ANOVA was conducted to examine the main effect of group (TD vs ASD) and play setting (a social-play based setting and a motor skill-based setting) and the interaction effect between group and play setting on parental behaviors. Post-hoc independent t-tests between groups in each setting were conducted to follow-up on significant interactions indicated in the repeated measures of ANOVA RESULTS: The repeated measures ANOVA revealed that parental encouragement showed a significant interaction effect, suggesting that the effect of group on parental encouragement depended on play setting. A post-hoc analysis revealed that parents of children with ASD showed statistically significant lower parental encouragement in a motor skill-based play setting but not in a social-play based setting compared to parents of TD children. Moreover, there was a main effect of group (parents of children with ASD vs. parents of TD children) on parental intrusiveness indicating that the mean parental intrusiveness on children with ASD was significantly higher than parents of TD children across both play settings. There were no statistically significant main or interaction effects on the other parental behaviors (parental negativity, sensitivity, and detachment) between groups.

CONCLUSIONS AND IMPLICATIONS

The current study indicated parental encouragement differences between parents of children with ASD and parents of TD children varied based on the play setting. These results identify a need to examine parental behaviors, especially parental encouragement and parental intrusiveness across various types of play settings. Furthermore, as parents of children with ASD displayed lower parental encouragement in a motor skill-based play setting compared to parents of TD children, future studies are warranted to improve parental encouragement of parents of children with ASD in a motor skill-based play setting.

Sex/Gender Differences in Screening for Autism Spectrum Disorder: Implications for Evidence-Based Assessment

Autism spectrum disorder (ASD) is diagnosed more often in boys than in girls; however, little is known about the nature of this sex/gender discrepancy or how it relates to diagnostic assessment practices. This study examined the performance of the Social Communication Questionnaire (SCQ) in screening for ASD among boys and girls. Data were drawn from the South Carolina Children's Educational Surveillance Study, a population-based study of ASD prevalence among children 8-10 years of age. Analyses were conducted using SCQ data from 3,520 children, with direct assessment data from 272 with elevated SCQ scores. A bifactor model based on the Diagnostic and Statistical Manual of Mental Disorders's (5th ed.) two ASD symptom domains fit the data well and performed slightly better for girls. In the general population sample, girls exhibited fewer social communication/interaction and restricted-repetitive behavior symptoms than boys. In the direct assessment sample, however, girls with ASD showed greater impairment in social communication/interaction than boys with ASD. Items pertaining to social communication/interaction problems at ages 4-5 were among the most diagnostically efficient overall and particularly for girls. Similarly, receiver operating characteristic analyses suggested that the SCQ performs adequately among boys and well among girls. Results support the use of the SCQ in screening for ASD but do not indicate sex/gender-specific cutoffs. Girls with ASD may exhibit pronounced intraindividual deficits in social communication/interaction compared to male peers with ASD and female peers without ASD. Although more research is needed, careful attention to social communication/interaction deficits around 4-5 years of age may be especially useful for assessing ASD in girls.

Temporal Changes in Effect Sizes of Studies Comparing Individuals With and Without Autism: A Meta-analysis

Importance

The definition and nature of autism have been highly debated, as exemplified by several revisions of the DSM (DSM-III, DSM-IIIR, DSM-IV, and DSM-5) criteria. There has recently been a move from a categorical view toward a spectrum-based view. These changes have been accompanied by a steady increase in the prevalence of the condition. Changes in the definition of autism that may increase heterogeneity could affect the results of autism research; specifically, a broadening of the population with autism could result in decreasing effect sizes of group comparison studies.

Objective

To examine the correlation between publication year and effect size of autism-control group comparisons across several domains of published autism neurocognitive research.

Data Sources

This meta-analysis investigated 11 meta-analyses obtained through a systematic search of PubMed for meta-analyses published from January 1, 1966, through January 27, 2019, using the search string autism AND (meta-analysis OR meta-analytic). The last search was conducted on January 27, 2019.

Study Selection

Meta-analyses were included if they tested the significance of group differences between individuals with autism and control individuals on a neurocognitive construct. Meta-analyses were only included if the tested group difference was significant and included data with a span of at least 15 years.

Data Extraction and Synthesis

Data were extracted and analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline using fixed-effects models.

Main Outcomes and Measures

Estimated slope of the correlation between publication year and effect size, controlling for differences in methods, sample size, and study quality.

Results

The 11 meta-analyses included data from a total of 27 723 individuals. Demographic data such as sex and age were not available for the entire data set. Seven different psychological and neurologic constructs were analyzed based on data from these meta-analyses. Downward temporal trends for effect size were found for all constructs (slopes: -0.067 to -0.003), with the trend being significant in 5 of 7 cases: emotion recognition (slope: -0.028 [95% CI, -0.048 to -0.007]), theory of mind (-0.045 [95% CI, -0.066 to -0.024]), planning (-0.067 [95% CI, -0.125 to -0.009]), P3b amplitude (-0.048 [95% CI, -0.093 to -0.004]), and brain size (-0.047 [95% CI, -0.077 to -0.016]). In contrast, 3 analogous constructs in schizophrenia, a condition that is also heterogeneous but with no reported increase in prevalence, did not show a similar trend.

Conclusions and Relevance

The findings suggest that differences between individuals with autism and those without the diagnosis have decreased over time and that possible changes in the definition of autism from a narrowly defined and homogenous population toward an inclusive and heterogeneous population may reduce our capacity to build mechanistic models of the condition.

Exploring diagnostic validity of the autism diagnostic observation schedule-2 in South Korean toddlers and preschoolers

This study aims to provide the initial validity of the Autism Diagnostic Observation Schedule-2 (ADOS-2) Toddler Module and Module 1-2 for South Korean toddlers and preschoolers. Based on 143 children, the ASD group (n = 68) showed significantly higher ADOS-2 item and algorithm total scores as well as social affect and repetitive and restricted behaviors domain scores compared with children with nonspectrum (NS; n = 42) disorders and typically developing (TD; n = 33) children. Using lower algorithm cutoffs, sensitivities were excellent for the ASD versus NS/TD comparisons, ranging from 94% to 100% across different Modules. Specificities varied more, ranging from 82% to 100%. Internal consistency was strong with high item-total correlations (r of 0.6-0.9) and Cronbach's Alphas (all above 0.7). Results demonstrated promising, initial evidence for the validity of the ADOS-2 for South Korean toddlers and preschoolers from 1 to 4 years of age. The ADOS-2 could be implemented, with minimal adaptations, in research and clinical settings in South Korea. This study is one of the first steps toward validating the ADOS-2 in other Eastern countries that are in great need for a valid instrument for the detection of ASD. Autism Res 2019, 12: 1356-1366. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Results of this study demonstrated promising, initial evidence for the validity of a gold standard measure for the diagnosis of autism, the Autism Diagnostic Observation Schedule-2 (ADOS-2), for South Korean toddlers and preschoolers. The ADOS-2 could be implemented, with minimal adaptations, in research and clinical settings in South Korea. This study is one of the first steps toward validating the ADOS-2 in other Eastern countries that are in great need of a valid instrument for the detection of ASD.

Beyond pass-fail: Examining the potential utility of two thresholds in the autism screening process

Access to early intervention as early in development as possible is critical to maximizing long-term outcomes for children with autism spectrum disorders (ASD). However, despite the fact that ASD can be reliably diagnosed by 24 months, the average age of diagnosis is 2 years later. Waitlists for specialized developmental evaluations are one barrier to early diagnosis. The purpose of this study was to examine one potential approach to reducing wait time for an ASD diagnostic evaluation by examining the utility of using more than one threshold for an autism screening tool, the Screening Tool for Autism in Toddlers and Young Children (STAT). Participants included 171 children between 24 and 36 months of age who received a medical diagnostic evaluation through Illinois' Early Intervention Program. This study directly compared the performance of the STAT when scored: (a) using the original single threshold, (b) using seven equally weighted items using a single threshold, and (c) using all items differentially weighted based on how strongly that item predicts a later ASD diagnosis. In addition, this study explored the potential utility of using two thresholds rather than a single threshold for each scoring method. Results of this study suggest that using a two-threshold logistic regression method has potential psychometric advantages over a single threshold and categorical scoring. Using this approach may reduce the wait time for specialty ASD diagnostic evaluations by maximizing true negatives and true positives, such that specialty evaluations may be reserved for those cases that are more ambiguous or more complex. Autism Research 2019, 12: 112-122. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: This study examined the benefits of using two versus one cutoff score when screening for autism. Results indicate that having two scores and weighting test items based on predictive association with an autism diagnosis is better than using a single score and weighting each item equally. Using such an approach may reduce the wait time for specialty autism diagnostic evaluations, such that specialty evaluations may be reserved for those cases that are more ambiguous or more complex.

Diagnostic accuracy of the ADOS and ADOS-2 in clinical practice

The Autism Diagnostic Observation Schedule is a semi-structured, standardized assessment tool for individuals with suspected autism spectrum disorders (ASD) and is deemed to be part of the gold standard for diagnostic evaluation. Good diagnostic accuracy and interpersonal objectivity have been demonstrated for the ADOS in research setting. The question arises whether this is also true for daily clinical practice and whether diagnostic accuracy depends on specialized experience in the diagnostic evaluation. The present study explores the diagnostic accuracy of the original and the revised version of the ADOS for Modules 1 through 4. Thus, seven cases of ADOS executions were recorded and coded by a group of experts of specialized outpatient clinics for ASD. In an extensive consensus process, including video analysis of every minute of the ADOS executions, a "gold standard" coding for every case was defined. The videos of the ADOS administration were presented to a large group of clinicians (from daily clinical routine care) and their codings (n = 189) were obtained and analysed. Variance of coding and congruence with the expert coding were determined. High variance was found in the codings. The accuracy of the coding depends on the experience of the coder with the ADOS as well as on characteristics of the cases and the quality of the administration of the ADOS. Specialization in the diagnostic of ASD has to be claimed. Specialized outpatient clinics for ASD are required which guarantee a qualified diagnostic/differential diagnostic and case management with the aim of demand-oriented supply of individual cases.

Exploring autism symptoms in an Australian cohort of patients with Prader-Willi and Angelman syndromes

Background

Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are neurodevelopmental disorders that are caused by abnormal expression of imprinted genes in the 15q11-13 region. Dysregulation of genes located in this region has been proposed as a susceptibility factor for autism spectrum disorder (ASD) in both disorders.

Methods

This study aimed to explore symptoms of ASD in 25 PWS and 19 AS individuals aged between 1 and 39 years via objective assessment. Participants completed the Autism Diagnostic Observation Schedule-2nd Edition (ADOS-2) and a developmentally or age-appropriate intellectual functioning assessment. All participants had their genetic diagnosis confirmed using DNA methylation analysis and microarray testing of copy number changes within the 15q11-13 region.

Results

Participants with PWS had significantly higher overall and social affect calibrated severity scores (CSS) on the ADOS-2 compared to AS participants (p = .0055 and .0015, respectively), but the two groups did not differ significantly on CSS for the repetitive and restricted behaviour domain.

Conclusions

PWS cases presented with greater symptoms associated with ASD compared to individuals with AS. Mental health issues associated with PWS may contribute to elevated symptoms of ASD, particularly in adolescents and adults with PWS.

Electronic supplementary material

The online version of this article (10.1186/s11689-018-9242-0) contains supplementary material, which is available to authorized users.

Diagnostic tests for autism spectrum disorder (ASD) in preschool children

BACKGROUND

Autism spectrum disorder (ASD) is a behaviourally diagnosed condition. It is defined by impairments in social communication or the presence of restricted or repetitive behaviours, or both. Diagnosis is made according to existing classification systems. In recent years, especially following publication of the Diagnostic and Statistical Manual of Mental Disorders - Fifth Edition (DSM-5; APA 2013), children are given the diagnosis of ASD, rather than subclassifications of the spectrum such as autistic disorder, Asperger syndrome, or pervasive developmental disorder - not otherwise specified. Tests to diagnose ASD have been developed using parent or carer interview, child observation, or a combination of both.

OBJECTIVES

Primary objectives1. To identify which diagnostic tools, including updated versions, most accurately diagnose ASD in preschool children when compared with multi-disciplinary team clinical judgement.2. To identify how the best of the interview tools compare with CARS, then how CARS compares with ADOS.a. Which ASD diagnostic tool - among ADOS, ADI-R, CARS, DISCO, GARS, and 3di - has the best diagnostic test accuracy?b. Is the diagnostic test accuracy of any one test sufficient for that test to be suitable as a sole assessment tool for preschool children?c. Is there any combination of tests that, if offered in sequence, would provide suitable diagnostic test accuracy and enhance test efficiency?d. If data are available, does the combination of an interview tool with a structured observation test have better diagnostic test accuracy (i.e. fewer false-positives and fewer false-negatives) than either test alone?As only one interview tool was identified, we modified the first three aims to a single aim (Differences between protocol and review): This Review evaluated diagnostic tests in terms of sensitivity and specificity. Specificity is the most important factor for diagnosis; however, both sensitivity and specificity are of interest in this Review because there is an inherent trade-off between these two factors.Secondary objectives1. To determine whether any diagnostic test has greater diagnostic test accuracy for age-specific subgroups within the preschool age range.

SEARCH METHODS

In July 2016, we searched CENTRAL, MEDLINE, Embase, PsycINFO, 10 other databases, and the reference lists of all included publications.

SELECTION CRITERIA

Publications had to: 1. report diagnostic test accuracy for any of the following six included diagnostic tools: Autism Diagnostic Interview - Revised (ADI-R), Gilliam Autism Rating Scale (GARS), Diagnostic Interview for Social and Communication Disorder (DISCO), Developmental, Dimensional, and Diagnostic Interview (3di), Autism Diagnostic Observation Schedule - Generic (ADOS), and Childhood Autism Rating Scale (CARS); 2. include children of preschool age (under six years of age) suspected of having an ASD; and 3. have a multi-disciplinary assessment, or similar, as the reference standard.Eligible studies included cohort, cross-sectional, randomised test accuracy, and case-control studies. The target condition was ASD.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed all studies for inclusion and extracted data using standardised forms. A third review author settled disagreements. We assessed methodological quality using the QUADAS-2 instrument (Quality Assessment of Studies of Diagnostic Accuracy - Revised). We conducted separate univariate random-effects logistical regressions for sensitivity and specificity for CARS and ADI-R. We conducted meta-analyses of pairs of sensitivity and specificity using bivariate random-effects methods for ADOS.

MAIN RESULTS

In this Review, we included 21 sets of analyses reporting different tools or cohorts of children from 13 publications, many with high risk of bias or potential conflicts of interest or a combination of both. Overall, the prevalence of ASD for children in the included analyses was 74%.For versions and modules of ADOS, there were 12 analyses with 1625 children. Sensitivity of ADOS ranged from 0.76 to 0.98, and specificity ranged from 0.20 to 1.00. The summary sensitivity was 0.94 (95% confidence interval (CI) 0.89 to 0.97), and the summary specificity was 0.80 (95% CI 0.68 to 0.88).For CARS, there were four analyses with 641 children. Sensitivity of CARS ranged from 0.66 to 0.89, and specificity ranged from 0.21 to 1.00. The summary sensitivity for CARS was 0.80 (95% CI 0.61 to 0.91), and the summary specificity was 0.88 (95% CI 0.64 to 0.96).For ADI-R, there were five analyses with 634 children. Sensitivity for ADI-R ranged from 0.19 to 0.75, and specificity ranged from 0.63 to 1.00. The summary sensitivity for the ADI-R was 0.52 (95% CI 0.32 to 0.71), and the summary specificity was 0.84 (95% CI 0.61 to 0.95).Studies that compared tests were few and too small to allow clear conclusions.In two studies that included analyses for both ADI-R and ADOS, tests scored similarly for sensitivity, but ADOS scored higher for specificity. In two studies that included analyses for ADI-R, ADOS, and CARS, ADOS had the highest sensitivity and CARS the highest specificity.In one study that explored individual and additive sensitivity and specificity of ADOS and ADI-R, combining the two tests did not increase the sensitivity nor the specificity of ADOS used alone.Performance for all tests was lower when we excluded studies at high risk of bias.

AUTHORS' CONCLUSIONS

We observed substantial variation in sensitivity and specificity of all tests, which was likely attributable to methodological differences and variations in the clinical characteristics of populations recruited.When we compared summary statistics for ADOS, CARS, and ADI-R, we found that ADOS was most sensitive. All tools performed similarly for specificity. In lower prevalence populations, the risk of falsely identifying children who do not have ASD would be higher.Now available are new versions of tools that require diagnostic test accuracy assessment, ideally in clinically relevant situations, with methods at low risk of bias and in children of varying abilities.

Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism

Background

Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the Autism Diagnostic Observation Schedule (ADOS). Shorter and potentially mobilized approaches would help to alleviate bottlenecks in the healthcare system. Previous work using machine learning suggested that a subset of the behaviors measured by ADOS can achieve clinically acceptable levels of accuracy. Here we expand on this initial work to build sparse models that have higher potential to generalize to the clinical population.

Methods

We assembled a collection of score sheets for two ADOS modules, one for children with phrased speech (Module 2; 1319 ASD cases, 70 controls) and the other for children with verbal fluency (Module 3; 2870 ASD cases, 273 controls). We used sparsity/parsimony enforcing regularization techniques in a nested cross validation grid search to select features for 17 unique supervised learning models, encoding missing values as additional indicator features. We augmented our feature sets with gender and age to train minimal and interpretable classifiers capable of robust detection of ASD from non-ASD.

Results

By applying 17 unique supervised learning methods across 5 classification families tuned for sparse use of features and to be within 1 standard error of the optimal model, we find reduced sets of 10 and 5 features used in a majority of models. We tested the performance of the most interpretable of these sparse models, including Logistic Regression with L2 regularization or Linear SVM with L1 regularization. We obtained an area under the ROC curve of 0.95 for ADOS Module 3 and 0.93 for ADOS Module 2 with less than or equal to 10 features.

Conclusions

The resulting models provide improved stability over previous machine learning efforts to minimize the time complexity of autism detection due to regularization and a small parameter space. These robustness techniques yield classifiers that are sparse, interpretable and that have potential to generalize to alternative modes of autism screening, diagnosis and monitoring, possibly including analysis of short home videos.