Short report: Transition to International Classification of Diseases, 10th Revision and the prevalence of autism in a cohort of healthcare systems

LAY ABSTRACT

Currently, the prevalence of autism spectrum disorder (henceforth "autism") is 1 in 36, an increasing trend from previous estimates. In 2015, the United States adopted a new version (International Classification of Diseases, 10th Revision) of the World Health Organization coding system, a standard for classifying medical conditions. Our goal was to examine how the transition to this new coding system impacted autism diagnoses in 10 healthcare systems. We obtained information from electronic medical records and insurance claims data from July 2014 through December 2016 for each healthcare system. We used member enrollment data for 30 consecutive months to observe changes 15 months before and after adoption of the new coding system. Overall, the rates of autism per 1000 enrolled members was increasing for 0- to 5-year-olds before transition to International Classification of Diseases, 10th Revision and did not substantively change after the new coding was in place. There was variation observed in autism diagnoses before and after transition to International Classification of Diseases, 10th Revision for other age groups. The change to the new coding system did not meaningfully affect autism rates at the participating healthcare systems. The increase observed among 0- to 5-year-olds is likely indicative of an ongoing trend related to increases in screening for autism rather than a shift associated with the new coding.

Comparison of 2 Case Definitions for Ascertaining the Prevalence of Autism Spectrum Disorder Among 8-Year-Old Children

The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year-old children in multiple US communities. From 2000 to 2016, investigators at ADDM Network sites classified ASD from collected text descriptions of behaviors from medical and educational evaluations which were reviewed and coded by ADDM Network clinicians. It took at least 4 years to publish data from a given surveillance year. In 2018, we developed an alternative case definition utilizing ASD diagnoses or classifications made by community professionals. Using data from surveillance years 2014 and 2016, we compared the new and previous ASD case definitions. Compared with the prevalence based on the previous case definition, the prevalence based on the new case definition was similar for 2014 and slightly lower for 2016. Sex and race/ethnicity prevalence ratios were nearly unchanged. Compared with the previous case definition, the new case definition's sensitivity was 86% and its positive predictive value was 89%. The new case definition does not require clinical review and collects about half as much data, yielding more timely reporting. It also more directly measures community identification of ASD, thus allowing for more valid comparisons among communities, and reduces resource requirements while retaining measurement properties similar to those of the previous definition.

The DSM-5 Clinical and Public Health Committee (CPHC): operations, mechanics, controversies and recommendations

BACKGROUND

For DSM - 5, the American Psychiatric Association Board of Trustees established a robust vetting and review process that included two review committees that did not exist in the development of prior DSMs, the Scientific Review Committee (SRC) and the Clinical and Public Health Committee (CPHC). The CPHC was created as a body that could independently review the clinical and public health merits of various proposals that would fall outside of the strictly defined scientific process.

METHODS

This article describes the principles and issues which led to the creation of the CPHC, the composition and vetting of the committee, and the processes developed by the committee - including the use of external reviewers.

RESULTS

Outcomes of some of the more involved CPHC deliberations, specifically, decisions concerning elements of diagnoses for major depressive disorder, autism spectrum disorder, catatonia, and substance use disorders, are described. The Committee's extensive reviews and its recommendations regarding Personality Disorders are also discussed.

CONCLUSIONS

On the basis of our experiences, the CPHC membership unanimously believes that external review processes to evaluate and respond to Work Group proposals is essential for future DSM efforts. The Committee also recommends that separate SRC and CPHC committees be appointed to assess proposals for scientific merit and for clinical and public health utility and impact.

Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network

With the increasing prevalence of autism spectrum disorder (ASD), it is important to identify ASD patients for effective treatment and intervention, especially in early childhood. Neuroimaging techniques have been used to characterize the complex biomarkers based on the functional connectivity anomalies in the ASD. However, the diagnosis of ASD still adopts the symptom-based criteria by clinical observation. The existing computational models tend to achieve unreliable diagnostic classification on the large-scale aggregated data sets. In this work, we propose a novel graph-based classification model using the deep belief network (DBN) and the Autism Brain Imaging Data Exchange (ABIDE) database, which is a worldwide multisite functional and structural brain imaging data aggregation. The remarkable connectivity features are selected through a graph extension of K -nearest neighbors and then refined by a restricted path-based depth-first search algorithm. Thanks to the feature reduction, lower computational complexity could contribute to the shortening of the training time. The automatic hyperparameter-tuning technique is introduced to optimize the hyperparameters of the DBN by exploring the potential parameter space. The simulation experiments demonstrate the superior performance of our model, which is 6.4% higher than the best result reported on the ABIDE database. We also propose to use the data augmentation and the oversampling technique to identify further the possible subtypes within the ASD. The interpretability of our model enables the identification of the most remarkable autistic neural correlation patterns from the data-driven outcomes.

Motor and sensory features successfully decode autism spectrum disorder and combine with the original RDoC framework to boost diagnostic classification

Sensory processing and motor coordination atypicalities are not commonly identified as primary characteristics of autism spectrum disorder (ASD), nor are they well captured in the NIMH's original Research Domain Criteria (RDoC) framework. Here, motor and sensory features performed similarly to RDoC features in support vector classification of 30 ASD youth against 33 typically developing controls. Combining sensory with RDoC features boosted classification performance, achieving a Matthews Correlation Coefficient (MCC) of 0.949 and balanced accuracy (BAcc) of 0.971 (p = 0.00020, calculated against a permuted null distribution). Sensory features alone successfully classified ASD (MCC = 0.565, BAcc = 0.773, p = 0.0222) against a clinically relevant control group of 26 youth with Developmental Coordination Disorder (DCD) and were in fact required to decode against DCD above chance. These findings highlight the importance of sensory and motor features to the ASD phenotype and their relevance to the RDoC framework.

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

Annual Research Review: Looking back to look forward - changes in the concept of autism and implications for future research

The concept of autism is a significant contribution from child psychiatry that has entered wider culture and public consciousness, and has evolved significantly over the last four decades. Taking a rather personal retrospective, reflecting on our own time in autism research, this review explores changes in the concept of autism and the implications of these for future research. We focus on seven major changes in how autism is thought of, operationalised, and recognised: (1) from a narrow definition to wide diagnostic criteria; (2) from a rare to a relatively common condition, although probably still under-recognised in women; (3) from something affecting children, to a lifelong condition; (4) from something discreet and distinct, to a dimensional view; (5) from one thing to many 'autisms', and a compound or 'fractionable' condition; (6) from a focus on 'pure' autism, to recognition that complexity and comorbidity is the norm; and finally, (7) from conceptualising autism purely as a 'developmental disorder', to recognising a neurodiversity perspective, operationalised in participatory research models. We conclude with some challenges for the field and suggestions for areas currently neglected in autism research.

[From autism spectrum to autism constellation]

Research on autism and mental disorders has been unsuccessful over the past few decades, as can be inferred from the poor results related to advances in other diseases. It is concerning that, after more than a half century of research based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), no biological markers have been found to prove the validity of the DSM mental disorders. Criticisms to DSM have been focused mainly on the categorical conceptualization, false comorbidity and the polythetic nature of diagnostic criteria. The lack of validity of the DSM model requests for a change in research designs, in order to overcome the problems derived from a paradigm that has stopped to be productive. In the field of clinical practice, it is even more pressing a change of mindset in order to incorporate the heterogeneity of endophenotypes that overflows the classification of the DSM, to adopt a dimensional perspective of mental problems and to develop an alternative interpretation for comorbidity. Related to research are suggested designs based on Domain Research Criteria and a multifactorial analysis with very large samples (big data). For clinical practice it is suggested a dimensional approach based on the specificities of each person with autism.

A network clustering based feature selection strategy for classifying autism spectrum disorder

BACKGROUND

Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance.

METHODS

In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification.

RESULTS

The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network.

CONCLUSION

It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

Adaptation of Diagnosis from Autism Spectrum Disorder to Social Communication Disorder in Adolescents with ADHD

Here, we describe a case in which an original diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) and Asperger's syndrome was later adapted to social communication disorder, to meet the new guidelines. First, separate diagnostic labels of autism disorder, Asperger's disorder, and PDD-NOS have been replaced by one umbrella term "autism spectrum disorder". Second, the new DSM-5 criteria are more stringent than the old criteria. For example, observation of a higher number of symptoms is necessary to meet the criteria, such as restricted interests and repetitive behaviors. Third, the communication and social interaction domains are combined into one, titled "social/communication deficits." Finally, requirement of a delay in language development is no longer necessary to establish a diagnosis of autism.

Diagnostic Accuracy of Indian Scale for Assessment of Autism in Indian Children Aged 2-5 Years

OBJECTIVE

To determine the diagnostic accuracy of Indian Scale for Assessment of Autism (ISAA) in children aged between 2-5 years. Design: Setting:.

STUDY DESIGN

Study of diagnostic accuracy.

PARTICIPANTS

A consecutive sample of 500 children with suspected Autism (delay or regression of developmental milestones, delay or regression in speech, age-inappropriate understanding, behaviour, play and/or social interaction) was recruited.

SETTING

Tertiary level hospital, (November 2015 - November 2017).

PROCEDURE

Each child underwent an expert comprehensive assessment of Autism (reference tool) that included history, observation, examination, diagnostic criteria for Autism Spectrum Disorder (ASD) of the Diagnostic and Statistical Manual of Mental Disorders', 5th edition, Childhood Autism Rating Scale-2 (CARS2), developmental status and adaptive function. This was followed by the administration of ISAA (test tool) in Hindi language. Parameters of diagnostic accuracy and Receiver Operating Characteristic curves were computed.

MAIN OUTCOME MEASURES

ASD based on (i) expert assessment, (ii) CARS-2, and (iii) ISAA.

RESULTS

In children aged 2-3 years, sensitivity of ISAA was 100% (95% CI 98.2% -100%), specificity 28.9% (95% CI 17.7% to 43.4%), positive likelihood ratio 1.4 and negative likelihood ratio 0. In 3-5 year olds, sensitivity was 99.6% (95% CI 97.6% to 99.6%), specificity 33.3% (95% CI 15.1% to 58.3%), positive likelihood ration 1.5 and negative likelihood ratio 0.01. The degrees of autism based on the existing cut off values were inaccurate.

CONCLUSIONS

ISAA has sub-optimal performance in diagnosing and assessing severity in 2-5 year old children.

Big data approaches to decomposing heterogeneity across the autism spectrum

Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.

Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification

BACKGROUND

Over the recent years there has been a growing debate regarding the extent and nature of the overlap in neuropathology between schizophrenia (SZ) and autism spectrum disorder (ASD). Dynamic functional network connectivity (dFNC) is a recent analysis method that explores temporal patterns of functional connectivity (FC). We compared resting-state dFNC in SZ, ASD and healthy controls (HC), characterized the associations between temporal patterns and symptoms, and performed a three-way classification analysis based on dFNC indices.

METHODS

Resting-state fMRI was collected from 100 young adults: 33 SZ, 33 ASD, 34 HC. Independent component analysis (ICA) was performed, followed by dFNC analysis (window = 33 s, step = 1TR, k-means clustering). Temporal patterns were compared between groups, correlated with symptoms, and classified via cross-validated three-way discriminant analysis.

RESULTS

Both clinical groups displayed an increased fraction of time (FT) spent in a state of weak, intra-network connectivity [p < .001] and decreased FT in a highly-connected state [p < .001]. SZ further showed decreased number of transitions between states [p < .001], decreased FT in a widely-connected state [p < .001], increased dwell time (DT) in the weakly-connected state [p < .001], and decreased DT in the highly-connected state [p = .001]. Social behavior scores correlated with DT in the widely-connected state in SZ [r = 0.416, p = .043], but not ASD. Classification correctly identified SZ at high rates (81.8%), while ASD and HC at lower rates.

CONCLUSIONS

Results indicate a severe and pervasive pattern of temporal aberrations in SZ (specifically, being "stuck" in a state of weak connectivity), that distinguishes SZ participants from both ASD and HC, and is associated with clinical symptoms.

Nutrition, BMI and Motor Competence in Children with Autism Spectrum Disorder

Background and objectives: The purpose of this study was to examine the relationship between motor competence, body mass index (BMI), and nutrition knowledge in children with autism spectrum disorder (ASD). Materials and Methods: Fifty-one children with ASD (five females and 46 males) aged 7-12 participated in the study. The Movement Assessment Battery for Children-2 (MABC-2) was used to examine children's fine and gross motor skill competence; the nutrition knowledge survey assessed children's overall knowledge of food groups and healthful eating; and BMI-for-age determined their weight status. Descriptive analysis and Pearson correlation was used to analyze the relationship between nutrition knowledge, BMI, and motor competence in children with ASD. Results: The majority of children with ASD (82%) showed significant motor delays in MABC-2 assessments. The BMI-for-age percentile data suggested that 20% of participants were obese, 17% were overweight, and 12% were underweight. The nutrition knowledge data indicated that 55% of children scored below 70% on accuracy in the nutrition knowledge survey. Pearson correlation analysis revealed a significant positive relationship between MABC-2 manual dexterity and nutrition knowledge (r = 0.327, p < 0.01), and between MABC-2 balance skills and nutrition knowledge (r = 0.413, p < 0.01). A significant negative relationship was also found between BMI and MABC-2 balance skills (r = -0.325, p < 0.01). Conclusions: The findings of the study suggest that nutrition knowledge and motor competence may be key factors influencing BMI in children with ASD and therefore interventions tackling both sides of the energy balance equation are necessary.

Subtyping the Autism Spectrum Disorder: Comparison of Children with High Functioning Autism and Asperger Syndrome

Since Hans Asperger's first description (Arch Psych Nervenkrankh 117:76-136, 1944), through Lorna Wing's translation and definition (Psychol Med 11:115-129, 1981), to its introduction in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM, 1994), Asperger Syndrome has always aroused huge interest and debate, until vanishing in the DSM fifth edition (2013). The debate regarded its diagnostic validity and its differentiation from high functioning autism (HFA). The present study aimed to examine whether AS differed from HFA in clinical profiles and to analyze the impact of DSM-5's innovation. Differences in cognitive, language, school functioning and comorbidities, were revealed when 80 AS and 70 HFA patients (3-18 years) were compared. Results suggested that an AS empirical distinction within autism spectrum disorder should be clinically useful.

Autism Spectrum Disorder: Classification, diagnosis and therapy

Autism Spectrum Disorder (ASD) refers to a group of neurodevelopmental disorders including autism, Asperger's syndrome (AS) and pervasive developmental disorder-not otherwise specified (PDD-NOS). The new diagnostic criteria of ASD focuses on two core domains: social communication impairment and restricted interests/repetitive behaviors. The prevalence of ASD has been steadily increasing over the past two decades, with current estimates reaching up to 1 in 36 children. Hereditary factors, parental history of psychiatric disorders, pre-term births, and fetal exposure to psychotropic drugs or insecticides have all been linked to higher risk of ASD. Several scales such as the Childhood Autism Rating Scale (CARS), The Autism Spectrum Disorder-Observation for Children (ASD-OC), The Developmental, Dimensional, and Diagnostic Interview (3di), are available to aid in better assessing the behaviors and symptoms associated with ASD. Nearly 75% of ASD patients suffer from comorbid psychiatric illnesses or conditions, which may include attention-deficit hyperactivity disorder (ADHD), anxiety, bipolar disorder, depression, Tourette syndrome, and others. Both pharmacological and non-pharmacological interventions are available for ASD. Pharmacological treatments include psychostimulants, atypical antipsychotics, antidepressants, and alpha-2 adrenergic receptor agonists. These medications provide partial symptomatic relief of core symptoms of ASD or manage the symptoms of comorbid conditions. Non-pharmacological interventions, which show promising evidence in improving social interaction and verbal communication of ASD patients, include music therapy, cognitive behavioral therapy and social behavioral therapy. Hormonal therapies with oxytocyin or vasopressin receptor antagonists have also shown some promise in improving core ASD symptoms. The use of vitamins, herbal remedies and nutritional supplements in conjunction with pharmacological and behavioral treatment appear to have some effect in symptomatic improvement in ASD, though additional studies are needed to confirm these benefits. Developing novel disease-modifying therapies may prove to be the ultimate intervention for sustained improvement of symptoms in ASD.

Cluster analysis of autism spectrum disorder symptomatology: Qualitatively distinct subtypes or quantitative degrees of severity of a single disorder?

The decision to collapse several related disorders into a single diagnosis of Autism Spectrum Disorder (ASD) generated significant controversy and debate. There has been mixed evidence as to whether various ASD subtypes are qualitatively distinct or if they exist on a spectrum of symptom severity. The present study conducted a two-step cluster analysis of major ASD symptoms in a sample of 147 young males with ASD aged between 6yr and 18yr with IQ > 70. Results indicated that a two-cluster solution (high and low severity of ASD symptomatology) was reliable and valid. Further, the construct of challenging behaviour was not a necessary component of the two-cluster solution, verifying the new conceptualisation of ASD. Further replication of these findings with other subsets of individuals with ASD is needed.

Autism: a transdiagnostic, dimensional, construct of reasoning?

The concept of autism has changed across time, from the Bleulerian concept, which defined it as one of several symptoms of dementia praecox, to the present-day concept representing a pervasive development disorder. The present theoretical contribution to this special issue of EJN on autism introduces new theoretical ideas and discusses them in light of selected prior theories, clinical examples, and recent empirical evidence. The overall aim is to identify some present challenges of diagnostic practice and autism research and to suggest new pathways that may help direct future research. Future research must agree on the definitions of core concepts such as autism and psychosis. A possible redefinition of the concept of autism may be a condition in which the rationale of an individual's behaviour differs qualitatively from that of the social environment due to characteristic cognitive impairments affecting reasoning. A broad concept of psychosis could focus on deviances in the experience of reality resulting from impairments of reasoning. In this light and consistent with recent empirical evidence, it may be appropriate to redefine dementia praecox as a developmental disorder of reasoning. A future challenge of autism research may be to develop theoretical models that can account for the impact of complex processes acting at the social level in addition to complex neurobiological and psychological processes. Such models could profit from a distinction among processes related to (i) basic susceptibility, (ii) adaptive processes and (iii) decompensating factors involved in the development of manifest illness.

High efficiency classification of children with autism spectrum disorder

Autism spectrum disorder (ASD) is a wide-ranging collection of developmental diseases with varying symptoms and degrees of disability. Currently, ASD is diagnosed mainly with psychometric tools, often unable to provide an early and reliable diagnosis. Recently, biochemical methods are being explored as a means to meet the latter need. For example, an increased predisposition to ASD has been associated with abnormalities of metabolites in folate-dependent one carbon metabolism (FOCM) and transsulfuration (TS). Multiple metabolites in the FOCM/TS pathways have been measured, and statistical analysis tools employed to identify certain metabolites that are closely related to ASD. The prime difficulty in such biochemical studies comes from (i) inefficient determination of which metabolites are most important and (ii) understanding how these metabolites are collectively related to ASD. This paper presents a new method based on scores produced in Support Vector Machine (SVM) modeling combined with High Dimensional Model Representation (HDMR) sensitivity analysis. The new method effectively and efficiently identifies the key causative metabolites in FOCM/TS pathways, ranks their importance, and discovers their independent and correlative action patterns upon ASD. Such information is valuable not only for providing a foundation for a pathological interpretation but also for potentially providing an early, reliable diagnosis ideally leading to a subsequent comprehensive treatment of ASD. With only tens of SVM model runs, the new method can identify the combinations of the most important metabolites in the FOCM/TS pathways that lead to ASD. Previous efforts to find these metabolites required hundreds of thousands of model runs with the same data.

A Biomarker Characterizing Neurodevelopment with applications in Autism

Despite great advances in neuroscience and genetic studies, our understanding of neurodevelopmental disorders is still quite limited. An important reason is not having objective psychiatric clinical tests. Here we propose a quantitative neurodevelopment assessment by studying natural movement outputs. Movement is central to behaviors: It involves complex coordination, temporal alterations, and precise dynamic controls. We carefully analyzed the continuous movement output data, collected with high definition electromagnetic sensors at millisecond time scales. We unraveled new metrics containing striking physiological information that was unseen neither by using traditional motion assessments nor by naked eye observations. Our putative biomarker leads to precise individualized classifications. It illustrates clear differences between Autism Spectrum Disorder (ASD) subjects from mature typical developing (TD) individuals. It provides an ASD complementary quantitative classification, which closely agrees with the clinicaly assessed functioning levels in the spectrum. It also illustrates TD potential age-related neurodevelopmental trajectories. Applying our movement biomarker to the parents of the ASD individuals studied in the cohort also shows a novel potential familial signature ASD tie. This paper proposes a putative behavioral biomarker to characterize the level of neurodevelopment with high predicting power, as illustrated in ASD subjects as an example.

Issues in Identification and Assessment of Children with Autism and a Proposed Resource Toolkit for Speech-Language Pathologists

BACKGROUND

The prevalence of autism spectrum disorder (ASD) has increased significantly in the last decade as have treatment choices. Nonetheless, the vastly diverse autism topic includes issues related to naming, description, iden-tification, assessment, and differentiation from other neu-rodevelopmental conditions. ASD issues directly impact speech-language pathologists (SLPs) who often see these children as the second contact, after pediatric medical practitioners. Because of shared symptomology, differentiation among neurodevelopmental disorders is crucial as it impacts treatment, educational choices, and the performance trajectory of affected children.

OBJECTIVES

To highlight issues in: identification and differentiation of ASD from other communication and language challenges, the prevalence differences between ASD gender phenotypes, and the insufficient consideration of cultural factors in evaluating ASD in children. A second objective was to propose a tool to assist SLPs in the management of autism in children.

SUMMARY

A universal resource toolkit development project for SLP communities at large is proposed. The resource is comprised of research-based observation and screening tools for caregivers and educators, as well as parent questionnaires for portraying the children's function in the family, cultural com-munity, and educational setting.

Demographic and Cognitive Profile of Individuals Seeking a Diagnosis of Autism Spectrum Disorder in Adulthood

Little is known about ageing with autism spectrum disorder (ASD). We examined the characteristics of adults referred to a specialist diagnostic centre for assessment of possible ASD, 100 of whom received an ASD diagnosis and 46 did not. Few demographic differences were noted between the groups. Comorbid psychiatric disorders were high in individuals with ASD (58 %) and non-ASD (59 %). Individuals who received an ASD diagnosis had higher self-rated severity of ASD traits than non-ASD individuals. Within the ASD group, older age was associated with higher ratings of ASD traits and better cognitive performance. One interpretation is that general cognitive ability and the development of coping strategies across the lifespan, do not necessarily reduce ASD traits but may mitigate their effects.

The Latent Structure of Autistic Traits: A Taxometric, Latent Class and Latent Profile Analysis of the Adult Autism Spectrum Quotient

Autistic traits are widely thought to operate along a continuum. A taxometric analysis of Adult Autism Spectrum Quotient data was conducted to test this assumption, finding little support but identifying a high severity taxon. To understand this further, latent class and latent profile models were estimated that indicated the presence of six distinct subtypes: one with little probability of endorsing any autistic traits, one engaging in ‘systemising’ behaviours, three groups endorsing multiple components of Wing and Gould’s autistic triad, and a group similar in size and profile to the taxon previously identified. These analyses suggest the AQ (and potentially by extension autistic traits) have a categorical structure. These findings have important implications for the analysis and interpretation of AQ data.

Multiple functional networks modeling for autism spectrum disorder diagnosis

Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way. In this article, we propose a novel framework to model multiple networks of rsfMRI with data-driven approaches. Specifically, we construct large-scale functional networks with hierarchical clustering and find discriminative connectivity patterns between ASD and normal controls (NC). We then learn features and classifiers for each cluster through discriminative restricted Boltzmann machines (DRBMs). In the testing phase, each DRBM determines whether a test sample is ASD or NC, based on which we make a final decision with a majority voting strategy. We assess the diagnostic performance of the proposed method using public datasets and describe the effectiveness of our method by comparing it to competing methods. We also rigorously analyze FCs learned by DRBMs on each cluster and discover dominant FCs that play a major role in discriminating between ASD and NC. Hum Brain Mapp 38:5804-5821, 2017. © 2017 Wiley Periodicals, Inc.

The EU-AIMS Longitudinal European Autism Project (LEAP): clinical characterisation

BACKGROUND

The EU-AIMS Longitudinal European Autism Project (LEAP) is to date the largest multi-centre, multi-disciplinary observational study on biomarkers for autism spectrum disorder (ASD). The current paper describes the clinical characteristics of the LEAP cohort and examines age, sex and IQ differences in ASD core symptoms and common co-occurring psychiatric symptoms. A companion paper describes the overall design and experimental protocol and outlines the strategy to identify stratification biomarkers.

METHODS

From six research centres in four European countries, we recruited 437 children and adults with ASD and 300 controls between the ages of 6 and 30 years with IQs varying between 50 and 148. We conducted in-depth clinical characterisation including a wide range of observational, interview and questionnaire measures of the ASD phenotype, as well as co-occurring psychiatric symptoms.

RESULTS

The cohort showed heterogeneity in ASD symptom presentation, with only minimal to moderate site differences on core clinical and cognitive measures. On both parent-report interview and questionnaire measures, ASD symptom severity was lower in adults compared to children and adolescents. The precise pattern of differences varied across measures, but there was some evidence of both lower social symptoms and lower repetitive behaviour severity in adults. Males had higher ASD symptom scores than females on clinician-rated and parent interview diagnostic measures but not on parent-reported dimensional measures of ASD symptoms. In contrast, self-reported ASD symptom severity was higher in adults compared to adolescents, and in adult females compared to males. Higher scores on ASD symptom measures were moderately associated with lower IQ. Both inattentive and hyperactive/impulsive ADHD symptoms were lower in adults than in children and adolescents, and males with ASD had higher levels of inattentive and hyperactive/impulsive ADHD symptoms than females.

CONCLUSIONS

The established phenotypic heterogeneity in ASD is well captured in the LEAP cohort. Variation both in core ASD symptom severity and in commonly co-occurring psychiatric symptoms were systematically associated with sex, age and IQ. The pattern of ASD symptom differences with age and sex also varied by whether these were clinician ratings or parent- or self-reported which has important implications for establishing stratification biomarkers and for their potential use as outcome measures in clinical trials.

The EU-AIMS Longitudinal European Autism Project (LEAP): design and methodologies to identify and validate stratification biomarkers for autism spectrum disorders

BACKGROUND

The tremendous clinical and aetiological diversity among individuals with autism spectrum disorder (ASD) has been a major obstacle to the development of new treatments, as many may only be effective in particular subgroups. Precision medicine approaches aim to overcome this challenge by combining pathophysiologically based treatments with stratification biomarkers that predict which treatment may be most beneficial for particular individuals. However, so far, we have no single validated stratification biomarker for ASD. This may be due to the fact that most research studies primarily have focused on the identification of mean case-control differences, rather than within-group variability, and included small samples that were underpowered for stratification approaches. The EU-AIMS Longitudinal European Autism Project (LEAP) is to date the largest multi-centre, multi-disciplinary observational study worldwide that aims to identify and validate stratification biomarkers for ASD.

METHODS

LEAP includes 437 children and adults with ASD and 300 individuals with typical development or mild intellectual disability. Using an accelerated longitudinal design, each participant is comprehensively characterised in terms of clinical symptoms, comorbidities, functional outcomes, neurocognitive profile, brain structure and function, biochemical markers and genomics. In addition, 51 twin-pairs (of which 36 had one sibling with ASD) are included to identify genetic and environmental factors in phenotypic variability.

RESULTS

Here, we describe the demographic characteristics of the cohort, planned analytic stratification approaches, criteria and steps to validate candidate stratification markers, pre-registration procedures to increase transparency, standardisation and data robustness across all analyses, and share some 'lessons learnt'. A clinical characterisation of the cohort is given in the companion paper (Charman et al., accepted).

CONCLUSION

We expect that LEAP will enable us to confirm, reject and refine current hypotheses of neurocognitive/neurobiological abnormalities, identify biologically and clinically meaningful ASD subgroups, and help us map phenotypic heterogeneity to different aetiologies.

Autism spectrum disorder in sub-saharan africa: A comprehensive scoping review

Autism spectrum disorder (ASD) is recognized as a global public health concern, yet almost everything we know about ASD comes from high-income countries. Here we performed a scoping review of all research on ASD ever published in sub-Saharan Africa (SSA) in order to identify ASD knowledge gaps in this part of the world. Fifty-three publications met inclusion criteria. Themes included the phenotype, genetics and risk factors for ASD in SSA, screening and diagnosis, professional knowledge, interventions for ASD, parental perceptions, and social-cognitive neuroscience. No epidemiological, early intervention, school-based or adult studies were identified. For each identified theme, we aimed to summarize results and make recommendations to fill the knowledge gaps. The quality of study methodologies was generally not high. Few studies used standardized diagnostic instruments, and intervention studies were typically small-scale. Overall, findings suggest a substantial need for large-scale clinical, training, and research programmes to improve the lives of people who live with ASD in SSA. However, SSA also has the potential to make unique and globally-significant contributions to the etiology and treatments of ASD through implementation, interventional, and comparative genomic science. Autism Res 2017, 10: 723-749. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

The Role of the Immune System in Autism Spectrum Disorder

Autism is a neurodevelopmental disorder characterized by deficits in communication and social skills as well as repetitive and stereotypical behaviors. While much effort has focused on the identification of genes associated with autism, research emerging within the past two decades suggests that immune dysfunction is a viable risk factor contributing to the neurodevelopmental deficits observed in autism spectrum disorders (ASD). Further, it is the heterogeneity within this disorder that has brought to light much of the current thinking regarding the subphenotypes within ASD and how the immune system is associated with these distinctions. This review will focus on the two main axes of immune involvement in ASD, namely dysfunction in the prenatal and postnatal periods. During gestation, prenatal insults including maternal infection and subsequent immunological activation may increase the risk of autism in the child. Similarly, the presence of maternally derived anti-brain autoantibodies found in ~20% of mothers whose children are at risk for developing autism has defined an additional subphenotype of ASD. The postnatal environment, on the other hand, is characterized by related but distinct profiles of immune dysregulation, inflammation, and endogenous autoantibodies that all persist within the affected individual. Further definition of the role of immune dysregulation in ASD thus necessitates a deeper understanding of the interaction between both maternal and child immune systems, and the role they have in diagnosis and treatment.

Development of a Machine Learning Algorithm for the Surveillance of Autism Spectrum Disorder

The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year old children in multiple US sites. To classify ASD, trained clinicians review developmental evaluations collected from multiple health and education sources to determine whether the child meets the ASD surveillance case criteria. The number of evaluations collected has dramatically increased since the year 2000, challenging the resources and timeliness of the surveillance system. We developed and evaluated a machine learning approach to classify case status in ADDM using words and phrases contained in children's developmental evaluations. We trained a random forest classifier using data from the 2008 Georgia ADDM site which included 1,162 children with 5,396 evaluations (601 children met ADDM ASD criteria using standard ADDM methods). The classifier used the words and phrases from the evaluations to predict ASD case status. We evaluated its performance on the 2010 Georgia ADDM surveillance data (1,450 children with 9,811 evaluations; 754 children met ADDM ASD criteria). We also estimated ASD prevalence using predictions from the classification algorithm. Overall, the machine learning approach predicted ASD case statuses that were 86.5% concordant with the clinician-determined case statuses (84.0% sensitivity, 89.4% predictive value positive). The area under the resulting receiver-operating characteristic curve was 0.932. Algorithm-derived ASD "prevalence" was 1.46% compared to the published (clinician-determined) estimate of 1.55%. Using only the text contained in developmental evaluations, a machine learning algorithm was able to discriminate between children that do and do not meet ASD surveillance criteria at one surveillance site.

Unsupervised data-driven stratification of mentalizing heterogeneity in autism

Individuals affected by autism spectrum conditions (ASC) are considerably heterogeneous. Novel approaches are needed to parse this heterogeneity to enhance precision in clinical and translational research. Applying a clustering approach taken from genomics and systems biology on two large independent cognitive datasets of adults with and without ASC (n = 694; n = 249), we find replicable evidence for 5 discrete ASC subgroups that are highly differentiated in item-level performance on an explicit mentalizing task tapping ability to read complex emotion and mental states from the eye region of the face (Reading the Mind in the Eyes Test; RMET). Three subgroups comprising 45-62% of ASC adults show evidence for large impairments (Cohen's d = -1.03 to -11.21), while other subgroups are effectively unimpaired. These findings delineate robust natural subdivisions within the ASC population that may allow for more individualized inferences and accelerate research towards precision medicine goals.

Characteristics of communication among Japanese children with autism spectrum disorder: A cluster analysis using the Children's Communication Checklist-2

Some overlap has been suggested among the subtypes of autism spectrum disorder (ASD) in children. The Japanese version of the Children's Communication Checklist-2 (CCC-2) is a useful measure for identifying profiles in relation to communication impairments in children with ASD. The aim of this study was to investigate whether the CCC-2 could identify subtypes in relation to communication impairments in Japanese children with ASD. The study participants were 113 children with ASD but without intellectual disabilities aged 3-12 years. Parents were given the Japanese version of the CCC-2 and asked to rate their children, who were then classified into two groups based on statistical analysis. Significant differences were found between clusters in mean CCC-2 subscales. These results suggest that one subtype was associated with low language competence and strong characteristics of autism, while the other was associated with relatively high language competence and milder characteristics of autism.

SYMPTOM PRESENTATIONS AND CLASSIFICATION OF AUTISM SPECTRUM DISORDER IN EARLY CHILDHOOD: APPLICATION TO THE DIAGNOSTIC CLASSIFICATION OF MENTAL HEALTH AND DEVELOPMENTAL DISORDERS OF INFANCY AND EARLY CHILDHOOD (DC:0-5)

Over the past 5 years, a great deal of information about the early course of autism spectrum disorder (ASD) has emerged from longitudinal prospective studies of infants at high risk for developing ASD based on a previously diagnosed older sibling. The current article describes early ASD symptom presentations and outlines the rationale for defining a new disorder, Early Atypical Autism Spectrum Disorder (EA-ASD) to accompany ASD in the new revision of the ZERO TO THREE Diagnostic Classification of Mental Health and Developmental Disorders of Infancy and Early Childhood (DC:0-5) (in press) alternative diagnostic classification manual. EA-ASD is designed to identify children who are 9 to 36 months of age presenting with a minimum of (a) two social-communication symptoms and (b) one repetitive and restricted behavior symptom as well as (c) evidence of impairment, with the intention of providing these children with appropriately tailored services and improving the likelihood of optimizing their development.

Category attribution as a device for diagnosis: fitting children to the autism spectrum

The practice of medicine involves applying abstract diagnostic classifications to individual patients. Patients present with diverse histories and symptoms, and clinicians are tasked with fitting them into generic categories. They must also persuade patients, or family members, that the diagnosis is appropriate and elicit compliance with prescribed treatments. This can be especially challenging with psychiatric disorders such as autism, for which there are no clear biomarkers. In this paper, we explicate a discursive procedure, which we term category attribution. The procedure juxtaposes a narrative about the child with a claim about members of a clinically relevant category, in this case, either children with autism or typically/normally developing children. The attribution procedure carries the implication that the child does or does not belong to that category. We show that category attributions are organised in a recurrent interactional sequence. Further, we argue that category attributions encode normative expectations about child development, such that the child is rendered typical or atypical relative to clinical and social norms. Accordingly, such categorisation devices have a moral dimension as well as a clinical one.

Divide and Conquer: Sub-Grouping of ASD Improves ASD Detection Based on Brain Morphometry

Low success (<60%) in autism spectrum disorder (ASD) classification using brain morphometry from the large multi-site ABIDE dataset and inconsistent findings on brain morphometric abnormalities in ASD can be attributed to the ASD heterogeneity. In this study, we show that ASD brain morphometry is highly heterogeneous, and demonstrate that the heterogeneity can be mitigated and classification improved if autism severity (AS), verbal IQ (VIQ) and age are used with morphometric features. Morphometric features from structural MRIs (sMRIs) of 734 males (ASD: 361, controls: 373) of ABIDE were derived using FreeSurfer. Applying the Random Forest classifier, an AUC of 0.61 was achieved. Adding VIQ and age to morphometric features, AUC improved to 0.68. Sub-grouping the subjects by AS, VIQ and age improved the classification with the highest AUC of 0.8 in the moderate-AS sub-group (AS = 7–8). Matching subjects on age and/or VIQ in each sub-group further improved the classification with the highest AUC of 0.92 in the low AS sub-group (AS = 4–5). AUC decreased with AS and VIQ, and was the lowest in the mid-age sub-group (13–18 years). The important features were mainly from the frontal, temporal, ventricular, right hippocampal and left amygdala regions. However, they highly varied with AS, VIQ and age. The curvature and folding index features from frontal, temporal, lingual and insular regions were dominant in younger subjects suggesting their importance for early detection. When the experiments were repeated using the Gradient Boosting classifier similar results were obtained. Our findings suggest that identifying brain biomarkers in sub-groups of ASD can yield more robust and insightful results than searching across the whole spectrum. Further, it may allow identification of sub-group specific brain biomarkers that are optimized for early detection and monitoring, increasing the utility of sMRI as an important tool for early detection of ASD.

[On the benefits to keep using the asperger diagnosis]

The purpose of this paper is to examine the possible benefits to keep using the diagnosis of Asperger's syndrome. We first describe the evolution of this entity over time and within nomenclature bases such as the ICD- 10, the CFTMEA and the last versions of DSM. Then, we discuss more precisely the impact of the decision made in the DSM-5 to suppress the Asperger syndrome as a differentiated entity within the pervasive developmental disorders (PDD). This disorder chapter by the way also disappears and is replaced by Autism Spectrum Disorder (ASD). We present here three clinical cases encountered in an outpatient general child psychiatry clinic : 1 case was diagnosed as Asperger syndrome, 1 as infantile autism (early infantile autism) and 1 as another pervasive developmental disorder (psychotic disharmony). The objective was to expose the commonali ties and differences between these three entities. We conclude that keeping using the Asperger diagnosis is important for the clinical management of these clinical situations but also for the individual, his or her family and society at large.

Visual memory and sustained attention impairment in youths with autism spectrum disorders

BACKGROUND

An uneven neurocognitive profile is a hallmark of autism spectrum disorder (ASD). Studies focusing on the visual memory performance in ASD have shown controversial results. We investigated visual memory and sustained attention in youths with ASD and typically developing (TD) youths.

METHOD

We recruited 143 pairs of youths with ASD (males 93.7%; mean age 13.1, s.d. 3.5 years) and age- and sex-matched TD youths. The ASD group consisted of 67 youths with autistic disorder (autism) and 76 with Asperger's disorder (AS) based on the DSM-IV criteria. They were assessed using the Cambridge Neuropsychological Test Automated Battery involving the visual memory [spatial recognition memory (SRM), delayed matching to sample (DMS), paired associates learning (PAL)] and sustained attention (rapid visual information processing; RVP).

RESULTS

Youths with ASD performed significantly worse than TD youths on most of the tasks; the significance disappeared in the superior intelligence quotient (IQ) subgroup. The response latency on the tasks did not differ between the ASD and TD groups. Age had significant main effects on SRM, DMS, RVP and part of PAL tasks and had an interaction with diagnosis in DMS and RVP performance. There was no significant difference between autism and AS on visual tasks.

CONCLUSIONS

Our findings implied that youths with ASD had a wide range of visual memory and sustained attention impairment that was moderated by age and IQ, which supports temporal and frontal lobe dysfunction in ASD. The lack of difference between autism and AS implies that visual memory and sustained attention cannot distinguish these two ASD subtypes, which supports DSM-5 ASD criteria.

[Dimensional measures in autism spectrum disorders: do we know what we measure?]

BACKGROUND

In the last decades, researchers often used measures to quantify autism spectrum disorder (ASD) traits, paralleling the tendency to describe psychiatric and developmental disorders more dimensionally. The broader autism phenotype (BAP) concept originates from this kind of research.

AIM

The primary aim of our studies was to study the existence of the BAP and the familial transmission of quantitative autism traits (QAT).

METHOD

We measured ASD-traits with interviews and questionnaires in all members of 170 families with at least one child with ASD.

RESULTS

We confirmed the existence of the BAP in fathers, as well as the familial transmission of QAT. The results also suggest that what is measured with these questionnaires might depend on the population and the context.

CONCLUSION

Based on our results and additional data from scientific literature, we reflect on the interpretations of research results and the use of quantitative scales in both research and clinical practice.