Annual research review: re-thinking the classification of autism spectrum disorders

Agency, embodiment and enactment in psychosomatic theory and practice

In this paper, we examine some of the conceptual, pragmatic and moral dilemmas intrinsic to psychosomatic explanation in medicine, psychiatry and psychology. Psychosomatic explanation invokes a social grey zone in which ambiguities and conflicts about agency, causality and moral responsibility abound. This conflict reflects the deep-seated dualism in Western ontology and concepts of personhood that plays out in psychosomatic research, theory and practice. Illnesses that are seen as psychologically mediated tend also to be viewed as less real or legitimate. New forms of this dualism are evident in philosophical attacks on Engel's biopsychosocial approach, which was a mainstay of earlier psychosomatic theory, and in the recent Research Domain Criteria research programme of the US National institute of Mental Health which opts for exclusively biological modes of explanation of illness. We use the example of resignation syndrome among refugee children in Sweden to show how efforts to account for such medically unexplained symptoms raise problems of the ascription of agency. We argue for an integrative multilevel approach that builds on recent work in embodied and enactive cognitive science. On this view, agency can have many fine gradations that emerge through looping effects that link neurophenomenology, narrative practices and cultural affordances in particular social contexts. This multilevel ecosocial view points the way towards a renewed biopsychosocial approach in training and clinical practice that can advance person-centred medicine and psychiatry.

Social Brain Functional Maturation in Newborn Infants With and Without a Family History of Autism Spectrum Disorder

IMPORTANCE

What is inherited or acquired in neurodevelopmental conditions such as autism spectrum disorder (ASD) is not a fixed outcome, but instead is a vulnerability to a spectrum of traits, especially social difficulties. Identifying the biological mechanisms associated with vulnerability requires looking as early in life as possible, before the brain is shaped by postnatal mechanisms and/or the experiences of living with these traits. Animal studies suggest that susceptibility to neurodevelopmental disorders arises when genetic and/or environmental risks for these conditions alter patterns of synchronous brain activity in the perinatal period, but this has never been examined in human neonates.

OBJECTIVE

To assess whether alternation of functional maturation of social brain circuits is associated with a family history of ASD in newborns.

DESIGN, SETTING, AND PARTICIPANTS

In this cohort study of 36 neonates with and without a family history of ASD, neonates underwent magnetic resonance imaging at St Thomas Hospital in London, England, using a dedicated neonatal brain imaging system between June 23, 2015, and August 1, 2018. Neonates with a first-degree relative with ASD (R+) and therefore vulnerable to autistic traits and neonates without a family history (R-) were recruited for the study. Synchronous neural activity in brain regions linked to social function was compared.

MAIN OUTCOMES AND MEASURES

Regions responsible for social function were selected with reference to a published meta-analysis and the level of synchronous activity within each region was used as a measure of local functional connectivity in a regional homogeneity analysis. Group differences, controlling for sex, age at birth, age at scan, and group × age interactions, were examined.

RESULTS

The final data set consisted of 18 R+ infants (13 male; median [range] postmenstrual age at scan, 42.93 [40.00-44.86] weeks) and 18 R- infants (13 male; median [range] postmenstrual age at scan, 42.50 [39.29-44.58] weeks). Neonates who were R+ had significantly higher levels of synchronous activity in the right posterior fusiform (t = 2.48; P = .04) and left parietal cortices (t = 3.96; P = .04). In addition, there was a significant group × age interaction within the anterior segment of the left insula (t = 3.03; P = .04) and cingulate cortices (right anterior: t = 3.00; P = .03; left anterior: t = 2.81; P = .03; right posterior: t = 2.77; P = .03; left posterior: t = 2.55; P = .03). In R+ infants, levels of synchronous activity decreased over 39 to 45 weeks' postmenstrual age, whereas synchronous activity levels increased in R- infants over the same period.

CONCLUSIONS AND RELEVANCE

Synchronous activity is required during maturation of functionally connected networks. This study found that in newborn humans, having a first-degree relative with ASD was associated with higher levels of local functional connectivity and dysmaturation of interconnected regions responsible for processing higher-order social information.

Self-directed speech and self-regulation in childhood neurodevelopmental disorders: Current findings and future directions

Self-directed speech is considered an important developmental achievement as a self-regulatory mediator of thinking and behavior. Atypical self-directed speech is often implicated in the self-regulatory challenges characteristic of children with neurodevelopmental disorders. A growing body of evidence provides snapshots across age-levels and diagnoses, often presenting conflicting results. This systematic review is undertaken to impose clarity on the nature, extent, and self-regulatory implications of self-directed speech interruption in children with developmental language disorder (DLD), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD).A rigorous search process of relevant databases (i.e., PsychInfo, PubMed, CINAHL, ERIC) uncovered 19 relevant peer-reviewed articles that investigate self-directed speech in children with neurodevelopmental disorders. Consistent across the research, children with DLD, ASD, and ADHD present with differential development and use of self-directed speech.In its synthesis of findings, this systematic review clearly explicates the differential ontogenesis of self-directed speech in neurodevelopmental disorders and interprets the self-regulatory implications for children with DLD, ASD, and ADHD. Furthermore, the review spotlights important future research directions to better understand the mechanistic relationship between self-directed speech and self-regulation.

Comparing the 1-year impact of preschool autism intervention programs in two Canadian provinces

Autism spectrum disorder (ASD) is associated with early differences in children's social interactions, communication, and play/interests. In many countries, considerable resources are invested in early intensive behavioral intervention (EIBI) programs for children with ASD, which aim to build adaptive skills and prevent or treat problem behavior. However, these programs vary widely in structure and delivery. Research evidence supports the efficacy of EIBI, but large knowledge gaps remain about the effectiveness of publicly funded EIBI programs. With policy-makers as formal research partners, we compared children's progress over 1 year in public preschool programs in adjacent Canadian provinces, New Brunswick (NB) and Nova Scotia (NS). In NB, children received up to 20 hr/week of comprehensive EIBI in a publicly funded, privately provided program. In NS, children received up to 15 hr/week of Pivotal Response Treatment and Positive Behavior Support delivered through the publicly funded healthcare system. In this observational parallel cohort study, we collected parent-reported data on 298 NB preschoolers (76.5% boys) and 221 NS preschoolers (86.9% boys) at EIBI start and 1 year later. Multilevel analysis revealed significant differences at baseline: NS children were older, with lower adaptive functioning and more severe ASD symptoms than NB children. Despite these pre-treatment differences that favor NB, children in both provinces showed similar adaptive functioning gains and reductions of maladaptive behavior. No changes were seen in mean ASD symptom severity in either province over time. Results highlight the value of evaluating interventions in their implementation contexts, and have important implications for devising optimal ASD policy. Autism Research 2019, 12: 667-681. © 2019 International Society for Autism Research,Wiley Periodicals, Inc. LAY SUMMARY: We need to know more about the impact of different forms of early intensive behavioral intervention (EIBI) for young children with autism spectrum disorder (ASD). We showed that preschoolers with ASD gained important skills while in public EIBI programs in two Canadian provinces. We also saw that differences in how EIBI programs are structured and characteristics of children who are served may affect outcomes. For these reasons, policy making requires evidence that fits the local context.

Factors that affect the physical and mental health of caregivers of school-age children and transitioning young adults with autism spectrum disorder

BACKGROUND

Although studies have examined quality of life (QOL) among family caregivers of individuals with autism spectrum disorder (ASD), little is known about potential differences in QOL based on the age of the individual with ASD, particularly in relation to caregivers' needs, beliefs and coping mechanisms.

METHOD

This study investigated 132 caregivers of school-age children and 61 caregivers of transitioning young adults with ASD on measures of caregiving, strain, coping, family needs and QOL.

RESULTS

The results indicated that there were no significant differences in QOL based on the age of the individuals with ASD, although parents of children reported significantly more caregiving behaviours, with coping, internalized strain and health information needs predicted mental health QOL.

DISCUSSION

Age of the individual with ASD was not significantly related to caregivers' QOL. More research is needed to determine independent predictors of caregiver QOL across the lifespan.

A Neurorobotics Simulation of Autistic Behavior Induced by Unusual Sensory Precision

Recently, applying computational models developed in cognitive science to psychiatric disorders has been recognized as an essential approach for understanding cognitive mechanisms underlying psychiatric symptoms. Autism spectrum disorder is a neurodevelopmental disorder that is hypothesized to affect information processes in the brain involving the estimation of sensory precision (uncertainty), but the mechanism by which observed symptoms are generated from such abnormalities has not been thoroughly investigated. Using a humanoid robot controlled by a neural network using a precision-weighted prediction error minimization mechanism, it is suggested that both increased and decreased sensory precision could induce the behavioral rigidity characterized by resistance to change that is characteristic of autistic behavior. Specifically, decreased sensory precision caused any error signals to be disregarded, leading to invariability of the robot's intention, while increased sensory precision caused an excessive response to error signals, leading to fluctuations and subsequent fixation of intention. The results may provide a system-level explanation of mechanisms underlying different types of behavioral rigidity in autism spectrum and other psychiatric disorders. In addition, our findings suggest that symptoms caused by decreased and increased sensory precision could be distinguishable by examining the internal experience of patients and neural activity coding prediction error signals in the biological brain.

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.

Giok the Alien: An AR-Based Integrated System for the Empowerment of Problem-Solving, Pragmatic, and Social Skills in Pre-School Children

The use of technology for educational purposes is a consolidated reality, and many new tools are constantly being devised and offered for use with both normally developing children and children with special needs. Nonetheless, a detailed analysis of the processes being stimulated and of the goals being pursued is often lacking or absent. In this work we describe the design, development and preliminary testing of an integrated system which combines the use of smart devices, a physical cube, augmented reality (AR) technology, a smart TV, and a software application especially designed to stimulate cognitive and social functions in pre-school children. The system was tested with three groups of children (25 children in total) during kindergarten activities. The results show that the system is easy to understand, elicits high levels of participation and social interaction, favors strategic behaviors, and can be used by the children with limited need of instruction and support by the adult. The implications for empowerment in typically developing children and the possibilities for use with children who have specific impairments in social communication are discussed.

Statistical Learning is Associated with Autism Symptoms and Verbal Abilities in Young Children with Autism

Statistical learning-extracting regularities in the environment-may underlie complex social behavior. 124 children, 56 with autism and 68 typically developing, ages 2-8 years, completed a novel visual statistical learning task on an iPad. Averaged together, children with autism demonstrated less learning on the task compared to typically developing children. However, multivariate classification analyses characterized individual behavior patterns, and demonstrated a subset of children with autism had similar learning patterns to typically developing children and that subset of children had less severe autism symptoms. Therefore, statistically averaging data resulted in missing critical heterogeneity. Variability in statistical learning may help to understand differences in autism symptoms across individuals and could be used to tailor and inform treatment decisions.

Measuring the Diagnostic Features of Social (Pragmatic) Communication Disorder: An Exploratory Study

PURPOSE

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition introduced a new neurodevelopmental disorder, social (pragmatic) communication disorder (SPCD), that is characterized by deficits in 4 areas of communication. Although descriptions of these areas are provided, no assessment tools for SPCD are recommended. The purpose of this study was to examine the extent to which items from measurement tools commonly used in assessing pragmatic language impairment and related disorders might be useful in assessing the characteristics of social communication that define SPCD in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

METHOD

Based on a literature search, 594 items from assessment tools commonly used to measure social communication abilities in people with pragmatic language impairment were identified. The first author judged whether each item reflected 1, more than 1, or none of the 4 SPCD diagnostic characteristics. After a brief training process, 5 second raters independently mapped subsets of items to the 6 categories. We calculated the percentage of agreement and Cohen's kappa for each pair of raters in assigning items to categories.

RESULTS

Percentages of agreement ranged from 76% to 82%, and Cohen's kappa values ranged from .69 to .76, indicating substantial agreement. Sources and item numbers for the 206 items that both raters assigned to the same SPCD feature are provided.

CONCLUSIONS

These items may provide guidance in assessing SPCD and in designing standardized screening and diagnostic measures for SPCD.

Social communication disorder: a narrative review on current insights

Social communication disorder (SCD) is a novel diagnosis listed under the rubric of communication disorders within the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) and it is reported to be characterized by impairment in use of verbal and nonverbal communication for social aims. This review attempts to summarize the current understanding of the SCD concept along with its evolution and presents data from previous studies conducted. Suggestions for further research are also delineated. As listed in DSM-5, the criteria for this novel diagnosis are vague, display elevated comorbidity with other neurodevelopmental disorders and other childhood psychopathologies, and show partial overlap with autistic spectrum disorders both in terms of genetics and family histories. Data on cross-cultural presentations and temporal stability are also limited. The social communication model proposed by Catani and Bambini may help integrate the neurobiological findings pertaining to SCD. Valid and reliable assessment methods need to be developed for SCD. This may involve either development of novel instruments capturing the DSM-5 criteria or application of statistical methods such as item response theory to existing instruments. The relationships between broad autism phenotype, pragmatic language impairment, nonverbal learning disorder, learning disorders, autistic spectrum disorders, and SCD should be evaluated with further studies.

Association between hypertensive disorders of pregnancy and risk of autism in offspring: a systematic review and meta-analysis of observational studies

Background

Autism spectrum disorder (ASD) is a common severe pervasive neurodevelopmental disorder of undetermined etiology. Environmental exposures, especially pregnancy complications, have been increasingly recognized as a potential risk factor for ASD. Our aim was to (1) systematically evaluate the association between hypertensive disorders of pregnancy (HDP) and the risk of ASD in offspring, (2) specifically draw a subgroup analysis of disease severity in patients with HDP to achieve more sufficient evidence on this issue.

Results

A total of 21 studies were identified with more than 6.5 million participants, including 31,027 ASD probands. A comparative meta-analysis established that offspring born premature to HDP were significantly associated with ASD than matched controls (OR = 1.42, 95% CI: 1.34–1.50). Subgroup analysis of clinical classification include: (1) gestational hypertension, (2) pre-eclampsia, (3) chronic hypertension complicating pregnancy (CHP). The offspring of mothers with pre-eclampsia and CHP have slightly higher risk (OR = 1.43; OR = 1.48, respectively) of ASD than those of mothers with gestational hypertension (OR = 1.37). In consistence with most previous researches, higher ASD prevalence was observed in male than female (OR = 1.38), indicating a potential role for gender in the pathophysiology of ASD.

Materials and Methods

We conducted a systematic literature search on PubMed, EMBASE, Web of Science, PsycINFO database and China National Knowledge Infrastructure up to Jun. 2017. Statistical analysis was performed using Stata 10.0.

Conclusions

This meta-analysis implies a possible link between HDP and the risk of ASD in offspring. However, further investigation should be conducted to confirm this conclusion, and intensive prenatal surveillance and early prediction for ASD is needed.

Three Approaches to Understanding and Classifying Mental Disorder: ICD-11, DSM-5, and the National Institute of Mental Health’s Research Domain Criteria (RDoC)

The diagnosis of mental disorder initially appears relatively straightforward: Patients present with symptoms or visible signs of illness; health professionals make diagnoses based primarily on these symptoms and signs; and they prescribe medication, psychotherapy, or both, accordingly. However, despite a dramatic expansion of knowledge about mental disorders during the past half century, understanding of their components and processes remains rudimentary. We provide histories and descriptions of three systems with different purposes relevant to understanding and classifying mental disorder. Two major diagnostic manuals—the International Classification of Diseases and the Diagnostic and Statistical Manual of Mental Disorders—provide classification systems relevant to public health, clinical diagnosis, service provision, and specific research applications, the former internationally and the latter primarily for the United States. In contrast, the National Institute of Mental Health’s Research Domain Criteria provides a framework that emphasizes integration of basic behavioral and neuroscience research to deepen the understanding of mental disorder. We identify four key issues that present challenges to understanding and classifying mental disorder: etiology, including the multiple causality of mental disorder; whether the relevant phenomena are discrete categories or dimensions; thresholds, which set the boundaries between disorder and nondisorder; and comorbidity, the fact that individuals with mental illness often meet diagnostic requirements for multiple conditions. We discuss how the three systems’ approaches to these key issues correspond or diverge as a result of their different histories, purposes, and constituencies. Although the systems have varying degrees of overlap and distinguishing features, they share the goal of reducing the burden of suffering due to mental disorder.

Joint-Attention and the Social Phenotype of School-Aged Children with ASD

The validity of joint attention assessment in school-aged children with ASD is unclear (Lord, Jones, Journal of Child Psychology and Psychiatry 53(5):490-509, 2012). This study examined the feasibility and validity of a parent-report measure of joint attention related behaviors in verbal children and adolescents with ASD. Fifty-two children with ASD and 34 controls were assessed with the Childhood Joint Attention Rating Scale (C-JARS). The C-JARS exhibited internally consistency, α = 0.88, and one factor explained 49% of the scale variance. Factor scores correctly identified between 88 and 94% of the children with ASD and 62-82% of controls. These scores were correlated with the ADOS-2, but not other parent-report symptom measures. The C-JARS appears to assess a unique dimension of the social-phenotype of children with ASD.

Brief Report: Joint Attention and Information Processing in Children with Higher Functioning Autism Spectrum Disorders

Theory suggests that information processing during joint attention may be atypical in children with Autism Spectrum Disorder (ASD). This hypothesis was tested in a study of school-aged children with higher functioning ASD and groups of children with symptoms of ADHD or typical development. The results indicated that the control groups displayed significantly better recognition memory for pictures studied in an initiating joint attention (IJA) rather than responding to joint attention (RJA) condition. This effect was not evident in the ASD group. The ASD group also recognized fewer pictures from the IJA condition than controls, but not the RJA condition. Atypical information processing may be a marker of the continued effects of joint attention disturbance in school aged children with ASD.

Design and Cohort Characteristics of the Social Spectrum Study: A Multicenter Study of the Autism Spectrum Among Clinically Referred Children

This paper provides an overview of the design and cohort characteristics of the Social Spectrum Study: a clinical cohort study that used a two-phase sampling design to identify children at risk for ASD. After screening 1281 children aged 2.5–10 years who had been consecutively referred to one of six mental health services in the Netherlands, children who screened positive for ASD (n = 428) and a random selection of screen negatives (n = 240) were invited to participate in diagnostic assessments and questionnaires regarding the child, family and society. A 1-year follow-up was also conducted. Results from this study may contribute to knowledge of the identification and characterization of children with ASD, family processes, and the impact of ASD on the family and society.

Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification

Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods.

Agreement between concern about autism spectrum disorder at the time of referral and diagnosis, and factors associated with agreement

AIM

This study examined the level of agreement between referral reason and diagnostic outcome for pre-school aged children showing developmental delays and differences. In particular, the level of agreement when there was concern about autism spectrum disorder (ASD) was investigated.

METHODS

Clinical records for children (n = 677) assessed for developmental concerns at a multidisciplinary assessment clinic in Sydney, Australia, were examined. Referral reason and diagnostic outcome were compared. Referral sources and factors including age, developmental level and language functioning, were explored for potential association with the consistency between referral reason and diagnosis.

RESULTS

Approximately 30% of children referred for developmental assessment with ASD concern were not diagnosed with ASD. Agreement between referral reason and diagnosis was similar regardless of referral source and child age. Of children where diagnostic outcome was discordant with referral reason, 24% had delayed language. Variability in cognitive or developmental profiles was common (64%), regardless of diagnostic outcome.

CONCLUSIONS

Referral for ASD diagnostic assessments in the cases where ASD is not the primary cause for a child's difficulties increases pressure on assessment services, can delay access to ASD-specific intervention services for children who do have ASD, and can contribute to parent anxiety unnecessarily. The current evidence that a substantial minority of children were referred for ASD diagnostic assessments when ASD was not the primary cause for the difficulties emphasises the value of adopting a developmental differences framework, focusing on dimensions of behaviour and functioning, rather than an autism-specific service structure.

Study protocol of the ASD-Net, the German research consortium for the study of Autism Spectrum Disorder across the lifespan: from a better etiological understanding, through valid diagnosis, to more effective health care

BACKGROUND

Autism Spectrum Disorder (ASD) is a severe, lifelong neurodevelopmental disorder with early onset that places a heavy burden on affected individuals and their families. Due to the need for highly specialized health, educational and vocational services, ASD is a cost-intensive disorder, and strain on health care systems increases with increasing age of the affected individual.

METHODS

The ASD-Net will study Germany's largest cohort of patients with ASD over the lifespan. By combining methodological expertise from all levels of clinical research, the ASD-Net will follow a translational approach necessary to identify neurobiological pathways of different phenotypes and their appropriate identification and treatment. The work of the ASD-Net will be organized into three clusters concentrating on diagnostics, therapy and health economics. In the diagnostic cluster, data from a large, well-characterized sample (N = 2568) will be analyzed to improve the efficiency of diagnostic procedures. Pattern classification methods (machine learning) will be used to identify algorithms for screening purposes. In a second step, the developed algorithm will be tested in an independent sample. In the therapy cluster, we will unravel how an ASD-specific social skills training with concomitant oxytocin administration can modulate behavior through neurobiological pathways. For the first time, we will characterize long-term effects of a social skills training combined with oxytocin treatment on behavioral and neurobiological phenotypes. Also acute effects of oxytocin will be investigated to delineate general and specific effects of additional oxytocin treatment in order to develop biologically plausible models for symptoms and successful therapeutic interventions in ASD. Finally, in the health economics cluster, we will assess service utilization and ASD-related costs in order to identify potential needs and cost savings specifically tailored to Germany. The ASD-Net has been established as part of the German Research Network for Mental Disorders, funded by the BMBF (German Federal Ministry of Education and Research).

DISCUSSION

The highly integrated structure of the ASD-Net guarantees sustained collaboration of clinicians and researchers to alleviate individual distress, harm, and social disability of patients with ASD and reduce costs to the German health care system.

TRIAL REGISTRATION

Both clinical trials of the ASD-Net are registered in the German Clinical Trials Register: DRKS00008952 (registered on August 4, 2015) and DRKS00010053 (registered on April 8, 2016).

Evolving Definitions of Autism and Impact on Eligibility for Developmental Disability Services: California Case Example

When establishing eligibility for developmental disability (DD) services, definitions of specific diagnostic conditions, such as autism, impact policy. Under the Medicaid home and community-based waiver program, states have discretion in determining specific program or service eligibility criteria, the nature of supports to be provided, and the number of individuals to be served. Individuals with DD, their families, and advocates have pushed to expand eligibility and improve the quality of services and supports received. This article uses a California legal case to explore the impact on individuals seeking eligibility for DD services when states rely on evolving diagnostic criteria for autism spectrum disorder. Recommendations are made for a more equitable and consistent approach to disability eligibility determination.

Multi-task diagnosis for autism spectrum disorders using multi-modality features: A multi-center study

Autism spectrum disorder (ASD) is a neurodevelopment disease characterized by impairment of social interaction, language, behavior, and cognitive functions. Up to now, many imaging-based methods for ASD diagnosis have been developed. For example, one may extract abundant features from multi-modality images and then derive a discriminant function to map the selected features toward the disease label. A lot of recent works, however, are limited to single imaging centers. To this end, we propose a novel multi-modality multi-center classification (M3CC) method for ASD diagnosis. We treat the classification of each imaging center as one task. By introducing the task-task and modality-modality regularizations, we solve the classification for all imaging centers simultaneously. Meanwhile, the optimal feature selection and the modeling of the discriminant functions can be jointly conducted for highly accurate diagnosis. Besides, we also present an efficient iterative optimization solution to our formulated problem and further investigate its convergence. Our comprehensive experiments on the ABIDE database show that our proposed method can significantly improve the performance of ASD diagnosis, compared to the existing methods. Hum Brain Mapp 38:3081-3097, 2017. © 2017 Wiley Periodicals, Inc.

Clinical Validity of the ADI-R in a US-Based Latino Population

The Autism Diagnostic Interview-Revised (ADI-R) has been validated as a tool to aid in the diagnosis of Autism; however, given the growing diversity in the United States, the ADI-R must be validated for different languages and cultures. This study evaluates the validity of the ADI-R in a US-based Latino, Spanish-speaking population of 50 children and adolescents with ASD and developmental disability. Sensitivity and specificity of the ADI-R as a diagnostic tool were moderate, but lower than previously reported values. Validity of the social reciprocity and restrictive and repetitive behaviors domains was high, but low in the communication domain. Findings suggest that language discordance between caregiver and child may influence reporting of communication symptoms and contribute to lower sensitivity and specificity.

Teaching a Child with Autism to Mand for Answers to Questions Using a Speech-Generating Device

The current study systematically replicates and extends the findings of Ingvarsson and Hollobaugh (2010) by teaching a boy with autism who used a speech-generating device to mand for answers to unknown questions. The effects of the intervention were evaluated via a multiple baseline across stimulus sets. The intervention resulted in acquisition of both the mand for information and intraverbal responses (i.e., correct answers to previously unknown questions). However, generalization of the mand for information was limited.

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

[Validation in Arabic of the Revised Autistic Behavior Summarized Evaluation Scale (BSE-R)]

The Behavioral Summarized Evaluation is a rating scale specifically related to the assessment of autistic behaviors in children and young teenagers with Autism Spectrum Disorder, such as communication, social interaction and interests. It also facilitates progressive recording of the evolution of children with ASD involved in short-term therapeutical studies. In the absence of standardized tools to assess autistic behaviors and effects of therapeutics in Lebanon, the BSE-R validation is an urgent need. The purpose of this study is to validate the BSE-R to the Lebanese population.

METHOD

Specialized translators and clinicians ensured forward and backward translation of the scale into Arabic. The participants consisted of 100 children with ASD, diagnosed upon DSMIV-TR criterion (age range: 35 to 153 months, DS: 28.0). Fifty-eight had intellectual disability (ID). Scores of autism severity as measured by CARS1 (Schopler et al., 1980) varied from 20-58.5. Trained raters on recorded individual and group situations performed BSE-R ratings.

RESULTS

BSE-R for Lebanese was found to have excellent inter-rater reliability. The internal validity highlighted a main factor which describes the severity of core autistic behaviors related to social interactions and communication, and behavioral specificities and particular interests with an internal consistency of 0.91 in a one-to-one setting and 0.92 in group settings. External validity of the scale as measured by correlations with CARS and the age of the children showed that this main factor is essentially determined by ASD severity, not by the severity of ID. It was named "relational deficiency" according to the initial paper of Barthélémy et al. (1997).

CONCLUSION

BSE-R in Arabic is a practical tool, useful to all team members working with ASD children in Lebanon and the Arab countries. It will also allow future research based on reliable tools at an international level.

Subdimensions of social-communication impairment in autism spectrum disorder

BACKGROUND

More refined dimensions of social-communication impairment are needed to elucidate the clinical and biological boundaries of autism spectrum disorders (ASD) and other childhood onset psychiatric disorders associated with social difficulties, as well as to facilitate investigations in treatment and long-term outcomes of these disorders.

METHODS

This study was intended to identify separable dimensions of clinician-observed social-communication impairments by examining scores on a widely used autism diagnostic instrument. Participants included verbally fluent children ages 3-13 years, who were given a clinical diagnosis of ASD (n = 120) or non-ASD (i.e. ADHD, language disorder, intellectual disability, mood or anxiety disorder; n = 118) following a comprehensive diagnostic assessment. Exploratory and confirmatory factor analysis examined the factor structure of algorithm items from the Autism Diagnostic Observation Schedule (ADOS), Module 3.

RESULTS

Results indicated that a three-factor model consisting of repetitive behaviors and two separate social-communication behavior factors had superior fit compared to a two-factor model that included repetitive behaviors and one social-communication behavior factor. In the three-factor model, impairments in 'Basic Social-Communication' behaviors (e.g. eye contact, facial expressions, gestures) were separated from impairments in 'Interaction quality.' Confirmatory factor analysis in an independent sample of children in the Simons Simplex Collection (SSC) further supported the division of social-communication impairments into these two factors. Scores in Interaction Quality were significantly associated with nonverbal IQ and male sex in the ASD group, and with age in the non-ASD group, while scores in basic social communication were not significantly associated with any of these child characteristics in either diagnostic group.

CONCLUSIONS

Efforts to conceptualize level, or severity, of social-communication impairment in children with neurodevelopmental disorders might be facilitated by separating the most basic (or proximal) social-communication impairments from those that could arise from a range of other phenotypic variables. Identification of social-communication subdimensions also highlights potential avenues for measuring different types of social-communication impairments for different purposes (e.g. for differential diagnosis vs. response to treatment).

Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion

BACKGROUND

Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools.

METHODS

The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation.

RESULTS

The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes.

CONCLUSIONS

ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight the limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools.

Hope and doubt in the promise of neuroimaging: The case of autism spectrum disorder

Although neuroimaging is currently not a component of the diagnostic process for autism spectrum disorders, some scientists hail these technologies for their promise to one day replace behaviorally based psychiatric diagnostic techniques. This article examines how psychiatrists understand the potential use of neuroimaging technologies within the context of clinical practice. Drawing on 10 semi-structured interviews with child and adolescent psychiatrists, I describe the hope and doubt that comprise their discourse of ambivalence. This analysis demonstrates that the uses and meanings of neuroimaging technologies are rearticulated in ongoing debates in the field of psychiatry regarding the role of the biopsychiatric model in the diagnosis and treatment of mental illness. This study highlights issues surrounding the perceived biopsychiatric focus of neuroimaging technologies within clinical practice, concerns regarding misdirected research attention, and the ways in which understandings of future utility mediate perceptions of technological utility.

Autism spectrum disorder in adults: diagnosis, management, and health services development

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by pervasive difficulties since early childhood across reciprocal social communication and restricted, repetitive interests and behaviors. Although early ASD research focused primarily on children, there is increasing recognition that ASD is a lifelong neurodevelopmental disorder. However, although health and education services for children with ASD are relatively well established, service provision for adults with ASD is in its infancy. There is a lack of health services research for adults with ASD, including identification of comorbid health difficulties, rigorous treatment trials (pharmacological and psychological), development of new pharmacotherapies, investigation of transition and aging across the lifespan, and consideration of sex differences and the views of people with ASD. This article reviews available evidence regarding the etiology, legislation, diagnosis, management, and service provision for adults with ASD and considers what is needed to support adults with ASD as they age. We conclude that health services research for adults with ASD is urgently warranted. In particular, research is required to better understand the needs of adults with ASD, including health, aging, service development, transition, treatment options across the lifespan, sex, and the views of people with ASD. Additionally, the outcomes of recent international legislative efforts to raise awareness of ASD and service provision for adults with ASD are to be determined. Future research is required to identify high-quality, evidence-based, and cost-effective models of care. Furthermore, future health services research is also required at the beginning and end of adulthood, including improved transition from youth to adult health care and increased understanding of aging and health in older adults with ASD.

The autism symptom interview, school-age: A brief telephone interview to identify autism spectrum disorders in 5-to-12-year-old children

This study reports on the initial validation of the Autism Symptom Interview (ASI), School-Age, a brief (15-20 min) phone interview derived from questions from the Autism Diagnostic Interview-Revised (ADI-R). The ASI, School-Age was administered by interviewers with minimal training to parents of children ages 5 to 12 who had all been previously identified with (or referred for assessment of) ASD or another neurodevelopmental disorder. Children then underwent a comprehensive assessment to determine a best-estimate clinical diagnosis of ASD (n = 159) or non-ASD (e.g. language disorder, intellectual disability, ADHD; n = 130). Clinicians who conducted the assessments were blind to ASI results. ROC analyses compared ASI scores to clinical diagnosis. Due to the small number of participants with non-ASD diagnoses who were classified as nonverbal (i.e. not yet using phrases on a daily basis), it was not possible to assess sensitivity and specificity of the nonverbal algorithm in this sample. The verbal algorithm yielded a sensitivity of 0.87 (95% CI = 0.81-0.92) and a specificity of 0.62 (95% CI = 0.53-0.70). When used in conjunction with the Autism Diagnostic Observation Schedule (ADOS), sensitivity and specificity were 0.82 (95% CI = 0.74-0.88) and 0.92 (95% CI = 0.86-0.96), respectively. Internal consistency and test-retest reliability were both excellent. Particularly for verbal school age children, the ASI may serve as a useful tool to more quickly ascertain or classify children with ASD for research or clinical triaging purposes. Additional data collection is underway to determine the utility of the ASI in children who are younger and/or nonverbal. Autism Res 2017, 10: 78-88. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Diagnosis of Autism Spectrum Disorders Using Temporally Distinct Resting-State Functional Connectivity Networks

INTRODUCTION

Resting-state functional magnetic resonance imaging (R-fMRI) is dynamic in nature as neural activities constantly change over the time and are dominated by repeating brief activations and deactivations involving many brain regions. Each region participates in multiple brain functions and is part of various functionally distinct but spatially overlapping networks. Functional connectivity computed as correlations over the entire time series always overlooks interregion interactions that often occur repeatedly and dynamically in time, limiting its application to disease diagnosis.

AIMS

We develop a novel framework that uses short-time activation patterns of brain connectivity to better detect subtle disease-induced disruptions of brain connectivity. A clustering algorithm is first used to temporally decompose R-fMRI time series into distinct clusters with similar spatial distribution of neural activity based on the assumption that functionally distinct networks should be largely temporally distinct as brain states do not simultaneously coexist in general. A Pearson correlation-based functional connectivity network is then constructed for each cluster to allow for better exploration of spatiotemporal dynamics of individual neural activity. To reduce significant intersubject variability and to remove possible spurious connections, we use a group-constrained sparse regression model to construct a backbone sparse network for each cluster and use it to weight the corresponding Pearson correlation network.

RESULTS

The proposed method outperforms the conventional static, temporally dependent fully connected correlation-based networks by at least 7% on a publicly available autism dataset. We were able to reproduce similar results using data from other centers.

CONCLUSIONS

By combining the advantages of temporal independence and group-constrained sparse regression, our method improves autism diagnosis.

Theory of Mind Indexes the Broader Autism Phenotype in Siblings of Children with Autism at School Age

Subclinical variants of the social-communicative challenges and rigidity that define autism spectrum disorder (ASD) are known as the broader autism phenotype (BAP). The BAP has been conceptualized categorically (as specific to a subset of relatives of individuals with ASD) and dimensionally (as continuously distributed within the general population). The current study examined the compatibility of these two approaches by assessing associations among autism symptoms and social-communicative skills in young school-age children with ASD, children who have a sibling with ASD, and children without a sibling with ASD. Autism symptoms were associated with reduced Theory of Mind (ToM), adaptive skills, cognitive empathy, and language skills across the full sample. Reduced ToM was a core aspect of the BAP in the current sample regardless of whether the BAP was defined categorically (in terms of siblings of children with ASD who exhibited atypical developmental) or dimensionally (in terms of associations with autism symptoms across the entire sample). Early language skills predicted school-age ToM. Findings support the compatibility of categorical and dimensional approaches to the BAP, highlight reduced ToM as a core aspect of the school-age BAP, and suggest that narrative-based approaches to promoting ToM may be beneficial for siblings of children with ASD.

Applying machine learning to facilitate autism diagnostics: pitfalls and promises

Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead to misinformed conclusions. To illustrate this concern, the current paper critically evaluates and attempts to reproduce results from two studies (Wall et al. in Transl Psychiatry 2(4):e100, 2012a; PloS One 7(8), 2012b) that claim to drastically reduce time to diagnose autism using machine learning. Our failure to generate comparable findings to those reported by Wall and colleagues using larger and more balanced data underscores several conceptual and methodological problems associated with these studies. We conclude with proposed best-practices when using machine learning in autism research, and highlight some especially promising areas for collaborative work at the intersection of computational and behavioral science.

Classifying aging as a disease in the context of ICD-11

Aging is a complex continuous multifactorial process leading to loss of function and crystalizing into the many age-related diseases. Here, we explore the arguments for classifying aging as a disease in the context of the upcoming World Health Organization's 11th International Statistical Classification of Diseases and Related Health Problems (ICD-11), expected to be finalized in 2018. We hypothesize that classifying aging as a disease with a "non-garbage" set of codes will result in new approaches and business models for addressing aging as a treatable condition, which will lead to both economic and healthcare benefits for all stakeholders. Actionable classification of aging as a disease may lead to more efficient allocation of resources by enabling funding bodies and other stakeholders to use quality-adjusted life years (QALYs) and healthy-years equivalent (HYE) as metrics when evaluating both research and clinical programs. We propose forming a Task Force to interface the WHO in order to develop a multidisciplinary framework for classifying aging as a disease with multiple disease codes facilitating for therapeutic interventions and preventative strategies.

Comparing Diagnostic Outcomes of Autism Spectrum Disorder Using DSM-IV-TR and DSM-5 Criteria

Controversy exists regarding the DSM-5 criteria for ASD. This study tested the psychometric properties of the DSM-5 model and determined how well it performed across different gender, IQ, and DSM-IV-TR sub-type, using clinically collected data on 227 subjects (median age = 3.95 years, majority had IQ > 70). DSM-5 was psychometrically superior to the DSM-IV-TR model (Comparative Fit Index of 0.970 vs 0.879, respectively). Measurement invariance revealed good model fit across gender and IQ. Younger children tended to meet fewer diagnostic criteria. Those with autistic disorder were more likely to meet social communication and repetitive behaviors criteria (p < .001) than those with PDD-NOS. DSM-5 is a robust model but will identify a different, albeit overlapping population of individuals compared to DSM-IV-TR.

Insistence on sameness relates to increased covariance of gray matter structure in autism spectrum disorder

Background

Autism spectrum disorder (ASD) is characterized by atypical development of cortical and subcortical gray matter volume. Subcortical structural changes have been associated with restricted and repetitive behavior (RRB), a core component of ASD. Behavioral studies have identified insistence on sameness (IS) as a separable RRB dimension prominent in high-functioning ASD, though no simple brain-behavior relationship has emerged. Structural covariance, a measure of morphological coupling among brain regions using magnetic resonance imaging (MRI), has proven an informative measure of anatomical relationships in typical development and neurodevelopmental disorders. In this study, we use this measure to characterize the relationship between brain structure and IS.

Methods

We quantified the structural covariance of cortical and subcortical gray matter volume in 55 individuals with high-functioning ASD using 3T MRI. We then related these structural metrics to individual IS scores, as assessed by the Repetitive Behavior Scale-Revised (RBS-R).

Results

We found that increased coupling among subcortical regions and between subcortical and cortical regions related to greater IS symptom severity. Most pronounced, the striatum and amygdala participated in a plurality of identified relationships, indicating a central role for these structures in IS symptomatology. These structural associations were specific to IS and did not relate to any of the other RRB subcomponents measured by the RBS-R.

Conclusions

This study indicates that behavioral dimensions in ASD can relate to the coordination of development across multiple brain regions, which might be otherwise obscured using typical brain-behavior correlations. It also expands the structures traditionally related to RRB in ASD and provides neuroanatomical evidence supportive of IS as a separate RRB dimension.

Trial registration

ClinicalTrials.gov NCT01031407

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-015-0047-7) contains supplementary material, which is available to authorized users.

Neuroanatomy of Individual Differences in Language in Adult Males with Autism

One potential source of heterogeneity within autism spectrum conditions (ASC) is language development and ability. In 80 high-functioning male adults with ASC, we tested if variations in developmental and current structural language are associated with current neuroanatomy. Groups with and without language delay differed behaviorally in early social reciprocity, current language, but not current autistic features. Language delay was associated with larger total gray matter (GM) volume, smaller relative volume at bilateral insula, ventral basal ganglia, and right superior, middle, and polar temporal structures, and larger relative volume at pons and medulla oblongata in adulthood. Despite this heterogeneity, those with and without language delay showed significant commonality in morphometric features when contrasted with matched neurotypical individuals (n = 57). In ASC, better current language was associated with increased GM volume in bilateral temporal pole, superior temporal regions, dorsolateral fronto-parietal and cerebellar structures, and increased white matter volume in distributed frontal and insular regions. Furthermore, current language-neuroanatomy correlation patterns were similar across subgroups with or without language delay. High-functioning adult males with ASC show neuroanatomical variations associated with both developmental and current language characteristics. This underscores the importance of including both developmental and current language as specifiers for ASC, to help clarify heterogeneity.

Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks

Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best single-parameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis.

Electromyographic responses to emotional facial expressions in 6-7 year olds with autism spectrum disorders

This study aimed to examine facial mimicry in 6-7 year old children with autism spectrum disorder (ASD) and to explore whether facial mimicry was related to the severity of impairment in social responsiveness. Facial electromyographic activity in response to angry, fearful, sad and happy facial expressions was recorded in twenty 6-7 year old children with ASD and twenty-seven typically developing children. Even though results did not show differences in facial mimicry between children with ASD and typically developing children, impairment in social responsiveness was significantly associated with reduced fear mimicry in children with ASD. These findings demonstrate normal mimicry in children with ASD as compared to healthy controls, but that in children with ASD the degree of impairments in social responsiveness may be associated with reduced sensitivity to distress signals.

[Screening interview for early detection of high-functioning autism spectrum disorders]

OBJECTIVE

Various different questionnaires are available for the screening of autism spectrum disorders (ASD). These screening instruments show high sensitivity and are able to identify a large number of individuals with ASD, but they lack the specificity to differentiate individuals with ASD from those children and adolescents with other complex neurobehavioural disorders (such as attention-deficit/hyperactivity disorder, emotional disorders, and others), especially for those without intellectual disabilities.

METHOD

The present study evaluates the data of 309 individuals (153 with high-functioning ASD, 156 with other psychiatric disorders, IQ > 70) to find out whether selected items of the ADI-R can be used for an economic and sensitive screening of high-functioning ASD.

RESULTS

The results show that 8 items of the ADI-R can be used to discriminate high-functioning ASD and other psychiatric disorders. A cutoff of 5 led to a sensitivity of 0.93 and a cutoff of 6 to a specificity of 0.74.

CONCLUSION

The combination of early onset, serious abnormalities in social contact with stereotyped or compulsive-ritualized behaviour or interests can be detected with few interview questions for screening of ASD. Nevertheless, a more detailed and specific assessment in an expert setting should follow the screening process.