A genotype-first approach to defining the subtypes of a complex disease.

Medical genetics typically entails the detailed characterization of a patient's phenotypes followed by genotyping to discover the responsible gene or mutation. Here, we propose that the systematic discovery of genetic variants associated with complex diseases such as autism are progressing to a point where a reverse strategy may be fruitful in assigning the pathogenic effects of many different genes and in determining whether particular genotypes manifest as clinically recognizable phenotypes. This "genotype-first" approach for complex disease necessitates the development of large, highly integrated networks of researchers, clinicians, and patient families, with the promise of improved therapies for subsets of patients.

The adult outcome of children referred for autism: typology and prediction from childhood.

BACKGROUND: Autism Spectrum Disorder is highly heterogeneous, no more so than in the complex world of adult life. Being able to summarize that complexity and have some notion of the confidence with which we could predict outcome from childhood would be helpful for clinical practice and planning. METHODS: Latent class profile analysis is applied to data from 123 participants from the Early Diagnosis Study (Lord et al., Archives of General Psychiatry, 2006, 63, 694) to summarize in a typology the multifacetted early adult outcome of children referred for autism around age 2. The form of the classes and their predictability from childhood is described. RESULTS: Defined over 15 measures, the adult outcomes were reduced to four latent classes, accounting for much of the variation in cognitive and functional measures but little in the affective measures. The classes could be well and progressively more accurately predicted from childhood IQ and symptom severity measurement taken at age 2 years to age 9 years. Removing verbal and nonverbal IQ and autism symptom severity measurement from the profile of adult measures did not change the number of the latent classes; however, there was some change in the class composition and they were more difficult to predict. CONCLUSIONS: While an empirical summary of adult outcome is possible, careful consideration needs to be given to the aspects that should be given priority. An outcome typology that gives weight to cognitive outcomes is well predicted from corresponding measures taken in childhood, even after account for prediction bias from fitting a complex model to a small sample. However, subjective well-being and affective aspects of adult outcome were weakly related to functional outcomes and poorly predicted from childhood.

Concordance between a U.S. Educational Autism Classification and the Autism Diagnostic Observation Schedule.

States in the United States differ in how they determine special education eligibility for autism services. Few states include an autism-specific diagnostic tool in their evaluation. In research, the Autism Diagnostic Observation Schedule (ADOS for first edition, ADOS-2 for second edition) is considered the gold-standard autism assessment. The purpose of this study was to estimate the proportion of children with an educational classification of autism who exceed the ADOS/ADOS-2 threshold for autism spectrum (concordance rate). Data were drawn from 4 school-based studies across 2 sites (Philadelphia, Pennsylvania, and San Diego, California). Participants comprised 627 children (2-12 years of age; 83% male) with an autism educational classification. Analyses included (a) calculating the concordance rate between educational and ADOS/ADOS-2 classifications and (b) estimating the associations between concordance and child's cognitive ability, study site, and ADOS/ADOS-2 administration year using logistic regression. More San Diego participants (97.5%, all assessed with the ADOS-2) met ADOS/ADOS-2 classification than did Philadelphia participants assessed with the ADOS-2 (92.2%) or ADOS (82.9%). Children assessed more recently were assessed with the ADOS-2; this group was more likely to meet ADOS/ADOS-2 classification than the group assessed longer ago with the ADOS. Children with higher IQ were less likely to meet ADOS/ADOS-2 classification. Most children with an educational classification of autism meet ADOS/ADOS-2 criteria, but results differ by site and by ADOS version and/or recency of assessment. Educational classification may be a reasonable but imperfect measure to include children in community-based trials.

Autism, spectrum or clusters? An EEG coherence study.

BACKGROUND: Autism prevalence continues to grow, yet a universally agreed upon etiology is lacking despite manifold evidence of abnormalities especially in terms of genetics and epigenetics. The authors postulate that the broad definition of an omnibus 'spectrum disorder' may inhibit delineation of meaningful clinical correlations. This paper presents evidence that an objectively defined, EEG based brain measure may be helpful in illuminating the autism spectrum versus subgroups (clusters) question. METHODS: Forty objectively defined EEG coherence factors created in prior studies demonstrated reliable separation of neuro-typical controls from subjects with autism, and reliable separation of subjects with Asperger's syndrome from all other subjects within the autism spectrum and from neurotypical controls. In the current study, these forty previously defined EEG coherence factors were used prospectively within a large (N = 430) population of subjects with autism in order to determine quantitatively the potential existence of separate clusters within this population. RESULTS: By use of a recently published software package, NbClust, the current investigation determined that the 40 EEG coherence factors reliably identified two distinct clusters within the larger population of subjects with autism. These two clusters demonstrated highly significant differences. Of interest, many more subjects with Asperger's syndrome fell into one rather than the other cluster. CONCLUSIONS: EEG coherence factors provide evidence of two highly significant separate clusters within the subject population with autism. The establishment of a unitary "Autism Spectrum Disorder" does a disservice to patients and clinicians, hinders much needed scientific exploration, and likely leads to less than optimal educational and/or interventional efforts.

Heritable genotype contrast mining reveals novel gene associations specific to autism subgroups.

Though the genetic etiology of autism is complex, our understanding can be improved by identifying genes and gene-gene interactions that contribute to the development of specific autism subtypes. Identifying such gene groupings will allow individuals to be diagnosed and treated according to their precise characteristics. To this end, we developed a method to associate gene combinations with groups with shared autism traits, targeting genetic elements that distinguish patient populations with opposing phenotypes. Our computational method prioritizes genetic variants for genome-wide association, then utilizes Frequent Pattern Mining to highlight potential interactions between variants. We introduce a novel genotype assessment metric, the Unique Inherited Combination support, which accounts for inheritance patterns observed in the nuclear family while estimating the impact of genetic variation on phenotype manifestation at the individual level. High-contrast variant combinations are tested for significant subgroup associations. We apply this method by contrasting autism subgroups defined by severe or mild manifestations of a phenotype. Significant associations connected 286 genes to the subgroups, including 193 novel autism candidates. 71 pairs of genes have joint associations with subgroups, presenting opportunities to investigate interacting functions. This study analyzed 12 autism subgroups, but our informatics method can explore other meaningful divisions of autism patients, and can further be applied to reveal precise genetic associations within other phenotypically heterogeneous disorders, such as Alzheimer's disease.

Neurodevelopmental Disorders (ASD and ADHD): DSM-5, ICD-10, and ICD-11.

Neurodevelopmental disorders, specifically autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) have undergone considerable diagnostic evolution in the past decade. In the United States, the current system in place is the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), whereas worldwide, the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) serves as a general medical system. This review will examine the differences in neurodevelopmental disorders between these two systems. First, we will review the important revisions made from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) to the DSM-5, with respect to ASD and ADHD. Next, we will cover the similarities and differences between ASD and ADHD classification in the DSM-5 and the ICD-10, and how these differences may have an effect on neurodevelopmental disorder diagnostics and classification. By examining the changes made for the DSM-5 in 2013, and critiquing the current ICD-10 system, we can help to anticipate and advise on the upcoming ICD-11, due to come online in 2017. Overall, this review serves to highlight the importance of progress towards complementary diagnostic classification systems, keeping in mind the difference in tradition and purpose of the DSM and the ICD, and that these systems are dynamic and changing as more is learned about neurodevelopmental disorders and their underlying etiology. Finally this review will discuss alternative diagnostic approaches, such as the Research Domain Criteria (RDoC) initiative, which links symptom domains to underlying biological and neurological mechanisms. The incorporation of new diagnostic directions could have a great effect on treatment development and insurance coverage for neurodevelopmental disorders worldwide.

Two kinds of autism: a comparison of distinct understandings of psychiatric disease.

In this article, I argue that the history and philosophy of autism need to account for two kinds of autism. Contemporary autism research and practice is structured, directed and connected by an 'ontological understanding of disease'. This implies that autism is understood as a disease like any other medical disease, existing independently of its particular manifestations in individual patients. In contrast, autism in the 1950s and 1960s was structured by a psychoanalytical framework and an 'individual understanding of disease'. This implied that autism was not a distinct disease but an idiosyncratic and meaningful response of the child to a disturbed development of the ego. These two kinds of autism are embedded in and reveal two very different 'styles of psychiatric thought'.

Potential social interactions are important to social attention.

Social attention, or how spatial attention is allocated to biologically relevant stimuli, has typically been studied using simplistic paradigms that do not provide any opportunity for social interaction. To study social attention in a complex setting that affords social interaction, we measured participants' looking behavior as they were sitting in a waiting room, either in the presence of a confederate posing as another research participant, or in the presence of a videotape of the same confederate. Thus, the potential for social interaction existed only when the confederate was physically present. Although participants frequently looked at the videotaped confederate, they seldom turned toward or looked at the live confederate. Ratings of participants' social skills correlated with head turns to the live, but not videotaped, confederate. Our results demonstrate the importance of studying social attention within a social context, and suggest that the mere opportunity for social interaction can alter social attention.

[Autism Spectrum Disorder in DSM-5 - concept, validity, and reliability, impact on clinical care and future research]. / Autismus-Spektrum Störung nach DSM-5. Konzeptualisierung validität und reliabilität, bedeutung für klinik, Versorgung und forschung.

Autism Spectrum Disorder (ASD) in DSM-5 comprises the former DSM-IV-TR diagnoses of Autistic Disorder, Asperger's Disorder and PDD-nos. The criteria for ASD in DSM-5 were considerably revised from those of ICD-10 and DSM-IV-TR. The present article compares the diagnostic criteria, presents studies on the validity and reliability of ASD, and discusses open questions. It ends with a clinical and research perspective.

[Autism spectrum syndrome replaces Asperger syndrome and autism]. / Autismspektrumsyndrom ersätter Aspergers syndrom och autism.

Autism spectrum disorder describes a behaviourally defined impairment in social interaction and communication, along with the presence of restricted interests and repetitive behaviours. Although the etiology is mostly unknown, it is evident that biological factors affect the brain and result in the autistic clinical presentation. Assessment for diagnosing autism spectrum disorder should be comprehensive in order to cover all sorts of problems related to the disorder. Knowledge and experience from working with neurological and psychiatric disorders are a prerequisite for quality in the examination. Up to now, there is no cure for autism spectrum disorder, but support and adaptations in education are nevertheless important for obtaining sufficient life quality for the patients and the family.

Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification.

In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models and delve into the detailed observation of certain features that previous literature has identified as prominent in the classification process. Our study employs a game-based tablet application to collect motor data. We use artificial neural networks to analyze raw trajectories in a "drag and drop" task. We compare a two-features model (utilizing only raw coordinates) with a four-features model (including velocities and accelerations). The aim is to assess the effectiveness of raw data analysis and determine the impact of acceleration on autism classification. Our results revealed that both models demonstrate promising accuracy in classifying motor trajectories. The four-features model consistently outperforms the two-features model, as evidenced by accuracy values (0.90 vs. 0.76). However, our findings support the potential of raw data analysis in objectively assessing motor behaviors related to autism. While the four-features model excels, the two-features model still achieves reasonable accuracy. Addressing limitations related to sample size and noise is essential for future research. Our study emphasizes the importance of integrating intelligent solutions to enhance and assist autism traditional diagnostic process and intervention, paving the way for more effective tools in assessing motor skills.

Toward a functional classification for autism in adulthood.

Autism spectrum disorder (ASD) is a heterogeneous condition that affects development and functioning from infancy through adulthood. Efforts to parse the heterogeneity of the autism spectrum through subgroups such as Asperger's and Profound Autism have been controversial, and have consistently struggled with issues of reliability, validity, and interpretability. Nonetheless, methods for successfully identifying clinically meaningful subgroups within autism are needed to ensure that research, interventions, and services address the range of needs experienced by autistic individuals. The purpose of this study was to generate and test whether a simple set of questions, organized in a flowchart, could be used in clinical practice and research to differentiate meaningful subgroups based on individuals' level of functioning. Once generated, subgroups could also be compared to the recently proposed administrative category of Profound Autism and to groupings based on standardized adaptive measures. Ninety-seven adults with autism or related neurodevelopmental disorders participating in a longstanding longitudinal study, or their caregivers if they could not answer for themselves, completed phone interviews when the participants were ~30 years old. Information from these phone interviews was used to generate vignettes summarizing characteristics and aspects of the daily lives of each participant (e.g., language level, vocational activities, and social relationships). Three expert clinicians then used these vignettes to classify each participant based on their level of support needs. Meaningfully distinct subgroups within the sample were identified which could be reliably distinguished from one another. Implications of such categorizations and future directions are discussed.

Social communication disorder outside autism? A diagnostic classification approach to delineating pragmatic language impairment, high functioning autism and specific language impairment.

BACKGROUND: Developmental disorders of language and communication present considerable diagnostic challenges due to overlapping of symptomatology and uncertain aetiology. We aimed to further elucidate the behavioural and linguistic profile associated with impairments of social communication occurring outside of an autism diagnosis. METHODS: Six to eleven year olds diagnosed with pragmatic language impairment (PLI), high functioning autism (HFA) or specific language impairment (SLI) were compared on measures of social interaction with peers (PI), restricted and repetitive behaviours/interests (RRBIs) and language ability. Odds ratios (OR) from a multinomial logistic regression were used to determine the importance of each measure to diagnostic grouping. MANOVA was used to investigate differences in subscale scores for the PI measure. RESULTS: Greater degrees of PI difficulties (OR = 1.22, 95% CI = 1.05-1.41), RRBI (OR = 1.23, 95% CI = 1.06-1.42) and expressive language ability (OR = 1.16, 95% CI = 1.03-1.30) discriminated HFA from PLI. PLI was differentiated from SLI by elevated PI difficulties (OR = 0.82, 95% CI = 0.70-0.96) and higher expressive language ability (OR = 0.88, 95% CI = 0.77-0.98), but indistinguishable from SLI using RRBI (OR = 1.01, 95% CI=0.94-1.09). A significant effect of group on PI subscales was observed (θ = 1.38, F(4, 56) = 19.26, p < .01) and PLI and HFA groups shared a similar PI subscale profile. CONCLUSIONS: Results provide empirical support for a conceptualisation of PLI as a developmental impairment distinguishable from HFA by absence of RRBIs and by the presence of expressive language difficulties. PI difficulties appear elevated in PLI compared with SLI, but may be less pervasive than in HFA. Findings are discussed with reference to the proposed new category of 'social communication disorder' in DSM-5.

Dentistry's intrinsic link to provision of services for persons with disabilities.

This article provides the dentist with a background on disabilities, education, practice and provision of services related to statutory and regulatory requirements, particularly the Americans with Disabilities Act anrid the California Fair Employment and Housing Act.

Autism researchers face off over language.

Terminology dispute underscores divide about what direction the field should take.

Nosology and epidemiology in autism: classification counts.

Since its initial description by Kanner in 1943, the criteria by which a diagnosis of autism or autism-like disorders was made--and their alleged etiologies portrayed--have undergone manifold changes, from a psychiatric disorder engendered by "refridgerator" parents to a neurodevelopmental disability produced in the main by genetic abnormalities. In addition, the behavioral characterization of autism has also entered the public consciousness and professional domains increasingly in the past 30 years, the effects of which we are continually coming to terms. A diagnosis of autism that once seemed quite unusual is now considered almost epidemic. Increasing numbers of individuals diagnosed with autism and related pervasive developmental disabilities will, in turn, affect the calculated prevalence of the disorder. In this essay, I attempt to account for the increasing prevalence of autism and autism-related disorders by examining its changing criteria, the individuals and instruments used to make the diagnosis, the reliability and validity of same, and the sample sizes and other aspects of the methodology needed to make an accurate estimate of its prevalence.

A twin study of autism symptoms in Sweden.

This study aimed to identify empirically the number of factors underlying autism symptoms-social impairments, communication impairments, and restricted repetitive behaviors and interests-when assessed in a general population sample. It also investigated to what extent these autism symptoms are caused by the same or different genetic and environmental influences. Autistic symptoms were assessed in a population-based twin cohort of >12,000 (9- and 12-year-old) children by parental interviews. Confirmatory factor analyses, principal component analyses and multivariate structural equation model fitting were carried out. A multiple factor solution was suggested, with nearly all analyses pointing to a three-factor model for both boys and girls and at both ages. A common pathway twin model fit the data best, which showed that there were some underlying common genetic and environmental influences across the different autism dimensions, but also significant specific genetic effects on each symptom type. These results suggest that the autism triad consists of three partly independent dimensions when assessed in the general population, and that these different autism symptoms, to a considerable extent, have partly separate genetic influences. These findings may explain the large number of children who do not meet current criteria for autism but who show some autism symptoms. Molecular genetic research may benefit from taking a symptom-specific approach to finding genes associated with autism.

Functional connectivity magnetic resonance imaging classification of autism.

Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 × 10(-7)). In subjects <20 years of age, the classifier performed at 89% accuracy (P = 5.4 × 10(-7)). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects <20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance >10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.