Autism researchers face off over language.

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

Accurate assessment of low-function autistic children based on EEG feature fusion.

Autism spectrum disorder (ASD) is a very serious neurodevelopmental disorder and diagnosis mainly depends on the clinical scale, which has a certain degree of subjectivity. It is necessary to make accurate evaluation by objective indicators. In this study, we enrolled 96 children aged from 3 to 6 years: 48 low-function autistic children (38 males and 10 females; mean±SD age: 4.9±1.1 years) and 48 typically developing (TD) children (38 males and 10 females; mean±SD age: 4.9 ± 1.2 years) to participate in our experiment. We investigated to fuse multi-features (entropy, relative power, coherence and bicoherence) to distinguish low-function autistic children and TD children accurately. Minimum redundancy maximum correlation algorithm was used to choose the features and support vector machine was used for classification. Ten-fold cross validation was used to test the accuracy of the model. Better classification result was obtained. We tried to provide a reliable basis for clinical evaluation and diagnosis for ASD.

Reflexiones y debates sobre el autismo / Reflections and debates on autism

El artículo plantea algunas reflexiones sobre el lugar del autismo en las diversas clasificaciones psiquiátricas y sobre ciertos posicionamientos que tratan de sacarlo de ellas por juzgarlas estigmatizadoras. Señala ciertas tendencias sociales e ideológicas que afectan a la psiquiatría y que influyen en la creación, supresión y modificaciones de sus diagnósticos, cuestionando su legitimidad para imponerlos. Describe, desde una perspectiva psicodinámica y estructural, aspectos de la psicopatología clínica y propone una comprensión integradora de la etiopatogenia del autismo y del denominado “neurodesarrollo”. Postula el valor epistemológico de una psicopatología psicodinámica para una clasificación de los funcionamientos autísticos que tenga en cuenta tanto su estructura psíquica como sus variantes clínicas, así como su utilidad para conducir a intervenciones psicoterapéuticas basadas en una comprensión que debe producirse en un marco relacional aceptado por los afectados y sus familiares. Debate sobre las características individuales y excepcionales de este tipo de tratamientos, y sobre las dificultades de poder evaluarlos conforme a las necesarias exigencias metodológicas actuales y las posibilidades de reproducirlos de manera uniforme y generalizada. (AU)

This article brings up some reflections on the place of autism in different psychiatric classifications and on some stances that intend to exclude it from them because it is considered stigmatizing. It points out some social and ideological tendencies that have an impact on psychiatry and influence in the creation, suppression, and modifications of its diagnoses, calling into question its legitimacy to impose them. It describes, from a psychodynamic and structural perspective, some aspects of clinical psychopathology and proposes an integrative understanding of the etiopathogeny of autism and the so-called “neurodevelopment”. It postulates the epistemological value of a psychodynamic psychopathology for a classification of autistic functioning that takes into consideration both its psychic structure and its clinical variants, as well as the usefulness to carry out psychotherapeutic interventions based on an understanding that must take place in a relational framework accepted by both the affected and their families. It discusses the individual and exceptional characteristics of these kinds of treatments, the difficulties to assess them according to the current methodological demands, and the possibilities to reproduce them in a uniform and generalized way. (AU)

Objective pupillometry shows that perceptual styles covary with autistic-like personality traits.

We measured the modulation of pupil size (in constant lighting) elicited by observing transparent surfaces of black and white moving dots, perceived as a cylinder rotating about its vertical axis. The direction of rotation was swapped periodically by flipping stereo-depth of the two surfaces. Pupil size modulated in synchrony with the changes in front-surface color (dilating when black). The magnitude of pupillary modulation was larger for human participants with higher Autism-Spectrum Quotient (AQ), consistent with a local perceptual style, with attention focused on the front surface. The modulation with surface color, and its correlation with AQ, was equally strong when participants passively viewed the stimulus. No other indicator, including involuntary pursuit eye movements, covaried with AQ. These results reinforce our previous report with a similar bistable stimulus (Turi, Burr, & Binda, 2018), and go on to show that bistable illusory motion is not necessary for the effect, or its dependence on AQ.

Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model.

OBJECTIVE: Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity. METHODS: Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD: Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112; ABIDE-II, N=1,114; ADHD-200 sample: N=776; OCD local institutional database: N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders. RESULTS: Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively. CONCLUSIONS: The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.

Brain MRI analysis using a deep learning based evolutionary approach.

Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional convolutional neural network (3D-CNN) is employed to classify brain MRI scans into two predefined groups. In addition, a genetic algorithm based brain masking (GABM) method is proposed as a visualization technique that provides new insights into the function of the 3D-CNN. The proposed GABM method consists of two main steps. In the first step, a set of brain MRI scans is used to train the 3D-CNN. In the second step, a genetic algorithm (GA) is applied to discover knowledgeable brain regions in the MRI scans. The knowledgeable regions are those areas of the brain which the 3D-CNN has mostly used to extract important and discriminative features from them. For applying GA on the brain MRI scans, a new chromosome encoding approach is proposed. The proposed framework has been evaluated using ADNI (including 140 subjects for Alzheimer's disease classification) and ABIDE (including 1000 subjects for Autism classification) brain MRI datasets. Experimental results show a 5-fold classification accuracy of 0.85 for the ADNI dataset and 0.70 for the ABIDE dataset. The proposed GABM method has extracted 6 to 65 knowledgeable brain regions in ADNI dataset (and 15 to 75 knowledgeable brain regions in ABIDE dataset). These regions are interpreted as the segments of the brain which are mostly used by the 3D-CNN to extract features for brain disease classification. Experimental results show that besides the model interpretability, the proposed GABM method has increased final performance of the classification model in some cases with respect to model parameters.

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.

Changes in Autism Nosology: The Social Impact of the Removal of Asperger's Disorder from the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition (DSM-5).

This study examined the perception of an ASD label compared to Asperger's syndrome or no diagnosis. Seventy-one undergraduates read an adapted vignette (Ohan et al. J Autism Dev Disord 45:3384-3389, 2015) about an undergraduate with ASD, Asperger's Syndrome, or No Diagnosis. Participants also completed questionnaires. More positive ratings emerged for the Asperger's and ASD labels than No Diagnosis in low contact scenarios, particularly when involving greater social versus professional interaction. In contrast, more positive ratings emerged for the Asperger's compared to the ASD and No Diagnosis on high contact items. Ratings between low and high contact items differed only for ASD. Results demonstrate the impact of diagnostic labels across social contexts and support the need for education surrounding changes in nosology.

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.

Default mode-visual network hypoconnectivity in an autism subtype with pronounced social visual engagement difficulties.

Social visual engagement difficulties are hallmark early signs of autism (ASD) and are easily quantified using eye tracking methods. However, it is unclear how these difficulties are linked to atypical early functional brain organization in ASD. With resting state fMRI data in a large sample of ASD toddlers and other non-ASD comparison groups, we find ASD-related functional hypoconnnectivity between 'social brain' circuitry such as the default mode network (DMN) and visual and attention networks. An eye tracking-identified ASD subtype with pronounced early social visual engagement difficulties (GeoPref ASD) is characterized by marked DMN-occipito-temporal cortex (OTC) hypoconnectivity. Increased DMN-OTC hypoconnectivity is also related to increased severity of social-communication difficulties, but only in GeoPref ASD. Early and pronounced social-visual circuit hypoconnectivity is a key underlying neurobiological feature describing GeoPref ASD and may be critical for future social-communicative development and represent new treatment targets for early intervention in these individuals.

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.

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.

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.

Assessing subtypes of restricted and repetitive behaviour using the Adult Repetitive Behaviour Questionnaire-2 in autistic adults.

Background: The majority of previous research into restricted and repetitive behaviours (RRBs) has focussed on children, partly due to a lack of suitable measures for RRBs in adults. This study aimed to explore the psychometric properties of the Adult Repetitive Behaviour Questionnaire-2 (RBQ-2A) in a large sample of autistic adults using a self-report questionnaire method. Methods: The RBQ-2A and Autism-Spectrum Quotient (AQ) were administered online. Data from 275 autistic adults aged 18-66 (M = 36.56, SD = 12.24; 100 men and 171 women) were analysed using polychoric principal components analysis (PCA). Reliability and validity were assessed using Cronbach's alpha and correlation analyses. Results: PCA resulted in two components of the RBQ-2A, interpretable as repetitive sensory and motor behaviours (RSMB) and insistence on sameness (IS). Both components showed acceptable internal consistency (α = .70 and .81 respectively) and were significantly moderately correlated with scores on the AQ (rs = .25 and .42). Participants' scores on IS were higher than their scores on RSMB. RSMB, but not IS, was negatively associated with age, particularly in older adults (≥ 50 years). There were no gender differences. Conclusions: The RBQ-2A is a reliable and valid self-report measure of RRBs in the present sample of autistic adults. As one of the few measures of RRBs aimed at adults, it is suitable for adults with the ability to read and complete a self-report questionnaire. Results build on previous work with children using the Repetitive Behaviour Questionnaire-2 (RBQ-2).

SSDOnt: An Ontology for Representing Single-Subject Design Studies.

BACKGROUND: Single-Subject Design is used in several areas such as education and biomedicine. However, no suited formal vocabulary exists for annotating the detailed configuration and the results of this type of research studies with the appropriate granularity for looking for information about them. Therefore, the search for those study designs relies heavily on a syntactical search on the abstract, keywords or full text of the publications about the study, which entails some limitations. OBJECTIVE: To present SSDOnt, a specific purpose ontology for describing and annotating single-subject design studies, so that complex questions can be asked about them afterwards. METHODS: The ontology was developed following the NeOn methodology. Once the requirements of the ontology were defined, a formal model was described in a Description Logic and later implemented in the ontology language OWL 2 DL. RESULTS: We show how the ontology provides a reference model with a suitable terminology for the annotation and searching of single-subject design studies and their main components, such as the phases, the intervention types, the outcomes and the results. Some mappings with terms of related ontologies have been established. We show as proof-of-concept that classes in the ontology can be easily extended to annotate more precise information about specific interventions and outcomes such as those related to autism. Moreover, we provide examples of some types of queries that can be posed to the ontology. CONCLUSIONS: SSDOnt has achieved the purpose of covering the descriptions of the domain of single-subject research studies.

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.

Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism.

BACKGROUND AND OBJECTIVE: The autism spectrum disorder (ASD) diagnosis requires a long and elaborate procedure. Due to the lack of a biomarker, the procedure is subjective and is restricted to evaluating behavior. Several attempts to use functional MRI as an assisting tool (as classifier) have been reported, but they barely reach an accuracy of 80%, and have not usually been replicated or validated with independent datasets. Those attempts have used functional connectivity and structural measurements. There is, nevertheless, evidence that not the topology of networks, but their temporal dynamics is a key feature in ASD. We therefore propose a novel MRI-based ASD biomarker by analyzing temporal brain dynamics in resting-state fMRI. METHODS: We investigate resting-state fMRI data from 2 independent datasets of adolescents: our in-house data (12 ADS, 12 controls), and the Leuven dataset (12 ASD, 18 controls, from Leuven university). Using independent component analysis we obtain relevant socio-executive resting-state networks (RSNs) and their associated time series. Upon these time series we extract wavelet coherence maps. Using these maps, we calculate our dynamics metric: time of in-phase coherence. This novel metric is then used to train classifiers for autism diagnosis. Leave-one-out cross validation is applied for performance evaluation. To assess inter-site robustness, we also train our classifiers on the in-house data, and test them on the Leuven dataset. RESULTS: We distinguished ASD from non-ASD adolescents at 86.7% accuracy (91.7% sensitivity, 83.3% specificity). In the second experiment, using Leuven dataset, we also obtained the classification performance at 86.7% (83.3% sensitivity, and 88.9% specificity). Finally we classified the Leuven dataset, with classifiers trained with our in-house data, resulting in 80% accuracy (100% sensitivity, 66.7% specificity). CONCLUSIONS: This study shows that change in the coherence of temporal neurodynamics is a biomarker of ASD, and wavelet coherence-based classifiers lead to robust and replicable results and could be used as an objective diagnostic tool for ASD.

Perfil comunicativo y de adaptación social en población infantil con trastornos del espectro autista: nuevo enfoque a partir de los criterios del DSM-5 / Communicative and social-adaptive profile in children with autism spectrum disorder: a new approach based on the DSM-5 criteria

Introducción. Tras la nueva clasificación diagnóstica del Manual diagnóstico y estadístico de los trastornos mentales, quinta edición (DSM-5), el trastorno del espectro autista (TEA) ha pasado a considerarse una categoría dimensional que engloba una serie de trastornos que antes se consideraban como entidades diferentes. La bibliografía previa ha mostrado perfiles comunicativos y lingüísticos diferentes en personas con estos trastornos, por lo que podrían encontrarse resultados contradictorios en los individuos que en la actualidad reciben un diagnóstico de TEA. Objetivo. Identificar los aspectos del lenguaje estructural (expresión y comprensión), interacción (pragmática) y adaptación social diferenciales en niños con diagnóstico de TEA de nivel 1 de apoyo y compararlos con niños con desarrollo típico. Sujetos y métodos. Diecisiete niños con síndrome de Asperger (según el DSM-IV-TR) y 20 niños con desarrollo típico de entre 7 y 12 años. Se ha equiparado el síndrome de Asperger del DSM-IV-TR con el TEA con nivel 1 de apoyo del DSM-5. Se ha evaluado la inteligencia, la comunicación y la adaptación social con medidas directas estandarizadas y medidas indirectas paternas. Resultados. Se han encontrado diferencias significativas en comprensión (comprensión de estructuras gramaticales; p = 0,025), interacción (cuestionario de capacidades y dificultades total; p = 0,001) y adaptación social (Children’s Communication Checklist-2 total; p = 0,001) entre los grupos. Conclusiones. Los participantes con TEA con nivel 1 muestran un nivel de inteligencia normal y buena expresividad estructural (sintaxis y semántica), lo que podría diferenciarlos sustancialmente, dada la gran heterogeneidad del trastorno, de otros niños que en la actualidad también reciben el diagnóstico de TEA. No obstante, nuestros participantes también presentan problemas de comprensión de estructuras gramaticales, interacción pragmática y adaptación social. Estas dificultades podrían relacionarse con posibles problemas emocionales y de socialización (AU)

Introduction. Following the adoption of the new international diagnosis classification from the Diagnostic and Statistical Manual (DSM-5), autism spectrum disorder (ASD) has been established as a dimensional category that includes other disorders that were previously considered as separate entities. Previous research has shown that some people with this disorder exhibit different communicative and linguistic profiles. Therefore, contradictory results could be found among people who receive the same diagnosis. Aim. To distinguish structural language aspects (expression and comprehension), interactive aspects (pragmatics), and social adaptation between children with an ASD-level 1 of support and children with typical development. Subjects and methods. Seventeen children with Asperger syndrome (according to the DSM-IV-TR), and 20 children with typical development between 7 and 12 years old. We have equated diagnosis of Asperger syndrome with ASD-level 1 of support. We have evaluated intelligence quotient, communication, and social adaptation with direct and indirect standardized parental scales. Results. We have found significant differences in comprehension (p = 0.025), interaction (p = 0.001), and social adaptation (p = 0.001) between the two groups. Conclusions. Subjects with ASD-level 1 of support demonstrate an average intelligence quotient, and good expressive structure (syntax and semantic level), which may be different from other children who receive the same diagnosis, due to the wide heterogeneity of the disorder. Nevertheless, our subjects have problems related to comprehension of grammar structure, pragmatics, and social adaptation. These difficulties could be related to emotional and social problems (AU)

Dysmorphism in Non-Syndromic Autism: A Cross-Sectional Study.

OBJECTIVE: To determine the effect of association of dysembryogenesis (manifested by presence of dysmorphic markers) on the developmental profile of autistic children. METHODS: 26 autistic children were classified into complex autism (if they had specific dysmorphic markers) or essential autism (in the absence of dysmorphic markers) using the Miles Autism Dysmorphology Measure (ADM). The developmental abilities (Griffith's Mental Development Scales) and the clinical severity (Childhood Autism Rating Scale) of both groups were compared. The prevalence of dysmorphic markers was also determined in 140 non-autistic controls. RESULTS: Children with complex autism had poorer development (General Quotient 29.4 vs 34.0, P=0.06) and earlier onset of autistic symptoms (18 vs 24 mo, P=0.05). Dysmorphic markers were significantly more in autistic children compared to normal children (27% vs 10%, P=0.002). CONCLUSION: Dysembryogenesis may contribute to the clinical heterogeneity of autistic children.