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Purpose: A considerable number of screening and diagnostic tools for autism exist, but variability in these measures presents challenges to data harmonization and the comparability and generalizability of findings. At the same time, there is a movement away from autism symptomatology to stances that capture heterogeneity and appreciate diversity. The International Classification of Functioning, Disability and Health (ICF) provides a classification system that can support content harmonization of different screening and diagnostic tools for autism while enabling the translation of diagnostic information into functioning., Method: Here we linked commonly used screening and diagnostic measures within the field of autism to the ICF to facilitate the unification of data obtained from these measures., Results: As expected, screening and diagnostic measures primarily focus on body functions and activities and participation domains of the ICF, and much less on environmental factors, reflecting biomedical and adaptive behavior operationalizations of autism derived from diagnostic manuals., Conclusion: By translating symptomology-based information to the continuous and diagnostically neutral view of functioning, the ICF linking presented here may provide a means to harmonize measures of autism characteristics while enabling diagnostic information to be re-examined through a more neurodiversity-affirmative lens., (© 2023. The Author(s).)

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., (© 2024 The Author(s). Autism Research published by International Society for Autism Research and Wiley Periodicals LLC.)

Autism is heterogeneous, which complicates providing tailored support and future prospects. We aim to identify subgroups in autistic adults with average to high intelligence, to clarify if certain subgroups might need support. We included 14 questionnaire variables related to aging and/or autism (e.g., demographic, psychological, and lifestyle). Community detection analysis was used for subgroup identification in an original sample of 114 autistic adults with an adulthood diagnosis (autism) and 58 non-autistic adults as comparison group (COMP), and a replication sample (N Autism  = 261; N COMP  = 287), both aged 30-89 years. Next, we identified subgroups and assessed external validity (for cognitive and psychological difficulties, and quality of life [QoL]) in the autism samples. To test specificity, we repeated the analysis after adding 123 adults with ADHD, aged 30-80 years. As expected, the autism and COMP groups formed distinct subgroups. Among autistic adults, we identified three subgroups of which two were replicated. One of these subgroups seemed most vulnerable on the cluster variables; this subgroup also reported the most cognitive and psychological difficulties, and lowest QoL. Adding the ADHD group did not alter results. Within autistic adults, one subgroup could especially benefit from support and specialized care, although this must be tested in future studies., (© 2023. The Author(s).)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Luongo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., Competing Interests: CT, MT, DB, PB No competing interests declared, (© 2021, Tortelli et al.)

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.

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.

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., (© 2020 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.)

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.

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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., Competing Interests: ML, LE, AM, MD, CC, DC, EC No competing interests declared, KP An invention disclosure form was filed by KP with the University of California, San Diego, on March 5, 2010., (© 2019, Lombardo et al.)

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.

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.

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.

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 ( r s  = .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)., Competing Interests: Not applicable.The study protocol presented here received ethical approval from the Cardiff University School of Psychology Research Ethics Committee (EC.14.04.08.3784R2A3). All participants provided informed electronic consent before taking part in the study.Not applicableThe authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Schattauer GmbH.)

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., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

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.

Community-based studies have consistently shown a sex ratio heavily skewed towards males in autism spectrum disorders (ASD). The factors underlying this predominance of males are largely unknown, but the way girls score on standardized categorical diagnostic tools might account for the underrecognition of ASD in girls. Despite the existence of different norms for boys and girls with ASD on several major screening tests, the algorithm of the Autism Diagnosis Interview-Revised (ADI-R) has not been reformulated. The aim of our study was to investigate which ADI-R items discriminate between males and females, and to evaluate their weighting in the final diagnosis of autism. We then conducted discriminant analysis (DA) on a sample of 594 probands including 129 females with ASD, recruited by the Paris Autism Research International Sibpair (PARIS) Study. A replication analysis was run on an independent sample of 1716 probands including 338 females with ASD, recruited through the Autism Genetics Resource Exchange (AGRE) program. Entering the raw scores for all ADI-R items as independent variables, the DA correctly classified 78.9% of males and 72.9% of females (P < 0.001) in the PARIS cohort, and 72.2% of males and 68.3% of females (P < 0.0001) in the AGRE cohort. Among the items extracted by the stepwise DA, four belonged to the ADI-R algorithm used for the final diagnosis of ASD. In conclusion, several items of the ADI-R that are taken into account in the diagnosis of autism significantly differentiates between males and females. The potential gender bias thus induced may participate in the underestimation of the prevalence of ASD in females. Autism Res 2016,. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 680-689. © 2016 International Society for Autism Research, Wiley Periodicals, Inc., (© 2016 International Society for Autism Research, Wiley Periodicals, Inc.)

We investigated whether theory of mind skills can indicate autism spectrum disorder severity. In all, 62 children with autism spectrum disorder completed a developmentally sensitive theory of mind battery. We used intelligence quotient, Diagnostic and Statistical Manual of Mental Disorders (4th ed.) diagnosis and level of support needed as indicators of severity level. Using hierarchical cluster analysis, we found three distinct clusters of theory of mind ability: early-developing theory of mind (Cluster 1), false-belief reasoning (Cluster 2) and sophisticated theory of mind understanding (Cluster 3). The clusters corresponded to severe, moderate and mild autism spectrum disorder. As an indicator of level of support needed, cluster grouping predicted the type of school children attended. All Cluster 1 children attended autism-specific schools; Cluster 2 was divided between autism-specific and special needs schools and nearly all Cluster 3 children attended general special needs and mainstream schools. Assessing theory of mind skills can reliably discriminate severity levels within autism spectrum disorder.

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.

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'.

Background: A substantial proportion of Autism Spectrum Disorder (ASD) risk resides in de novo germline and rare inherited genetic variation. In particular, rare copy number variation (CNV) contributes to ASD risk in up to 10% of ASD subjects. Despite the striking degree of genetic heterogeneity, case-control studies have detected specific burden of rare disruptive CNV for neuronal and neurodevelopmental pathways. Here, we used machine learning methods to classify ASD subjects and controls, based on rare CNV data and comprehensive gene annotations. We investigated performance of different methods and estimated the percentage of ASD subjects that could be reliably classified based on presumed etiologic CNV they carry., Results: We analyzed 1,892 Caucasian ASD subjects and 2,342 matched controls. Rare CNVs (frequency 1% or less) were detected using Illumina 1M and 1M-Duo BeadChips. Conditional Inference Forest (CF) typically performed as well as or better than other classification methods. We found a maximum AUC (area under the ROC curve) of 0.533 when considering all ASD subjects with rare genic CNVs, corresponding to 7.9% correctly classified ASD subjects and less than 3% incorrectly classified controls; performance was significantly higher when considering only subjects harboring de novo or pathogenic CNVs. We also found rare losses to be more predictive than gains and that curated neurally-relevant annotations (brain expression, synaptic components and neurodevelopmental phenotypes) outperform Gene Ontology and pathway-based annotations., Conclusions: CF is an optimal classification approach for case-control rare CNV data and it can be used to prioritize subjects with variants potentially contributing to ASD risk not yet recognized. The neurally-relevant annotations used in this study could be successfully applied to rare CNV case-control data-sets for other neuropsychiatric disorders.

Objective: The relationship between sex/gender differences and autism has attracted a variety of research ranging from clinical and neurobiological to etiological, stimulated by the male bias in autism prevalence. Findings are complex and do not always relate to each other in a straightforward manner. Distinct but interlinked questions on the relationship between sex/gender differences and autism remain underaddressed. To better understand the implications from existing research and to help design future studies, we propose a 4-level conceptual framework to clarify the embedded themes., Method: We searched PubMed for publications before September 2014 using search terms "'sex OR gender OR females' AND autism." A total of 1,906 articles were screened for relevance, along with publications identified via additional literature reviews, resulting in 329 articles that were reviewed., Results: Level 1, "Nosological and diagnostic challenges," concerns the question, "How should autism be defined and diagnosed in males and females?" Level 2, "Sex/gender-independent and sex/gender-dependent characteristics," addresses the question, "What are the similarities and differences between males and females with autism?" Level 3, "General models of etiology: liability and threshold," asks the question, "How is the liability for developing autism linked to sex/gender?" Level 4, "Specific etiological-developmental mechanisms," focuses on the question, "What etiological-developmental mechanisms of autism are implicated by sex/gender and/or sexual/gender differentiation?", Conclusions: Using this conceptual framework, findings can be more clearly summarized, and the implications of the links between findings from different levels can become clearer. Based on this 4-level framework, we suggest future research directions, methodology, and specific topics in sex/gender differences and autism., (Copyright © 2015 American Academy of Child & Adolescent Psychaitry. Published by Elsevier Inc. All rights reserved.)

The use of autism as a diagnostic category guiding translational research is fraught with so many problems that the validity of research conclusions is suspect. Neuroscientists would benefit from attending to nosological difficulties to formulate meaningful research bridging basic biological systems and human disease. I propose a diagnostic schema that could translate more efficiently between the clinical and the neuroscience perspective as a step to improve the effectiveness of this type of research., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The changes in the diagnostic classification of the pervasive developmental disorders from the 4th edition of the Diagnostic and Statistical Manual for Mental Disorders (DSM-IV) to DSM-5 are expected to affect patients with autism, their families, as well as clinicians and researchers in the field of autism. This article reviews the new DSM-5 diagnostic criteria for Autism Spectrum Disorder (ASD) and Social Communication Disorder (SCD), and discusses potential consequences in the perspectives of major stakeholders., (Copyright © 2014 Elsevier B.V. All rights reserved.)

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.

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.

Purpose: To verify if there are characteristic behaviors of the different diagnosis included in the autism spectrum according to the Differential Assessment of Autism and Other Developmental Disorders (DAADD) and to the Autism Behavior Checklist (ABC)., Method: Participants were 45 individuals and their respective speech-language therapists. All therapists are graduate students working with the children for at least 1 year. This time was considered sufficient to the therapists to have the information required by the DAADD questionnaire. It is comprised by 3 protocols specifically designed to children with 2 to 4 years, 4 to 6 years and 6 to 8 years, the same criteria used to separate the research groups, G1, G2 and G3, respectively. Data referring to the ABC were retrieved from the subject's files at the Laboratório de Investigação Fonoaudiológica nos Distúrbios do Espectro do Autismo (Research Laboratory on Language Disorders in the Autism Spectrum) of the School of Medicine, Universidade de São Paulo, where it is routinely applied during the annual assessment., Results: Answers to the different areas of DAADD are similar to the different areas of ABC. These data show data the diagnosis by DAADD is easier in older children. Although there is no significant difference, the large occurrence of Rett's syndrome diagnosis according to the DAADD was associated to higher risk for autism according to the ABC in G1. With increasing age this tendency decreases and either in G2 and G3 Autism is the most frequent diagnosis., Conclusion: Although the results of both questionnaires tend to agree more with increasing age, the DAADD is more sensitive in the different ages while the ABC if more specific only to older children.

Purpose of Review: In the lead up to and following the recent publication of the DSM-5, the diagnostic construct of autism has received intense scrutiny., Recent Findings: This article briefly reviews the history of the diagnosis of autism, the changes that have occurred in the diagnosis over time, and the rationale for change. The most significant changes being introduced with the DSM-5 are highlighted, as well as some of the concerns that will be a focus of attention with respect to the potential impacts going forward., Summary: The categorical divisions that characterized the pervasive developmental disorders are now collapsed into a single entity, autism spectrum disorder. The final DSM-5 criteria have yet to be formally compared prospectively against prior criteria, but early indications suggest that the boundaries around the pervasive developmental disorders have not been substantially altered.

This article considers the fate of Asperger's disorder in the light of proposals for the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) to collapse Asperger's disorder along with other pervasive developmental disorders into a general spectrum of autism. It is argued that a powerful lay and scientific culture has evolved around the concept of Asperger's disorder, which has found a particularly compelling voice over the last decade in the online Asperger community, with websites such as Wrong Planet recruiting tens of thousands of members. In order to assess the impact of these proposed changes on the online Asperger community, 19 threads on the topic of Diagnostic and Statistical Manual of Mental Disorders (5th ed.) are analysed according to the arguments that community members put forward in favour of, or in opposition to, the proposals. Many members embrace the notion of the spectrum and have already coined a new identity - 'spectrumite' - to adapt to the diagnostic shift. Others, however, are suspicious of the motives behind the absorption of Asperger's disorder, and potential threats to the provision of services as well as the strong 'aspie' identity that reflects the large literature and the online public sphere around Asperger's disorder. To what extent this culture poses a challenge to the authority of the Diagnostic and Statistical Manual of Mental Disorders is uncertain at present.

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., (Copyright © 2014 Elsevier Inc. All rights reserved.)

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., (© 2013 The Authors Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.)

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.

Changes in the criteria for autism spectrum disorders in the new Diagnostic and Statistical Manual, fifth edition (DSM-5), have raised concerns among both professionals and the public. This commentary reviews these changes and their implications for families and clinicians.

New research findings provide major challenges regarding our understanding of the concept of autism. These are critically discussed in relation to research relevant to classification, genetics, environmental risk factors, gene-environment interplay, animal models, biomarkers, clinical features, neuropathology, pharmacotherapy, behavioral treatments, and functioning in adult life. It is concluded that, although there have been major research advances; there is a need for a reconceptualization and an avoidance of claims that go beyond the evidence.

Methods to assess recollection and familiarity separately in autism spectrum disorder were recently developed and piloted (Bigham et al. in J Autism Dev Disord 40:878-889, 2010). The preliminary data obtained via these methods showed that whereas recollection was mildly impaired in high functioning autism, familiarity was spared. The current study set out to replicate the methods of assessment for recollection and familiarity devised by Bigham and her colleagues with individuals diagnosed with low functioning autism (LFA). Three critical modifications to the original paradigms were made within the current study. The modifications and implications of the findings for individuals with LFA will be discussed.

Multiple Complex Developmental Disorder (MCDD) is a well-defined and validated behavioral subtype of autism with a proposed elevated risk of developing a schizophrenic spectrum disorder. The current study investigated whether children with MCDD show the same deficits in sensory gating that are commonly reported in schizophrenia, or whether they are indistinguishable from children with autism in this respect. P50 suppression and prepulse inhibition (PPI) of the startle reflex were assessed in children with MCDD (n=14) or autism (n=13), and healthy controls (n=12), matched on age and IQ. All subjects showed high levels of PPI and P50 suppression. However, no group differences were found. No abnormalities in sensory filtering could be detected in children with autism or MCDD. Since sensory gating deficits are commonly regarded as possible endophenotypic markers for schizophrenia, the current results do not support a high level of similarity between schizophrenia and MCDD., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

The Broad Autism Phenotype Questionnaire (BAPQ) was administered to a large community-based sample of biological parents of children with autism (PCAs) and comparison parents (CPs) (n = 1,692). Exploratory factor analysis and internal consistency parameters confirmed a robust three-factor structure of the BAPQ, corresponding to the proposed aloof, pragmatic language and rigidity subscales. Based upon the distribution of Broad Autism Phenotype (BAP) features in the general population, new normative cutoff values for BAPQ subscales were established that provide increased specificity relative to those previously reported, and thus enhance the utility of the BAPQ for diagnostically classifying the BAP. These cutoffs were also used to estimate prevalence of the BAP and its three components, with rates ranging between 14-23% for PCAs and between 5-9% for CPs. Analysis of patterns of BAP characteristics within family members revealed that BAP features were more likely to co-occur in PCAs relative to CPs. Collectively, these findings extend the utility of the BAPQ and provide additional evidence that it is an efficient and reliable tool for disaggregating the heterogeneity of autism through the identification of meaningful subgroups of parents. Autism Res 2013, 6: 134-143. © 2013 International Society for Autism Research, Wiley Periodicals, Inc., (© 2013 International Society for Autism Research, Wiley Periodicals, Inc.)

The article presents the Polish version of the autism diagnostic observation schedule-generic (ADOS), which together with the autism diagnostic interview-revised (ADI-R) is cited as the "gold standard" for the diagnosis of autism. The ADOS is a standardised, semistructured observation protocol appropriate for children and adults of differing age and language levels. It is linked to ICD-10 and DSM-IV-TR criteria. The ADOS consists of four modules, ranging from module 1 for nonverbal individuals to module 4 for verbally fluent adults. The adequate inter-rater reliability for items has been established. The protocol has high discriminant validity and distinguishes children with pervasive developmental disorders from children, who are outside of the spectrum. Although it does not enable to distinguish individuals with pervasive developmental disorder, unspecified from individuals with childhood autism. The paper presents subsequent steps of the translation process of the original version into Polish, as well as a chosen adaptation strategy of the Polish version. The ADOS is a very useful tool both for clinical diagnosis and for the scientific purpose diagnosis. In this last case it is extremely important to use a standardised method. Until now, there was no standardised diagnostic tool for autism in Poland.