Pervasive developmental disorders are now commonly referred to as autism spectrum disorders (ASDs). ASDs present with a range of severity and impairments, and often are a cause of severe disability, representing a major public health concern. The diagnostic criteria require delays or abnormal functioning in social interaction, language, and/or imaginative play within the first 3 years of life, resulting in a deviation from the developmental pattern expected for the age. Because establishing a diagnosis of ASD is possible as early as 18-24 months of age, clinicians should strive to identify and begin intervention in children with ASD as soon as signs are manifest. Increasing efforts are underway to make ASD screening universal in pediatric healthcare. Given the crucial importance of early identification and multiple modalities of treatment for ASD, this review will summarize the diagnostic criteria, key areas for assessment by clinicians, specific scales and instruments for assessment, and discussion of evidence-based treatment programs and the role of specific drug therapies for symptom management.

BackgroundPoor eye contact and joint attention are early signs of autism spectrum disorder (ASD) and important prerequisites for developing other socio‐communicative skills. Teaching parents evidence‐based techniques to improve these skills can impact the overall functioning of children with ASD. We aimed to analyse the impact of conducting a group parent‐training intervention with video modelling to improve the intelligent quotient (IQ), social and communication functioning and to minimise symptoms in children with ASD and intellectual disability (ID).MethodsStudy design: A multicentre, single‐blinded, randomised clinical pilot trial of parent training using video modelling was conducted.Sample: Sixty‐seven parents of children with ASD, aged between 3 and 6 years and with IQs between 50 and 70, were randomised: 34 to the intervention group and 33 to the control group.Intervention program: The intervention group received parent training over 22 sessions, and the control group received the standard community treatment.Instruments: Pre‐evaluation and post‐evaluation (week 28), the following were used: Autism Diagnostic Interview, Vineland Adaptive Behaviour Scale I, Snijders‐Oomen Nonverbal Intelligence Test, Autism Behaviour Checklist and Hamilton Depression Rating Scale.Data Analysis: Intention to treat and complier‐average causal effect (CACE) were used to estimate the effects of the intervention.ResultsThere was a statistically significant improvement in the Vineland standardized communication scores in CACE (Cohen'sd = 0.260). There was a non‐statistically significant decrease in autism symptomatology (Autism Behaviour Checklist total scores) and a significant increase in the non‐verbal IQ in the intervention group. After the false discovery rate correction was applied, IQ remained statistically significant under both paradigms. The effect size for this adjusted outcome under the intention‐to‐treat paradigm was close to 0.4, and when considering adherence (CACE), the effect sizes were more robust (IQ's Cohen'sd = 0.433).ConclusionsParent training delivered by video modelling can be a useful technique for improving the care given to children with ASD and ID, particularly in countries that lack specialists.

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An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.

Although Autism Spectrum Disorders (ASD) is recognized as being heavily influenced by genetic factors, the role of epigenetic and environmental factors is still being established. This study aimed to identify ASD vulnerability components based on familial history and intrauterine environmental stress exposure, explore possible vulnerability subgroups, access DNA methylation age acceleration (AA) as a proxy of stress exposure during life, and evaluate the association of ASD vulnerability components and AA to phenotypic severity measures. Principal Component Analysis (PCA) was used to search the vulnerability components from 67 mothers of autistic children. We found that PC1 had a higher correlation with psychosocial stress (maternal stress, maternal education, and social class), and PC2 had a higher correlation with biological factors (psychiatric family history and gestational complications). Comparing the methylome between above and below PC1 average subgroups we found 11,879 statistically significant differentially methylated probes (DMPs, p < 0.05). DMPs CpG sites were enriched in variably methylated regions (VMRs), most showing environmental and genetic influences. Hypermethylated probes presented higher rates in different regulatory regions associated with functional SNPs, indicating that the subgroups may have different affected regulatory regions and their liability to disease explained by common variations. Vulnerability components score moderated by epigenetic clock AA was associated with Vineland Total score (p = 0.0036, adjR2 = 0.31), suggesting risk factors with stress burden can influence ASD phenotype.