Diagnostic accuracy of the ADOS and ADOS-2 in clinical practice

Use of Telehealth in Autism Spectrum Disorder Assessment in Children: Evaluation of an Online Diagnostic Protocol Including the Brief Observation of Symptoms of Autism

The COVID-19 pandemic revealed the need to develop the field of remote assessment for autism spectrum disorders (ASD). The purpose of the study was to evaluate an online assessment protocol that includes the Brief Observation of Symptoms of Autism (BOSA). The online protocol consisting of BOSA and the Autism Diagnostic Interview-Revised (ADI-R) was administered by experienced examiners to 29 children with suspected ASD. The participants were then evaluated by clinical psychologists in a standard clinical setting using the Autism Diagnostic Observation Schedule-2 (ADOS-2) and other methods, and the ASD diagnosis was confirmed or ruled out. The results show substantial to moderate inter-rater agreement between the online and face-to-face raters with the value of Cohen's Kappa = 0.66 (P < 0.001); this corresponds with 79.8% agreement. The sensitivity of the protocol was approx. 94.7%, the specificity was 70%, the positive predictive value was 85.7%, and the negative predictive value was 87.5%. Further, direct false positive or false negative diagnostic conclusions based on the online protocol were absent when the possible conclusion of "I cannot decide" was included. The items B9 Showing, B10 Spontaneous Initiation of Joint Attention, B1 Unusual Eye Contact, B3 Facial Expressions Directed to Others, and C2 Imagination/Creativity were shown to be well observable in BOSA when related to ADOS-2 scoring. The results indicate that the protocol consisting of BOSA and ADI-R administered by an experienced examiner is a promising combination of tools for remote autism assessment.

Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis

Timely and unbiased evaluation of Autism Spectrum Disorder (ASD) is essential for providing lasting benefits to affected individuals. However, conventional ASD assessment heavily relies on subjective criteria, lacking objectivity. Recent advancements propose the integration of modern processes, including artificial intelligence-based eye-tracking technology, for early ASD assessment. Nonetheless, the current diagnostic procedures for ASD often involve specialized investigations that are both time-consuming and costly, heavily reliant on the proficiency of specialists and employed techniques. To address the pressing need for prompt, efficient, and precise ASD diagnosis, an exploration of sophisticated intelligent techniques capable of automating disease categorization was presented. This study has utilized a freely accessible dataset comprising 547 eye-tracking systems that can be used to scan pathways obtained from 328 characteristically emerging children and 219 children with autism. To counter overfitting, state-of-the-art image resampling approaches to expand the training dataset were employed. Leveraging deep learning algorithms, specifically MobileNet, VGG19, DenseNet169, and a hybrid of MobileNet-VGG19, automated classifiers, that hold promise for enhancing diagnostic precision and effectiveness, was developed. The MobileNet model demonstrated superior performance compared to existing systems, achieving an impressive accuracy of 100%, while the VGG19 model achieved 92% accuracy. These findings demonstrate the potential of eye-tracking data to aid physicians in efficiently and accurately screening for autism. Moreover, the reported results suggest that deep learning approaches outperform existing event detection algorithms, achieving a similar level of accuracy as manual coding. Users and healthcare professionals can utilize these classifiers to enhance the accuracy rate of ASD diagnosis. The development of these automated classifiers based on deep learning algorithms holds promise for enhancing the diagnostic precision and effectiveness of ASD assessment, addressing the pressing need for prompt, efficient, and precise ASD diagnosis.

Objective Measurement of Social Gaze and Smile Behaviors in Children with Suspected Autism Spectrum Disorder During Administration of the Autism Diagnostic Observation Schedule, 2nd Edition

Best practice for the assessment of autism spectrum disorder (ASD) symptom severity relies on clinician ratings of the Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2), but the association of these ratings with objective measures of children's social gaze and smiling is unknown. Sixty-six preschool-age children (49 boys, M = 39.97 months, SD = 10.58) with suspected ASD (61 confirmed ASD) were administered the ADOS-2 and provided social affect calibrated severity scores (SA CSS). Children's social gaze and smiling during the ADOS-2, captured with a camera contained in eyeglasses worn by the examiner and parent, were obtained via a computer vision processing pipeline. Children who gazed more at their parents (p = .04) and whose gaze at their parents involved more smiling (p = .02) received lower social affect severity scores, indicating fewer social affect symptoms, adjusted R2 = .15, p = .003.

A study of the effects of screen exposure on the neuropsychological development in children with autism spectrum disorders based on ScreenQ

PURPOSE

To investigate the relationship between multi-dimensional aspects of screen exposure and autistic symptoms, as well as neuropsychological development in children with ASD.

METHODS

We compared the ScreenQ and Griffiths Development Scales-Chinese Language Edition (GDS-C) of 636 ASD children (40.79 ± 11.45 months) and 43 typically developing (TD) children (42.44 ± 9.61 months). Then, we analyzed the correlations between ScreenQ and Childhood Autism Rating Scale (CARS), and GDS-C. We further used linear regression model to analyze the risk factors associated with high CARS total scores and low development quotients (DQs) in children with ASD.

RESULTS

The CARS of children with ASD was positively correlated with the ScreenQ total scores and "access, frequency, co-viewing" items of ScreenQ. The personal social skills DQ was negatively correlated with the "access, frequency, content, co-viewing and total scores" of ScreenQ. The hearing-speech DQ was negatively correlated with the "frequency, content, co-viewing and total scores" of ScreenQ. The eye-hand coordination DQ was negatively correlated with the "frequency and total scores" of ScreenQ. The performance DQ was negatively correlated with the "frequency" item of ScreenQ.

CONCLUSION

ScreenQ can be used in the study of screen exposure in children with ASD. The higher the ScreenQ scores, the more severe the autistic symptoms tend to be, and the more delayed the development of children with ASD in the domains of personal-social, hearing-speech and eye-hand coordination. In addition, "frequency" has the greatest impact on the domains of personal social skills, hearing-speech, eye-hand coordination and performance of children with ASD.

Effectiveness of IMPUTE ADT-1 mobile application in children with autism spectrum disorder: An interim analysis of an ongoing randomized controlled trial

Objectives

IMPUTE Inc., a software firm dedicated to healthcare technology, has developed a mobile medical application known as IMPUTE ADT-1 for children with autism spectrum disorder (ASD) based on the principle of applied behavior analysis.

Materials and Methods

The primary objective of this trial was to compare the efficacy of add-on treatment with IMPUTE ADT-1 in children with ASD aged two to six years as compared to standard care alone for 12 weeks (in terms of change in Autism Diagnostic Observation Schedule [ADOS-2] scores). The secondary objective of the study was to assess the compliance with IMPUTE ADT-1 among participants and also to evaluate the feedback of parents regarding IMPUTE ADT-1 at the end of 12 weeks. The application provides personalized programs tailored to each user's needs, and the program evolves based on the user's progress. It also utilizes face tracking, eye tracking, and body tracking to gather behavior-related information for each child and apply it in reinforcement learning employing artificial intelligence-based algorithms.

Results

Till the time of interim analysis, 37 and 33 children had completed 12-week follow-up in IMPUTE ADT-1 and control arm. At 12 weeks, as compared to baseline, change in social affect domain, repetitive ritualistic behavior domain, total ADOS-2 score, and ADOS-2 comparison score was better in the intervention group as compared to the control group (P < 0.001 for all). A total of 30 (81%), 28 (75%), and 29 (78%) caregivers in the IMPUTE ADT-1 group believed that the ADT-1 app improved their child's verbal skills, social skills, and reduced repetitive behavior, respectively.

Conclusion

IMPUTE ADT-1 mobile application has the efficacy to improve the severity of autism symptoms in children. Parents of these children also feel that the application is beneficial for improving the socialization and verbal communication of their children.

The impact of the physical activity intervention on sleep in children and adolescents with autism spectrum disorder: A systematic review and meta-analysis

Pharmacological treatments (i.e., melatonin) and non-pharmacological therapies (e.g., parent-based sleep education programs and behavioural interventions) have been found to result in improved sleep in children and adolescents with autism spectrum disorder (ASD). However, there are several limitations to these treatment approaches, including concerns about the possible side-effects and safety, high-cost and uncertainties of long-term effects. Physical activity (PA) intervention is a promising behavioural intervention that has received increasing attention. However, the effects of PA intervention on sleep are still unclear in this clinical group. This study aimed to synthesize available empirical studies concerning the effects of PA interventions on sleep in children and adolescents with ASD. Following PRISMA guidelines, seven electronic databases: APA PsychInfo, CINAHL Ultimate, ERIC, MEDLINE, PubMed, SPORTDiscus, and Web of Science, were searched from inception to March 2023. Randomized controlled trials/quasi-experimental designs with comparison groups were included. Initially, 444 articles were identified, 13 articles underwent systematic review, and 8 studies with control groups and sufficient statistical data were selected for meta-analysis. Compared to no-treatment control groups, PA interventions had a large positive effect on parent-reported general sleep problems, night awakenings, sleep resistance, sleep duration and actigraphy-assessed sleep efficiency in children and adolescents with ASD.

Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews

Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.

Breakdowns and repairs: Communication initiation and effectiveness in infants with and without an older sibling with autism

Infants initiate interactions to get their wants and needs met; but sometimes they are not effective in their communication and are misunderstood by caregivers. When this happens, they must recognize this breakdown in communication and attempt repairs. Experimental literature suggests that in neurotypically developing infants these skills develop during the first two years. However, little work has investigated communication breakdowns and repairs in populations of infants with known social communication difficulties (e.g., infants with an elevated likelihood for autism). Here we explored early social communication initiations, breakdowns, and repair strategies in naturalistic videos of 18-month-old infants (N = 64) with elevated likelihood (EL) for autism and other developmental delays (N = 49) and infants with population-level likelihood for autism (e.g., typical likelihood, TL, N = 15). EL infants, including those who later met criteria for autism (EL-AUT), initiated with caregivers, experienced breakdowns, and made repairs at similar rates to TL infants. However, the types of behaviors used differed, such that EL infants appeared to have a relative strength in making behavior regulation bids. EL-AUT infants used a large proportion of developmentally appropriate repair behaviors (i.e., addition and substitution), even though their repertoires of repair strategies were smaller. Additionally, EL-AUT infants produced a larger proportion of simplification repairs, which are less developmentally advanced and less helpful to interlocutors. Identifying patterns in how EL infants communicate with caregivers and capitalizing on their strengths could improve interventions focused on social communication.

Challenges and opportunities in the diagnosis and treatment of early-onset psychosis: a case series from the youth affective disorders clinic in Stockholm, Sweden

Early-onset psychosis is linked to adverse long-term outcomes, recurrent disease course, and prolonged periods of untreated illness; thus highlighting the urgency of improving early identification and intervention. This paper discusses three cases where initial emphasis on psychosocial treatments led to diagnostic and therapeutic delays: (1) a 15-year-old misdiagnosed with emotionally unstable personality disorder and autism, who improved on bipolar medication and antipsychotics; (2) another 15-year-old misdiagnosed with autism, who stabilized on lithium and antipsychotics, subsequently allowing for gender dysphoria evaluation; (3) a 9-year-old autistic boy incorrectly treated for ADHD, who recovered with appropriate antipsychotic treatment. These cases illuminate the vital importance of adhering to a diagnostic hierarchy, prioritizing diagnostic utility, and conducting longitudinal evaluations to facilitate early targeted treatment of psychotic symptoms in early-onset psychosis. Adherence to such strategies can minimize delays in managing early-onset psychosis and improve long-term prognoses.

Association between Autism Spectrum Disorder (ASD) and vision problems. A systematic review and meta-analysis

AIM

To conduct a systematic review and meta-analysis assessing whether vision and/or eye disorders are associated with Autism Spectrum Disorder (ASD).

METHOD

Based on a pre-registered protocol (PROSPERO: CRD42022328485), we searched PubMed, Web of Knowledge/Science, Ovid Medline, Embase and APA PsycINFO up to 5th February 2022, with no language/type of document restrictions. We included observational studies 1) reporting at least one measure of vision in people of any age with a diagnosis of ASD based on DSM or ICD criteria, or ADOS; or 2) reporting the prevalence of ASD in people with and without vision disorders. Study quality was assessed with the Appraisal tool for Cross-Sectional Studies (AXIS). Random-effects meta-analyses were used for data synthesis.

RESULTS

We included 49 studies in the narrative synthesis and 46 studies in the meta-analyses (15,629,159 individuals distributed across multiple different measures). We found meta-analytic evidence of increased prevalence of strabismus (OR = 4.72 [95% CI: 4.60, 4.85]) in people with versus those without ASD (non-significant heterogeneity: Q = 1.0545, p = 0.7881). We also found evidence of increased accommodation deficits (Hedge's g = 0.68 [CI: 0.28, 1.08]) (non-significant heterogeneity: Q = 6.9331, p = 0.0741), reduced peripheral vision (-0.82 [CI: -1.32, -0.33]) (non-significant heterogeneity: Q = 4.8075, p = 0.4398), reduced stereoacuity (0.73 [CI: -1.14, -0.31]) (non-significant heterogeneity: Q = 0.8974, p = 0.3435), increased color discrimination difficulties (0.69 [CI: 0.27,1.10]) (non-significant heterogeneity: Q = 9.9928, p = 0.1890), reduced contrast sensitivity (0.45 [CI: -0.60, -0.30]) (non-significant heterogeneity: Q = 9.9928, p = 0.1890) and increased retinal thickness (=0.29 [CI: 0.07, 0.51]) (non-significant heterogeneity: Q = 0.8113, p = 0.9918) in ASD.

DISCUSSION

ASD is associated with some self-reported and objectively measured functional vision problems, and structural alterations of the eye, even though we observed several methodological limitations in the individual studies included in our meta-analyses. Further research should clarify the causal relationship, if any, between ASD and problems of vision during early life.

PROSPERO REGISTRATION

CRD42022328485.

Conceptualization of the latent structure of autism: further evidence and discussion of dimensional and hybrid models

Autism spectrum disorder (ASD) might be conceptualized as an essentially dimensional, categorical, or hybrid model. Yet, current empirical studies are inconclusive and the latent structure of ASD has explicitly been examined only in a few studies. The aim of our study was to identify and discuss the latent model structure of behavioral symptoms related to ASD and to address the question of whether categories and/or dimensions best represent ASD symptoms. We included data of 2920 participants (1-72 years of age), evaluated with the Autism Diagnostic Observation Schedule (Modules 1-4). We applied latent class analysis, confirmatory factor analysis, and factor mixture modeling and evaluated the model fit by a combination of criteria. Based on the model selection criteria, the model fits, the interpretability as well as the clinical utility we conclude that the hybrid model serves best for conceptualization and assessment of ASD symptoms. It is both grounded in empirical evidence and in clinical usefulness, is in line with the current classification system (DSM-5) and has the potential of being more specific than the dimensional approach (decreasing false positive diagnoses).

Diagnostic assessment of autism in adults - current considerations in neurodevelopmentally informed professional learning with reference to ADOS-2

Services for the assessment and diagnosis of autism in adults have been widely criticized and there is an identified need for further research in this field. There is a call for diagnostic services to become more accessible, person-centered, neurodiversity affirming, and respectful. There is a need for workforce development which will increase capacity for diagnostic assessment and support for adults. ADOS-2 is a gold-standard diagnostic assessment tool for autism recommended in clinical guidelines. However, diagnostic procedures such as the ADOS-2 are rooted in the medical model and do not always sit comfortably alongside the neurodiversity paradigm or preferences of the autistic community. Training and educational materials need to account for the differences between these approaches and support clinicians to provide services which meet the needs of the adults they serve. The National Autism Implementation Team worked alongside ADOS-2 training providers to support clinicians in Scotland, to provide effective and respectful diagnostic assessment. The team engaged with clinicians who had attended ADOS training to identify areas of uncertainty or concern. Training materials were developed to support ADOS assessors to incorporate key principles including "nothing about us without us"; "difference not deficit"; "environment first"; "diagnosis matters," "language and mindsets matter"; and "a neurodevelopmental lens," to support the provision of neurodiversity affirming assessment practice. The National Autism Implementation Team also provided examples of actions which can be undertaken by clinicians to improve the assessment experience for those seeking a diagnosis. Training materials are based on research evidence, clinical experience, and the needs and wishes of autistic people.

Are there gender-based variations in the presentation of Autism amongst female and male children?

The Questionnaire for Autism Spectrum Conditions (Q-ASC; Attwood, Garnett & Rynkiewicz, 2011) is one of the few screening instruments that includes items designed to assess female-specific ASD-Level 1 traits. This study examined the ability of a modified version of the Q-ASC (Q-ASC-M; Ormond et al., 2018) to differentiate children with and without ASD-Level 1. Participants included 111 parents of autistic children and 212 parents of neurotypical children (5-12 years). Results suggested that the gendered behaviour, sensory sensitivity, compliant behaviours, imagination, and imitation subscales differentiated autistic females from neurotypical females. Compared to autistic males, autistic females had higher scores on gendered behaviour, sensory sensitivity, social masking, and imitation. Results are discussed in relation to early detection of autistic female children.

The relationship between screen time before bedtime and behaviors of preschoolers with autism spectrum disorder and the mediating effects of sleep

BACKGROUND

There are overlapping effects of screen time and sleep on children's behavior. The purpose of this study was to explore the relationship of screen time with behavior problems in children with autism spectrum disorder (ASD) and the probable mediating effects of sleep, in order to provide evidence for the need for clinical identification and intervention.

METHODS

A sample of 358 preschoolers with ASD were included. We investigated the children's basic characteristics of sex and age, ASD symptoms (ABC, CARS, and ADOS-2), neurodevelopment (GDS-C), sleep habits (CSHQ), and behavior (CBCL). Pearson correlation tests were used to determine the direct correlations among children's screen time, CBCL, and CSHQ. Linear regression analysis was used to explore whether screen time predicted total score of CBCL. Multi-step linear regression analysis was used to investigate the mediating effect of sleep on the relationship between screen time and total score of CBCL.

RESULTS

Screen time before bedtime was correlated with CBCL and CSHQ, which indicated that screen time before bedtime was correlated with sleep and behavior in children with ASD. Screen time before bedtime was a predictor of CBCL total score (indicating children's behavior), and CSHQ total score (indicating children's sleep habits) played a partial mediating role between screen time before bedtime and children's behavior.

CONCLUSION

Clinicians should support and educate parents of children with ASD, which should focus on managing screen time, especially screen time before bedtime.

Diagnostic Overshadowing in High-Functioning Autism: Mirtazapine, Buspirone, and Modified Cognitive Behavioral Therapy (CBT) as Treatment Options

Diagnostic overshadowing (DO) is identified as a contributor to the missed diagnosis of individuals with an autism spectrum disorder (ASD). It has been used predominantly in the scientific literature and clinical settings to describe a phenomenon where a person's symptoms and behaviors are attributed solely to their primary diagnosis, rather than being recognized due to co-occurring conditions. DO is seen across many developmental disorders; however, females with autism may have a more difficult time getting diagnosed than males with autism because traditional diagnostic criteria for autism are often based on research that has primarily focused on males with autism. Likewise, the efficacy of approved psychopharmacological like selective serotonin reuptake inhibitors (SSRIs) and cognitive behavioral therapy (CBT) in individuals with ASD is not well established. Amidst these challenges, it's imperative to underscore the need for screening these disorders and provide informed evidence-based treatment alternatives for shared decision-making. Mirtazapine has low but promising findings, though modified CBT has superior empirical support in the treatment of co-occurring conditions associated with ASD.

Genesis, modelling and methodological remedies to autism heterogeneity

Diagnostic criteria used in autism research have undergone a shift towards the inclusion of a larger population, paralleled by increasing, but variable, estimates of autism prevalence across clinical settings and continents. A categorical diagnosis of autism spectrum disorder is now consistent with large variations in language, intelligence, comorbidity, and severity, leading to a heterogeneous sample of individuals, increasingly distant from the initial prototypical descriptions. We review the history of autism diagnosis and subtyping, and the evidence of heterogeneity in autism at the cognitive, neurological, and genetic levels. We describe two strategies to address the problem of heterogeneity: clustering, and truncated-compartmentalized enrollment strategy based on prototype recognition. The advances made using clustering methods have been modest. We present an alternative, new strategy for dissecting autism heterogeneity, emphasizing incorporation of prototypical samples in research cohorts, comparison of subgroups defined by specific ranges of values for the clinical specifiers, and retesting the generality of neurobiological results considered to be acquired from the entire autism spectrum on prototypical cohorts defined by narrow specifiers values.

Adequate Management of Autism Spectrum Disorder in Children in India

Despite the significant burden of neurodevelopmental disorders such as autism spectrum disorder (ASD) in India, there are areas of unmet needs at every level of the health care system. This includes screening and recognition, reliable and valid tools to evaluate, and to adequately manage ASD. There are also gaps in education and training of medical professionals, paraprofessionals, special education teachers and the related services. Lack of public awareness and cultural factors contribute to delays in early recognition and interventions. A framework is suggested to address these unmet needs at various levels to improve the care of these children with ASD. These include a) a focus on education of medical professionals, paraprofessionals, and teachers; b) setting up infrastructure at community, regional, and statewide levels, with adequate funding; and c) use of audiovisual technology and collaboration with international expertise.

Biochemical, Genetic and Clinical Diagnostic Approaches to Autism-Associated Inherited Metabolic Disorders

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders characterized by impaired social interaction, limited communication skills, and restrictive and repetitive behaviours. The pathophysiology of ASD is multifactorial and includes genetic, epigenetic, and environmental factors, whereas a causal relationship has been described between ASD and inherited metabolic disorders (IMDs). This review describes biochemical, genetic, and clinical approaches to investigating IMDs associated with ASD. The biochemical work-up includes body fluid analysis to confirm general metabolic and/or lysosomal storage diseases, while the advances and applications of genomic testing technology would assist with identifying molecular defects. An IMD is considered likely underlying pathophysiology in ASD patients with suggestive clinical symptoms and multiorgan involvement, of which early recognition and treatment increase their likelihood of achieving optimal care and a better quality of life.

The effects of face masks on emotional appraisal ability of students with autism spectrum disorder

The impact of the COVID-19 pandemic has drastically altered the ways in which people are able to communicate effectively. The addition of the face mask as a distracting stimulus interrupts the holistic process that people use to interpret facial expressions. The present study seeks to investigate the impact of face masking and gaze direction on emotion recognition in students with autism. We predicted that students with autism (n = 14), who characteristically experience difficulties when appraising emotions, would struggle to assess the emotions of people wearing face masks. We did not find a significant three-way interaction of emotion, mask, and gaze on classification accuracy. We did, however, find that face masks reduced participants' ability to emotionally appraise sad faces. Further, participants showed better accuracy appraising faces with a direct gaze. Exploring how face masks impact autistic individuals' emotion recognition will benefit special educators as they adapt to teaching during the pandemic, as well as the general population that seeks to improve communication with neurodiverse persons. Future studies should examine emotional appraisal ability and additional emotions as well as different kinds of emotional stimuli.

Risk factors for developmental quotients in ASD children: A cross-sectional study

Objective

To analyze the risk factors for developmental quotients (DQs) of children with autism spectrum disorder (ASD) and to better understand the effects of screen time on neurodevelopment in children with ASD.

Methods

We retrospectively analyzed the data of 382 children with ASD, including demographic profiles; socioeconomic status; score on the Chinese parent–child interaction scale (CPCIS); screen time questionnaire; ASD symptom rating scales, including the Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), and Autism Diagnostic Observation Schedule Second Edition (ADOS-2); and DQs using Griffiths Development Scales–Chinese Edition. Univariate analysis was carried out to analyze the factors related to the DQs of children with ASD, and then the linear regression model was used to identify the independent influencing factors of the DQs of children with ASD.

Results

Vitamin D (β = 0.180, p = 0.002), age (β = −0.283, p = 0.000) and CARS score (β = −0.347, p = 0.000) are risk factors related to DQ of locomotor in children with ASD. Vitamin D (β = 0.108, p = 0.034), CARS score (β =  −0.503, p = 0.000), ADOS-2 severity score (β =  −0.109, p = 0.045) and CPCIS score (β = 0.198, p = 0.000) are risk factors related to DQ of personal social skill in children with ASD. Vitamin D (β = 0.130, p = 0.018), CARS score (β =  −0.469, p = 0.000), and CPCIS score (β = 0.133, p = 0.022) are risk factors related to DQ of hearing-speech in children with ASD. Vitamin D (β = 0.163, p = 0.003) and CARS score (β =  −0.471, p = 0.000) are risk factors related to DQ of eye-hand coordination in children with ASD. Age (β =  −0.140, p = 0.020), CARS score (β =  −0.342, p = 0.000), ADOS-2 severity score (β = −0.133, p = 0.034) and CPCIS score (β = 0.193, p = 0.002) are risk factors related to DQ of performance in children with ASD. Vitamin D (β = 0.801, p = 0.000) and CPCIS score (β = 0.394, p = 0.019) are risk factors related to DQ of practical reasoning in children with ASD.

Conclusion

Vitamin D status, the severity of autistic symptoms and parent-child interaction are risk factors for developmental quotients in children with ASD. Screen exposure time is negatively associated with DQs in children with ASD but is not an independent risk factor for DQs.

"Why do they do it?": The short-story task for measuring fiction-based mentalizing in autistic and non-autistic individuals

This study aimed to validate the short-story-task (SST) based on Dodell-Feder et al. as an instrument to quantify the ability of mentalizing and to differentiate between non-autistic adults and autistic adults, who may have acquired rules to interpret the actions of non-autistic individuals. Autistic (N = 32) and non-autistic (N = 32) adult participants were asked to read "The End of Something" by Ernest Hemingway and to answer implicit and explicit mentalizing questions, and comprehension questions. Furthermore, verbal and nonverbal IQ was measured and participants were asked how much fiction they read each month. Mentalizing performance was normally distributed for autistic and non-autistic participants with autistic participants scoring in the lower third of the distribution. ROC (receiver operator curve) analysis revealed the task to be an excellent discriminator between autistic and non-autistic participants. A linear regression analysis identified number of books read, years of education and group as significant predictors. Overall, the SST is a promising measure of mentalizing. On the one hand, it differentiates among non-autistic individuals and on the other hand it is sensitive towards performance differences in mentalizing among autistic adults. Implications for interventions are discussed.

Sex Differences in the Developmental Trajectories of Autism Spectrum Disorder

Purpose of Review

Females and males are disproportionately diagnosed with autism, a sex difference that has historically represented this neurodevelopmental condition. The current review examines lifespan developmental trajectories of autism based on sex to elucidate behavioral phenotypic differences that may contribute to differential rates of diagnosis.

Recent Findings

We review sex differences in diagnostic criteria: social communication and restricted interests/repetitive behaviors (RRBs). Results suggest RRBs are more indicative of a diagnosis in males, whereas social differences are more indicative of a diagnosis in females. Factors contributing to a later diagnosis in females include social strengths (camouflaging) and diagnostic overshadowing.

Summary

Sex differences in diagnostic criteria may contribute to differential rates of identification in males and females. Sex differences are most pronounced when assessing naturalistic social communication instead of reliance on standardized measure. Numerous future directions are identified including increasing samples of sub-threshold autistic females and evaluating longitudinal sex differences.

Candidate diagnostic biomarkers for neurodevelopmental disorders in children and adolescents: a systematic review

Neurodevelopmental disorders - including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, communication disorders, intellectual disability, motor disorders, specific learning disorders, and tic disorders - manifest themselves early in development. Valid, reliable and broadly usable biomarkers supporting a timely diagnosis of these disorders would be highly relevant from a clinical and public health standpoint. We conducted the first systematic review of studies on candidate diagnostic biomarkers for these disorders in children and adolescents. We searched Medline and Embase + Embase Classic with terms relating to biomarkers until April 6, 2022, and conducted additional targeted searches for genome-wide association studies (GWAS) and neuroimaging or neurophysiological studies carried out by international consortia. We considered a candidate biomarker as promising if it was reported in at least two independent studies providing evidence of sensitivity and specificity of at least 80%. After screening 10,625 references, we retained 780 studies (374 biochemical, 203 neuroimaging, 133 neurophysiological and 65 neuropsychological studies, and five GWAS), including a total of approximately 120,000 cases and 176,000 controls. While the majority of the studies focused simply on associations, we could not find any biomarker for which there was evidence - from two or more studies from independent research groups, with results going into the same direction - of specificity and sensitivity of at least 80%. Other important metrics to assess the validity of a candidate biomarker, such as positive predictive value and negative predictive value, were infrequently reported. Limitations of the currently available studies include mostly small sample size, heterogeneous approaches and candidate biomarker targets, undue focus on single instead of joint biomarker signatures, and incomplete accounting for potential confounding factors. Future multivariable and multi-level approaches may be best suited to find valid candidate biomarkers, which will then need to be validated in external, independent samples and then, importantly, tested in terms of feasibility and cost-effectiveness, before they can be implemented in daily clinical practice.

How to Make the Unpredictable Foreseeable? Effective Forms of Assistance for Children with Autism Spectrum Disorder (ASD) during the COVID-19 Pandemic

Symptomatology in patients with the diagnosis of autism spectrum disorder (ASD) is very heterogeneous. The symptoms they present include communication difficulties, behavior problems, upbringing problems from their parents, and comorbidities (e.g., epilepsy, intellectual disability). A predictable and stable environment and the continuity of therapeutic interactions are crucial in this population. The COVID-19 pandemic has created much concern, and the need for home isolation to limit the spread of the virus has disrupted the functioning routine of children/adolescents with ASD. Are there effective diagnostic and therapeutic alternatives to limit the consequences of disturbing the daily routine of young patients during the unpredictable times of the pandemic? Modern technology and telemedicine have come to the rescue. This narrative review aims to present a change in the impact profile in the era of isolation and assess the directions of changes that specialists may choose when dealing with patients with ASD.

Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses

BACKGROUND

Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co-occurring disorders.

METHOD

We used a well-characterized clinical sample of individuals (n = 1,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n = 481) and covered a range of additional overlapping diagnoses, including anxiety-related disorders (ANX, n = 122), ADHD (n = 439), and conduct disorder (CD, n = 194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best-estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co-occurring ADHD, and an unspecific model using all available data. We employed nested cross-validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice.

RESULTS

We obtained very good overall sensitivity (0.89-0.94) and specificity (0.87-0.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses.

CONCLUSIONS

ML-based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult.

Predicting the future of neuroimaging predictive models in mental health

Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).

Gaze and social functioning associations in autism spectrum disorder: A systematic review and meta-analysis

Autism spectrum disorder (ASD) is characterized by significant social functioning impairments, including (but not limited to) emotion recognition, mentalizing, and joint attention. Despite extensive investigation into the correlates of social functioning in ASD, only recently has there been focus on the role of low‐level sensory input, particularly visual processing. Extensive gaze deficits have been described in ASD, from basic saccadic function through to social attention and the processing of complex biological motion. Given that social functioning often relies on accurately processing visual information, inefficient visual processing may contribute to the emergence and sustainment of social functioning difficulties in ASD. To explore the association between measures of gaze and social functioning in ASD, a systematic review and meta‐analysis was conducted. A total of 95 studies were identified from a search of CINAHL Plus, Embase, OVID Medline, and psycINFO databases in July 2021. Findings support associations between increased gaze to the face/head and eye regions with improved social functioning and reduced autism symptom severity. However, gaze allocation to the mouth appears dependent on social and emotional content of scenes and the cognitive profile of participants. This review supports the investigation of gaze variables as potential biomarkers of ASD, although future longitudinal studies are required to investigate the developmental progression of this relationship and to explore the influence of heterogeneity in ASD clinical characteristics.

Objective measurement of vocalizations in the assessment of autism spectrum disorder symptoms in preschool age children

Assessment of autism spectrum disorder (ASD) relies on expert clinician observation and judgment, but objective measurement tools have the potential to provide additional information on ASD symptom severity. Diagnostic evaluations for ASD typically include the autism diagnostic observation schedule (ADOS-2), a semi-structured assessment composed of a series of social presses. The current study examined associations between concurrent objective features of child vocalizations during the ADOS-2 and examiner-rated autism symptom severity. The sample included 66 children (49 male; M = 40 months, SD = 10.58) evaluated in a university-based clinic, 61 of whom received an ASD diagnosis. Research reliable administration of the ADOS-2 provided social affect (SA) and restricted and repetitive behavior (RRB) calibrated severity scores (CSS). Audio was recorded from examiner-worn eyeglasses during the ADOS-2 and child and adult speech were differentiated with LENA SP Hub. PRAAT was used to ascertain acoustic features of the audio signal, specifically the mean fundamental vocal frequency (F0) of LENA-identified child speech-like vocalizations (those with phonemic content), child cry vocalizations, and adult speech. Sphinx-4 was employed to estimate child and adult phonological features indexed by the average consonant and vowel count per vocalization. More than a quarter of the variance in ADOS-2 RRB CSS was predicted by the combination of child phoneme count per vocalization and child vocalization F0. Findings indicate that both acoustic and phonological features of child vocalizations are associated with expert clinician ratings of autism symptom severity. LAY SUMMARY: Determination of the severity of autism spectrum disorder is based in part on expert (but subjective) clinician observations during the ADOS-2. Two characteristics of child vocalizations-a smaller number of speech-like sounds per vocalization and higher pitched vocalizations (including cries)-were associated with greater autism symptom severity. The results suggest that objectively ascertained characteristics of children's vocalizations capture variance in children's restricted and repetitive behaviors that are reflected in clinician severity indices.

rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis

Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria through a lengthy and time-consuming process. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis. In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy and reliability. Therefore, we conducted a systematic review and meta-analysis to summarize the available evidence in the literature so far. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for ANN classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seems promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7% versus 72.8%). Finally, our analysis showed AUC values between acceptable and excellent. Still, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.

EEG abnormalities and clinical phenotypes in pre-school children with autism spectrum disorder

BACKGROUND

Abnormalities on electroencephalography (EEG) results have been reported in a high percentage of children with Autism Spectrum Disorder (ASD). The purpose of this study was to explore the prevalence of EEG abnormalities in a clinical population of pre-school children with Autism Spectrum Disorder and the differences in terms of the following phenotypic characteristics: adaptive behavior, executive functioning, severity of Autism Spectrum Disorder core symptoms, and comorbidity symptoms.

METHODS

A cross-sectional analysis of 69 children who attended the Autism Spectrum Disorder early diagnosis program with electroencephalography and clinical diagnosis was performed. A battery of questionnaires was also made to parents to evaluate emotions, behavior, and functional skills for daily living.

RESULTS

Out of 69 pre-school children with Autism Spectrum Disorder, twenty nine (42%) had abnormalities in electroencephalography results. The group with abnormal epileptiform electroencephalography exhibited more impairment in executive functioning and social-relationship coexisting symptoms.

CONCLUSIONS

The presence of an abnormal epileptiform electroencephalography in pre-school children with ASD already suggests a worse development in clinical features.

A Personalized Multidisciplinary Approach to Evaluating and Treating Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder without a known cure. Current standard-of-care treatments focus on addressing core symptoms directly but have provided limited benefits. In many cases, individuals with ASD have abnormalities in multiple organs, including the brain, immune and gastrointestinal system, and multiple physiological systems including redox and metabolic systems. Additionally, multiple aspects of the environment can adversely affect children with ASD including the sensory environment, psychosocial stress, dietary limitations and exposures to allergens and toxicants. Although it is not clear whether these medical abnormalities and environmental factors are related to the etiology of ASD, there is evidence that many of these factors can modulate ASD symptoms, making them a potential treatment target for improving core and associated ASD-related symptoms and improving functional limitation. Additionally, addressing underlying biological disturbances that drive pathophysiology has the potential to be disease modifying. This article describes a systematic approach using clinical history and biomarkers to personalize medical treatment for children with ASD. This approach is medically comprehensive, making it attractive for a multidisciplinary approach. By concentrating on treatable conditions in ASD, it is possible to improve functional ability and quality of life, thus providing optimal outcomes.

Diagnostic Utility of the Gilliam Autism Rating Scales-3rd Edition Parent Report in Clinically Referred Children

There is limited research regarding the Gilliam Autism Rating Scales-3rd Edition (GARS-3) despite its extensive use. A comprehensive diagnostic evaluation, including the Autism Diagnostic Observation Schedule-2nd Edition (ADOS-2) was provided to 186 clinically referred children suspected of autism ([Formula: see text] age = 8.98; Autism [AUT] n = 87; Not Autism [NOT] n = 99). Mean difference analyses, Logistic Regressions, and ROC analyses were non-significant for both Autism Index scores. The author-suggested cutoff score of 70 correctly classified approximately 47% of participants, with false positive rates = 82.83-87.88%. ADOS-2 correlations were significantly lower vis-à-vis the standardization sample. The Social Interaction subscale demonstrated weak, marginal results, and sensitivity/specificity could not be optimized. In its current form, the GARS-3 does not demonstrate adequate criterion validity for use in assessment of complex community samples.

Abilities and Disabilities-Applying Machine Learning to Disentangle the Role of Intelligence in Diagnosing Autism Spectrum Disorders

Objective

Although autism spectrum disorder (ASD) is a relatively common, well-known but heterogeneous neuropsychiatric disorder, specific knowledge about characteristics of this heterogeneity is scarce. There is consensus that IQ contributes to this heterogeneity as well as complicates diagnostics and treatment planning. In this study, we assessed the accuracy of the Autism Diagnostic Observation Schedule (ADOS/2) in the whole and IQ-defined subsamples, and analyzed if the ADOS/2 accuracy may be increased by the application of machine learning (ML) algorithms that processed additional information including the IQ level.

Methods

The study included 1,084 individuals: 440 individuals with ASD (with a mean IQ level of 3.3 ± 1.5) and 644 individuals without ASD (with a mean IQ level of 3.2 ± 1.2). We applied and analyzed Random Forest (RF) and Decision Tree (DT) to the ADOS/2 data, compared their accuracy to ADOS/2 cutoff algorithms, and examined most relevant items to distinguish between ASD and Non-ASD. In sum, we included 49 individual features, independently of the applied ADOS module.

Results

In DT analyses, we observed that for the decision ASD/Non-ASD, solely one to four items are sufficient to differentiate between groups with high accuracy. In addition, in sub-cohorts of individuals with (a) below (IQ level ≥4)/ID and (b) above average intelligence (IQ level ≤ 2), the ADOS/2 cutoff showed reduced accuracy. This reduced accuracy results in (a) a three times higher risk of false-positive diagnoses or (b) a 1.7 higher risk for false-negative diagnoses; both errors could be significantly decreased by the application of the alternative ML algorithms.

Conclusions

Using ML algorithms showed that a small set of ADOS/2 items could help clinicians to more accurately detect ASD in clinical practice across all IQ levels and to increase diagnostic accuracy especially in individuals with below and above average IQ level.

Duration of untreated autism in rural America: emerging public health crisis

The rural areas have been at the receiving end amidst mental health disparity across the USA. There is a serious and concerning divide among ones with autism spectrum disorders (ASDs) living in underserved areas as compared to urban residents. With the higher than ever prevalence of ASD as per the recent reports of the Centers for Disease Control and Prevention; there is a need for a closer look at the prevailing issues. The trends are reflecting marked underdiagnosis, late diagnosis, lack of evidence-based diagnostic measures and interventions. These factors interplay in worsening the mental health crisis and there is an urgent need for corrective measures to address these highly modifiable problems.

Predictive language processing in young autistic children

Recent theories propose that domain-general deficits in prediction (i.e., the ability to anticipate upcoming information) underlie the behavioral characteristics associated with autism spectrum disorder (ASD). If these theories are correct, autistic children might be expected to demonstrate difficulties on linguistic tasks that rely on predictive processing. Previous research has largely focused on older autistic children and adolescents with average language and cognition. The present study used an eye-gaze task to evaluate predictive language processing among 3- to 4-year-old autistic children (n = 34) and 1.5- to 3-year-old, language-matched neurotypical (NT) children (n = 34). Children viewed images (e.g., a cake and a ball) and heard sentences with informative verbs (e.g., Eat the cake) or neutral verbs (e.g., Find the cake). Analyses of children's looking behaviors indicated that young autistic children, like their language-matched NT peers, engaged in predictive language processing. Regression results revealed a significant effect of diagnostic group, when statistically controlling for age differences. The NT group displayed larger difference scores between the informative and neutral verb conditions (in looks to target nouns) compared to the ASD group. Receptive language measures were predictive of looking behavior across time for both groups, such that children with stronger language skills were more efficient in making use of informative verbs to process upcoming information. Taken together, these results suggest that young autistic children can engage in predictive processing though further research is warranted to explore the developmental trajectory relative to NT development.

CLEAR Autism Diagnostic Evaluation (CADE): Evaluation of Reliability and Validity

Recent surveillance efforts indicate that 1 in 54 American children meet the criteria for Autism Spectrum Disorder (ASD), making it the fastest growing neurodevelopmental disorder in the U.S. Despite evidence that ASD can be reliably diagnosed as early as 24 months, the median age at ASD diagnosis in 2016 in the U.S. was 51 months. The CLEAR Autism Diagnostic Evaluation (CADE; Willard & Kroncke, 2019), was developed in response to the need to improve, shorten, and standardize the clinical ASD evaluation process. The CADE is a 33-item rating scale designed to be completed by caregivers and clinicians. The current study was conducted to examine the reliability and validity of the CADE using a sample of 191 individuals who received a private evaluation for ASD. Using the client's evaluation records, clinicians completed the CADE items. The coefficient alpha was .94, which indicates that the items form a scale that has high internal consistency. The CADE total scores were highly correlated with ADOS scores, with r values ranging from .52-.86, and discriminated between those participants with a diagnosis of ASD and those without (p < .001). Receiver operator characteristic (ROC) curve analyses indicated excellent diagnostic accuracy of the CADE total score (ROC area under the curve = .998). Results suggest that the CADE can be used as an efficient and accurate means of evaluating ASD. Limitations and implications for use of the CADE are discussed.

Pre-symptomatic intervention for autism spectrum disorder (ASD): defining a research agenda

Autism spectrum disorder (ASD) impacts an individual's ability to socialize, communicate, and interact with, and adapt to, the environment. Over the last two decades, research has focused on early identification of ASD with significant progress being made in understanding the early behavioral and biological markers that precede a diagnosis, providing a catalyst for pre-symptomatic identification and intervention. Evidence from preclinical trials suggest that intervention prior to the onset of ASD symptoms may yield more improved developmental outcomes, and clinical studies suggest that the earlier intervention is administered, the better the outcomes. This article brings together a multidisciplinary group of experts to develop a conceptual framework for behavioral intervention, during the pre-symptomatic period prior to the consolidation of symptoms into diagnosis, in infants at very-high-likelihood for developing ASD (VHL-ASD). The overarching goals of this paper are to promote the development of new intervention approaches, empirical research, and policy efforts aimed at VHL-ASD infants during the pre-symptomatic period (i.e., prior to the consolidation of the defining features of ASD).

Correlation Between Serum Concentrations of Menaquinone-4 and Developmental Quotients in Children With Autism Spectrum Disorder

Objective: The vitamin K family has a wide range of effects in the body, including the central nervous system. Menaquinone-4 (MK-4), a form of vitamin K2, is converted from phylloquinone (PK), which is the main source of dietary vitamin K and is the main form of vitamin K in the brain. We conducted this study to investigate the serum concentration of MK-4 and the correlations between MK-4 and developmental quotients in children with autism spectrum disorder (ASD). Methods: We selected 731 children with ASD who were diagnosed for the first time. During the same period, 332 neurotypical children who underwent regular physical examinations in our outpatient department were selected as the TD group. We investigated the general situation of children, including gender and age. Children in ASD group were assessed for autistic symptoms and development quotients, including Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), ADOS-2, and Griffiths Development Scales-Chinese Language Edition (GDS-C). Both groups of children were tested for serum menaquinone-4. We compared serum menaquinone-4 levels of ASD group and TD group. We then conducted a correlation analysis between the level of menaquinone-4 and the developmental quotient of children with ASD. Results: The results of this study indicate that the serum concentration of MK-4 in children with ASD is lower than that in children with typical development (t = -2.702, P = 0.007). The serum concentration of MK-4 is related to the developmental quotients of several subscales in ASD children, and this correlation is more obvious in males. Conclusion: we conclude that MK-4 is present in lower concentrations in children with ASD, which may affect cognition and developmental quotients. The role of MK-4 in ASD needs to be further explored.

Is the Combination of ADOS and ADI-R Necessary to Classify ASD? Rethinking the "Gold Standard" in Diagnosing ASD

Diagnosing autism spectrum disorder (ASD) requires extensive clinical expertise and training as well as a focus on differential diagnoses. The diagnostic process is particularly complex given symptom overlap with other mental disorders and high rates of co-occurring physical and mental health concerns. The aim of this study was to conduct a data-driven selection of the most relevant diagnostic information collected from a behavior observation and an anamnestic interview in two clinical samples of children/younger adolescents and adolescents/adults with suspected ASD. Via random forests, the present study discovered patterns of symptoms in the diagnostic data of 2310 participants (46% ASD, 54% non-ASD, age range 4-72 years) using data from the combined Autism Diagnostic Observation Schedule (ADOS) and Autism Diagnostic Interview-Revised (ADI-R) and ADOS data alone. Classifiers built on reduced subsets of diagnostic features yield satisfactory sensitivity and specificity values. For adolescents/adults specificity values were lower compared to those for children/younger adolescents. The models including ADOS and ADI-R data were mainly built on ADOS items and in the adolescent/adult sample the classifier including only ADOS items performed even better than the classifier including information from both instruments. Results suggest that reduced subsets of ADOS and ADI-R items may suffice to effectively differentiate ASD from other mental disorders. The imbalance of ADOS and ADI-R items included in the models leads to the assumption that, particularly in adolescents and adults, the ADI-R may play a lesser role than current behavior observations.

Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review

The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children's social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed.

Validation of the Social Responsiveness Scale (SRS) to screen for atypical social behaviors in juvenile macaques

Primates form strong social bonds and depend on social relationships and networks that provide shared resources and protection critical for survival. Social deficits such as those present in autism spectrum disorder (ASD) and other psychiatric disorders hinder the individual’s functioning in communities. Given that early diagnosis and intervention can improve outcomes and trajectories of ASD, there is a great need for tools to identify early markers for screening/diagnosis, and for translational animal models to uncover biological mechanisms and develop treatments. One of the most widely used screening tools for ASD in children is the Social Responsiveness Scale (SRS), a quantitative measure used to identify individuals with atypical social behaviors. The SRS has been adapted for use in adult rhesus monkeys (Macaca mulatta)–a species very close to humans in terms of social behavior, brain anatomy/connectivity and development–but has not yet been validated or adapted for a necessary downward extension to younger ages matching those for ASD diagnosis in children. The goal of the present study was to adapt and validate the adult macaque SRS (mSRS) in juvenile macaques with age equivalent to mid-childhood in humans. Expert primate coders modified the mSRS to adapt it to rate atypical social behaviors in juvenile macaques living in complex social groups at the Yerkes National Primate Research Center. Construct and face validity of this juvenile mSRS (jmSRS) was determined based on well-established and operationalized measures of social and non-social behaviors in this species using traditional behavioral observations. We found that the jmSRS identifies variability in social responsiveness of juvenile rhesus monkeys and shows strong construct/predictive validity, as well as sensitivity to detect atypical social behaviors in young male and female macaques across social status. Thus, the jmSRS provides a promising tool for translational research on macaque models of children social disorders.

How Do Adults with Autism Spectrum Disorder Participate in the Labor Market? A German Multi-center Survey

International studies show disadvantages for adults with autism spectrum disorder (ASD) in the labor market. Data about their participation in the German labor market are scarce. The aim of this study was to examine the integration of adults with ASD in the German labor market in terms of education, employment and type of occupation by means of a cross-sectional-study, using a postal questionnaire. Findings show above average levels of education for adults with ASD compared to the general population of Germany and simultaneously, below average rates of employment and high rates of financial dependency. That indicates a poor integration of adults with ASD in the German labor market and emphasizes the need for vocational support policies for adults with ASD.

Sleep Disorders in Autism Spectrum Disorder Pre-School Children: An Evaluation Using the Sleep Disturbance Scale for Children

Background and Objectives: Sleep disorders are common in children with Autism Spectrum Disorder (ASD). The aims of this study were to describe the incidence and characteristics of sleep disorders using a questionnaire completed by the caregiver in a sample of preschool-aged children with ASD and to identify possible differences in a control group of peers. Materials and Methods: Sleep disorders were investigated with the Sleep Disturbance Scale for Children (SDSC) in a population of pre-school-aged (3-5 years) ASD children and in a control group. The Autism Diagnostic Observation Schedule-second ed. (ADOS-2) was further used to assess autism symptom severity. A total of 84 children (69 males; mean age 3.9 ± 0.8 years) with a diagnosis of ASD and 84 healthy controls (65 males; mean age of 3.7 ± 0.8 years) that were matched for age and sex were enrolled. Results: ASD children reported significantly higher (pathological) scores than the control group on the SDSC total scores and in some of the factor scores, such as Difficulty in Initiating and Maintaining Sleep (DIMS), disorders of excessive somnolence (DOES), and sleep hyperhidrosis. A total of 18% of ASD children had a pathological SDSC total T-score, and 46% had an abnormal score on at least one sleep factor; DIMS, parasomnias, and DOES showed the highest rates among the sleep factors. Younger children (3 years) reported higher scores in DIMS and sleep hyperhidrosis than older ones (4 and 5 years). No specific correlation was found between ADOS-2 and SDSC scores. Conclusions: Pre-school children with ASD showed a high incidence of sleep disorders with different distributions of specific sleep factors according to their age. We suggest a screening assessment of sleep disorders using the SDSC in these children with a more in-depth evaluation for those reporting pathological scores on the questionnaire.

Systematic Review and Meta-Analysis of the Clinical Utility of the ADOS-2 and the ADI-R in Diagnosing Autism Spectrum Disorders in Children

The Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) and the Autism Diagnostic Interview, Revised (ADI-R) have high accuracy as diagnostic instruments in research settings, while evidence of accuracy in clinical settings is less robust. This meta-analysis focused on efficacy of these measures in research versus clinical settings. Articles (n = 22) were analyzed using a hierarchical summary receiver operating characteristics (HSROC) model. ADOS-2 performance was stronger than the ADI-R. ADOS-2 sensitivity and specificity ranged from .89-.92 and .81-.85, respectively. ADOS-2 accuracy in research compared with clinical settings was mixed. ADI-R sensitivity and specificity were .75 and .82, respectively, with higher specificity in research samples (Research = .85, Clinical = .72). A small number of clinical studies were identified, indicating ongoing need for investigation outside research settings.

Screen Time, Age and Sunshine Duration Rather Than Outdoor Activity Time Are Related to Nutritional Vitamin D Status in Children With ASD

Objective: This study aimed to investigate the possible association among vitamin D, screen time and other factors that might affect the concentration of vitamin D in children with autism spectrum disorder (ASD). Methods: In total, 306 children with ASD were recruited, and data, including their age, sex, height, weight, screen time, time of outdoor activity, ASD symptoms [including Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS) and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2)] and vitamin D concentrations, were collected. A multiple linear regression model was used to analyze the factors related to the vitamin D concentration. Results: A multiple linear regression analysis showed that screen time (β = -0.122, P = 0.032), age (β = -0.233, P < 0.001), and blood collection month (reflecting sunshine duration) (β = 0.177, P = 0.004) were statistically significant. The vitamin D concentration in the children with ASD was negatively correlated with screen time and age and positively correlated with sunshine duration. Conclusion: The vitamin D levels in children with ASD are related to electronic screen time, age and sunshine duration. Since age and season are uncontrollable, identifying the length of screen time in children with ASD could provide a basis for the clinical management of their vitamin D nutritional status.

Screen Time and Autism: Current Situation and Risk Factors for Screen Time Among Pre-school Children With ASD

Objective: To investigate the current status of screen time in children with ASD, its correlation with autistic symptoms and developmental quotient (DQ), and the factors affecting screen time. Method: One hundred ninety-three Chinese children with ASD were recruited. We collected the demographic and screen time data using a questionnaire. The ASD core symptoms and developmental quotient (DQ) were measured by the Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), Autism Diagnostic Observation Schedule-Second Edition (ADOS-2), Griffiths Development Scales-Chinese Language Edition (GDS-C), and Chinese Children's Parent-Child Relationship Questionnaire (CPCIS). Then, we analyzed the correlations between the screen time of children with ASD and the ABC, CARS, ADOS, GDS-C DQs, and CPCIS scores. Linear regression was used to analyze the risk factors that affect screen time. Results: The children's average daily screen time was 2.64 ± 2.24 h. Forty eight percent children were exposed to two or more types of electronic devices. Their favorite activity of screen time was watching cartoons. Only 34% children spent screen time accompanied by parents and with communication. 50.26% children had no screen time before sleeping. The screen time of children with ASD had a negative correlation with the GDS-C CQ (r = -0.234, P = 0.001) and the CPCIS score (r = -0.180, P = 0.012) and a positive correlation with the CARS score (r = 0.192, P = 0.009). A low father's education level (P = 0.010), less restriction of the child's screen time by the guardian (P = 0.001), greater caregiver screen time (P < 0.001), the use of the screen as a tool for child rearing (P = 0.001), and the child's ownership of independent electronic equipment (P = 0.027) are risk factors for long screen time in children with ASD. Conclusion: The screen time of children with ASD in China is higher than the recommended standard, and the current situation is serious. The screen time of ASD children is related to their autism symptoms, DQ and parent-child interaction. Low paternal education levels, less restriction of children's screen time by guardians, greater guardian screen time, the use of screens in child rearing, and children's ownership of independent electronic equipment can lead to an increase in children's screen time. These findings may have implications for family intervention strategies.

An Exploratory Study of Executive Function Development in Children with Autism, after Receiving Early Intensive Behavioral Training

Objective: To examine the development of executive functions, in preschool children with autism spectrum disorders (ASD), receiving early intensive behavioral training (EIBI). Method: Executive functions (EF) were assessed with The Behavior Rating Inventory of Executive Function - Preschool Version (BRIEF-P), by parents and preschool teachers at the time of diagnostic assessment and after 15 months of EIBI intervention. Ten children with ASD (M = 2.9 years, nine males) participated in the study. Reliable Change Index scores were computed for each of the participants in order to investigate any significant change in BRIEF-P T-scores. Results: Three children showed a significant improvement in EF, based on parent ratings. Four children showed a significant improvement in EF based on preschool teacher ratings. Conclusion: Findings indicating a reliable improvement in one third of preschool children with ASD receiving EIBI are encouraging but need to be replicated in larger scale controlled studies.