Attention-deficit Hyperactivity Disorder and Autism Spectrum Disorder: Towards Better Diagnosis and Management.

Background: Attention-deficit hyperactivity disorder (ADHA) is one of the most common comorbid disorders of autism spectrum disorder (ASD) that can accompany autism, triggered by it, or be a consequence of it. Objective: This review explored the prevalence of the comorbidity of both disorders, neurobiological background, symptoms, latest assessment methods, and therapeutic approaches. Results and Discussion: It concluded that effective assessment, diagnosis and management of ADHD in ASD children and adults is essential for this group of patients to thrive and live a good quality of life. Further research is recommended to explore the most effective intervention for such important members of our society. Conclusion: More studies are needed to understand the mechanisms underlying these comorbidities, and to prevent the misdiagnosis and mismanagement of these disorders. Also, to develop up to date personalized therapeutic plans for such children.

Clinical correlates of diagnostic certainty in children and youths with Autistic Disorder.

BACKGROUND: Clinicians diagnosing autism rely on diagnostic criteria and instruments in combination with an implicit knowledge based on clinical expertise of the specific signs and presentations associated with the condition. This implicit knowledge influences how diagnostic criteria are interpreted, but it cannot be directly observed. Instead, insight into clinicians' understanding of autism can be gained by investigating their diagnostic certainty. Modest correlations between the certainty of an autism diagnosis and symptom load have been previously reported. Here, we investigated the associations of diagnostic certainty with specific items of the ADOS as well as other clinical features including head circumference. METHODS: Phenotypic data from the Simons Simplex Collection was used to investigate clinical correlates of diagnostic certainty in individuals diagnosed with Autistic Disorder (n = 1511, age 4 to 18 years). Participants were stratified by the ADOS module used to evaluate them. We investigated how diagnostic certainty was associated with total ADOS scores, age, and ADOS module. We calculated the odds-ratios of being diagnosed with the highest possible certainty given the presence or absence of different signs during the ADOS evaluation. Associations between diagnostic certainty and other cognitive and clinical variables were also assessed. RESULTS: In each ADOS module, some items showed a larger association with diagnostic certainty than others. Head circumference was significantly higher for individuals with the highest certainty rating across all three ADOS modules. In turn, head circumference was positively correlated with some of the ADOS items that were associated with diagnostic certainty, and was negatively correlated with verbal/nonverbal IQ ratio among those assessed with ADOS module 2. LIMITATIONS: The investigated cohort was heterogeneous, e.g. in terms of age, IQ, language level, and total ADOS score, which could impede the identification of associations that only exist in a subgroup of the population. The variability of the certainty ratings in the sample was low, limiting the power to identify potential associations with other variables. Additionally, the scoring of diagnostic certainty may vary between clinicians. CONCLUSION: Some ADOS items may better capture the signs that are most associated with clinicians' implicit knowledge of Autistic Disorder. If replicated in future studies, new diagnostic instruments with differentiated weighting of signs may be needed to better reflect this, possibly resulting in better specificity in standardized assessments.

ESDM intervention in severe preschool autism: an Italian Case report, psychological and social medicine reflections.

BACKGROUND: The goal of our contribution is to discuss a preschool intervention based on the Early Start Denver Model and the use of the main tools for the detection of adaptive behaviour in cases of autism: Vineland, ABAS. CASE PRESENTATION: the work is the presentation of a clinical case that has benefited from an intervention with the Early Start Denver Model methodology for the benefit of a child with socio-cultural and economic disadvantages. This early intervention, in a child of 36 months, which followed the diagnosis, was possible thanks to the intervention of many third-sector organizations which allowed this child, with a serious autism profile, to receive an evidence-based intervention for free. At the beginning of the intervention, the child presented a diagnosis of severe autism with absence of gaze, vocalizations and other communicative impairments. The level of motor clumsiness was also quite high, as were stereotypies. CONCLUSIONS: Research has shown the usefulness of intervening in this area with an early assessment and/or diagnosis and immediate intervention; however, public health services are not always able to maintain this pace. Our contribution therefore shows on the one hand the evidence of the improvements achieved by the child despite the low intensity of the treatment, and on the other hand, demonstrates the total versatility and adaptability of the Denver Model to the Italian context. In our conclusions, there are also some reflections on the tools used to measure adaptive behavior which seem to have a number of limitations and criticalities.

Identification and support of autistic individuals within the UK Criminal Justice System: a practical approach based upon professional consensus with input from lived experience.

BACKGROUND: Autism spectrum disorder (hereafter referred to as autism) is characterised by difficulties with (i) social communication, social interaction, and (ii) restricted and repetitive interests and behaviours. Estimates of autism prevalence within the criminal justice system (CJS) vary considerably, but there is evidence to suggest that the condition can be missed or misidentified within this population. Autism has implications for an individual's journey through the CJS, from police questioning and engagement in court proceedings through to risk assessment, formulation, therapeutic approaches, engagement with support services, and long-term social and legal outcomes. METHODS: This consensus based on professional opinion with input from lived experience aims to provide general principles for consideration by United Kingdom (UK) CJS personnel when working with autistic individuals, focusing on autistic offenders and those suspected of offences. Principles may be transferable to countries beyond the UK. Multidisciplinary professionals and two service users were approached for their input to address the effective identification and support strategies for autistic individuals within the CJS. RESULTS: The authors provide a consensus statement including recommendations on the general principles of effective identification, and support strategies for autistic individuals across different levels of the CJS. CONCLUSION: Greater attention needs to be given to this population as they navigate the CJS.

Diagnostic Evaluation of Autism Spectrum Disorder in Pediatric Primary Care.

BACKGROUND AND OBJECTIVES: Children with autism spectrum disorder (ASD) continue to experience significant delays in diagnosis and interventions. One of the main factors contributing to this delay is a shortage of developmental-behavioral specialists. Diagnostic evaluation of ASD by primary care pediatricians (PCPs) has been shown to be reliable and to decrease the interval from first concern to diagnosis. In this paper, we present the results of a primary care ASD diagnosis program in which the PCP serves as the primary diagnostician and leverages the infrastructure of the primary care medical home to support the child and family during the pre- and post-diagnostic periods, along with data on parental satisfaction with this model. METHODS: Retrospective data from a cohort of patients evaluated through this program were analyzed to determine the mean age at diagnosis and interval from referral for evaluation to diagnosis. We used survey methodology to obtain data from parents regarding their satisfaction with the process. RESULTS: Data from 8 of 20 children evaluated from April 2021 through May 2022 showed a median age of diagnosis of 34.5 months compared to the national average of 49 months. Mean interval from referral for evaluation to diagnosis was 3.5 months. Parental survey responses indicated high satisfaction. CONCLUSIONS: This model was successful in shortening the interval from referral to diagnosis resulting in significant decrease of age at diagnosis compared with the national average. Widespread implementation could improve access to timely diagnostic services and improve outcomes for children with ASD.

Identifying autism using EEG: unleashing the power of feature selection and machine learning.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is characterized by communication barriers, societal disengagement, and monotonous actions. Currently, the diagnosis of ASD is made by experts through a subjective and time-consuming qualitative behavioural examination using internationally recognized descriptive standards. In this paper, we present an EEG-based three-phase novel approach comprising 29 autistic subjects and 30 neurotypical people. In the first phase, preprocessing of data is performed from which we derived one continuous dataset and four condition-based datasets to determine the role of each dataset in the identification of autism from neurotypical people. In the second phase, time-domain and morphological features were extracted and four different feature selection techniques were applied. In the last phase, five-fold cross-validation is used to evaluate six different machine learning models based on the performance metrics and computational efficiency. The neural network outperformed when trained with maximum relevance and minimum redundancy (MRMR) algorithm on the continuous dataset with 98.10% validation accuracy and 0.9994 area under the curve (AUC) value for model validation, and 98.43% testing accuracy and AUC test value of 0.9998. The decision tree overall performed the second best in terms of computational efficiency and performance accuracy. The results indicate that EEG-based machine learning models have the potential for ASD identification from neurotypical people with a more objective and reliable method.

Evaluation of functional gastrointestinal disorders in children aged 4-10 years with autism spectrum disorder.

BACKGROUND: Gastrointestinal system disorders are known to be prevalent among children with autism spectrum disorder (ASD). Some ASD-associated comorbidities are abdominal pain, constipation, diarrhea, gastroesophageal reflux, sleep disturbances, epilepsy, and psychiatric problems. Nonetheless, there is still limited information about the presence of functional GI disorders (FGIDs) among children with ASD, especially in Türkiye. Using the Rome criteria, we aimed to investigate FGIDs in children with ASD. METHODS: The sample of the study consisted of 68 children aged 4-10 years, diagnosed with ASD according to the DSM-5 diagnostic criteria and had scores greater than 30 on the Childhood Autism Rating Scale (CARS-2) and an age-sex matched control group (n=78). The Rome III criteria were used to evaluate FGIDs. RESULTS: The frequency of FGIDs in the ASD group was higher (76.5%) compared to the control group (p < 0.001). Compared to the control group, abdominal migraine frequency increased 10 times (p=0.012), functional constipation 7 times (p < 0.001), and fecal incontinence 6 times (p < 0.001) in the ASD group. Stool retention was not present in most children in the ASD group who were found to have fecal incontinence. CONCLUSION: In this study, the most common FGIDs in the ASD group were abdominal migraine, functional constipation, and non-retentive fecal incontinence. The finding that most children with ASD who had fecal incontinence did not show stool retention implicated social, psychological, and behavioral factors as the causes of incontinence. Raising awareness of healthcare professionals about the frequency of FGIDs in children with ASD will improve many areas in the daily lives of these children.

Relationship between serum cortisol levels, stereotypies, and the presence of autism spectrum disorder in patients with severe intellectual disability.

Stereotypies are one of the diagnostic criteria for autism spectrum disorder (ASD) and are common to both ASD and intellectual disability (ID). Previous studies have been inconclusive, with some showing a positive correlation between stereotypies and cortisol, while others have shown a negative correlation. We hypothesised and investigated the presence of ASD as one of the variables involved in this discrepancy. We tested the following hypotheses on serum cortisol in a total of 84 hospitalised patients with severe ID and ASD with severe ID. Hypothesis (1) Higher levels of stereotypies are associated with higher levels of serum cortisol. Hypothesis (2) The presence of ASD will moderate the association between stereotypies and high serum cortisol levels. The results of the analysis supported hypotheses (1) and (2). We also found that in the population with ID, serum cortisol levels were significantly lower in the ASD group compared to the non-ASD group. The present findings that the association between stereotypies and serum cortisol levels in people with severe ID is moderated by the presence of ASD suggest that the stress response system may function differently in people with ID and ASD than in the general population.

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OBJECTIVES: To investigate the differences in visual perception between children with autism spectrum disorder (ASD) and typically developing (TD) children when watching different intention videos, and to explore the feasibility of machine learning algorithms in objectively distinguishing between ASD children and TD children. METHODS: A total of 58 children with ASD and 50 TD children were enrolled and were asked to watch the videos containing joint intention and non-joint intention, and the gaze duration and frequency in different areas of interest were used as original indicators to construct classifier-based models. The models were evaluated in terms of the indicators such as accuracy, sensitivity, and specificity. RESULTS: When using eight common classifiers, including support vector machine, linear discriminant analysis, decision tree, random forest, and K-nearest neighbors (with K values of 1, 3, 5, and 7), based on the original feature indicators, the highest classification accuracy achieved was 81.90%. A feature reconstruction approach with a decision tree classifier was used to further improve the accuracy of classification, and then the model showed the accuracy of 91.43%, the specificity of 89.80%, and the sensitivity of 92.86%, with an area under the receiver operating characteristic curve of 0.909 (P<0.001). CONCLUSIONS: The machine learning model based on eye-tracking data can accurately distinguish ASD children from TD children, which provides a scientific basis for developing rapid and objective ASD screening tools.

The Autism Spectrum Disorder and Its Possible Origins in Pregnancy.

Autism Spectrum Disorder (ASD) belongs to the group of neurodevelopmental disorders, and has a high prevalence, affecting 1 in 100 children according to data from the World Health Organization (WHO). To be diagnosed with ASD, the child must have persistent deficits in communication and social interactions, and restricted and repetitive patterns of behavior, interests, or activities. Despite its prevalence, the etiology of ASD is still uncertain, with multifactorial characteristics, including those associated with the gestational period, where maternal exposure to biological, chemical, or physical hazards occurs, some of which have already been proposed as causes of ASD outcomes. Since pregnancy requires a balance between the maternal-fetal binomial, the breakdown of this balance caused by such environmental hazards can lead to altered fetal neurodevelopment, including ASD. With this firmly in mind, this review aims to compile the most recent data on the gestational causes that may be associated with the development of ASD to help health professionals identify risk factors and act for the prevention and management of ASD.

Body Composition and Anthropometric Measurements in Children and Adolescents with Autism Spectrum Disorder: A Case-Control Study in Lebanon.

The occurrence of overweight and obesity among individuals with Autism Spectrum Disorder (ASD) has become a worldwide epidemic. However, there is limited research on this topic in the Lebanese population. Therefore, this study aimed to assess the differences in anthropometric measurements and body composition variables among Lebanese children, pre-adolescents, and adolescents diagnosed with ASD in contrast to typically developing peers across various developmental stages. Additionally, it aimed to investigate the prevalence of overweight and obesity within this population. A total of 86 participants with ASD and 86 controls were involved in this case-control study, conducted between June 2022 and June 2023. Anthropometric measurements and body composition variables were assessed, followed by statistical analyses to examine the differences between these two groups. The results revealed a significantly higher prevalence of overweight and obesity among individuals with ASD, particularly evident during childhood and pre-adolescence. Additionally, this group exhibited a higher body fat mass and total body fat percentage compared to controls. However, there were no significant differences observed between the two groups during adolescence. These findings emphasize the significance of monitoring and addressing weight status in individuals with ASD to improve their overall health outcomes. Future research directions could focus on investigating the underlying mechanisms contributing to the heightened prevalence of overweight and obesity in this population, ultimately enhancing their quality of life and well-being.

Validation of the Hindi Versions of Three Autism Specific Screening Tools (M-CHAT-R/F, RBSK-ASQ and TABC) Widely Used in India in 16-30-Month-Old Children.

OBJECTIVE: To determine the diagnostic accuracy of MCHAT-R/F, RBSK-ASQ and TABC for screening children aged 16 to 30 months for autism spectrum disorder (ASD). METHOD: Children aged 16 to 30 months were recruited from the pediatrics department. Those with known neurodevelopmental disorders, disabilities, severe medical illnesses, unavailable mothers, or lack of maternal understanding of Hindi, were excluded. The three index tools were translated into Hindi; each tool was piloted on 25 mothers and modified accordingly. The researcher was trained in administration, scoring and interpretation of the three tools. After enrollment the index tools and Developmental Profile (DP-3) were administered to each participant. The reference tool was a comprehensive assessment by experts that included clinical evaluation, computation of DP-3 scores, and application of diagnostic criteria of ASD; the final diagnosis being ASD or Non-ASD. RESULTS: Sensitivity and specificity of M-CHAT-R/F were 95.2% and 94.4%, of RBSK-ASQ were 100% and 93.9%, and of TABC were 100% and 94.4%, respectively. Convergent validity was high (Spearman's correlation coefficient 0.98). Test-retest and inter-rater reliability of each tool was excellent (Intra-class correlation coefficient 1.00). CONCLUSION: All three tools had acceptable psychometric properties, high convergent validity and excellent test-retest and inter-rater reliability.

Gut microbial and clinical characteristics of individuals with autism spectrum disorder differ depending on the ecological structure of the gut microbiome.

Understanding the relationship between the gut microbiome and autism spectrum disorder (ASD) is challenging due to the heterogeneous nature of ASD. Here, we analyzed the microbial and clinical characteristics of individuals with ASD using enterotypes. A total of 456 individuals participated in the study, including 249 participants with ASD, 106 typically developing siblings, and 101 controls. The alpha and beta diversities of the ASD, sibling, and control groups did not show significant differences. Analysis revealed a negative association between the Bifidobacterium longum group and the Childhood Autism Rating Scale, as well as a negative association between the Streptococcus salivarus group and the Social Responsiveness Scale (SRS) within the ASD group. When clustered based on microbial composition, participants with ASD exhibited two distinct enterotypes, E1 and E2. In the E2 group, the SRS score was significantly higher, and the Vineland Adaptive Behavior Scale score was significantly lower compared to the E1 group. Machine learning results indicated that the microbial species predicting SRS scores were distinct between the two enterotypes. Our study suggests that the microbial composition in individuals with ASD exhibits considerable variability, and the patterns of associations between the gut microbiome and clinical symptoms may vary depending on the enterotype.

Self-Guided DMT: Exploring a Novel Paradigm of Dance Movement Therapy in Mixed Reality for Children with ASD.

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

Evaluating screening for autism spectrum disorder using cluster randomization.

We evaluated the rate of autism spectrum disorder (ASD) in a group invited to a screening program compared to the rates in two groups who received usual care. The population eligible for screening was all children in Iceland registered for their 30-month well-child visits at primary healthcare centers (PHCs) from March 1, 2016, to October 31, 2017 (N = 7173). The PHCs in the capital area of Reykjavik were the units of cluster randomization. Nine PHCs were selected for intervention (invited group), while eight PHCs received usual care (control group 1). PHCs outside the capital area were without randomization (control group 2). An interdisciplinary team, including a pediatrician contributing with physical and neurological examination, a psychologist evaluating autism symptoms using a diagnostic instrument, and a social worker interviewing the parents, reached a consensus on the clinical diagnosis of ASD according to the ICD-10 diagnostic system. Children in the population were followed up for at least two years and 119 cases were identified. The overall cumulative incidence of ASD was 1.66 (95% confidence interval (CI): 1.37, 1.99). In the invited group the incidence rate was 2.13 (95% CI: 1.60, 2.78); in control group 1, the rate was 1.83 (95% CI: 1.31, 2.50); and in control group 2, the rate was 1.02 (95% CI: 0.66, 1.50). Although the rate of ASD was higher in the invited group than in the control groups, the wide confidence intervals prevented us from concluding definitively that the screening detected ASD more readily than usual care.

Improved emotion differentiation under reduced acoustic variability of speech in autism.

BACKGROUND: Socio-emotional impairments are among the diagnostic criteria for autism spectrum disorder (ASD), but the actual knowledge has substantiated both altered and intact emotional prosodies recognition. Here, a Bayesian framework of perception is considered suggesting that the oversampling of sensory evidence would impair perception within highly variable environments. However, reliable hierarchical structures for spectral and temporal cues would foster emotion discrimination by autistics. METHODS: Event-related spectral perturbations (ERSP) extracted from electroencephalographic (EEG) data indexed the perception of anger, disgust, fear, happiness, neutral, and sadness prosodies while listening to speech uttered by (a) human or (b) synthesized voices characterized by reduced volatility and variability of acoustic environments. The assessment of mechanisms for perception was extended to the visual domain by analyzing the behavioral accuracy within a non-social task in which dynamics of precision weighting between bottom-up evidence and top-down inferences were emphasized. Eighty children (mean 9.7 years old; standard deviation 1.8) volunteered including 40 autistics. The symptomatology was assessed at the time of the study via the Autism Diagnostic Observation Schedule, Second Edition, and parents' responses on the Autism Spectrum Rating Scales. A mixed within-between analysis of variance was conducted to assess the effects of group (autism versus typical development), voice, emotions, and interaction between factors. A Bayesian analysis was implemented to quantify the evidence in favor of the null hypothesis in case of non-significance. Post hoc comparisons were corrected for multiple testing. RESULTS: Autistic children presented impaired emotion differentiation while listening to speech uttered by human voices, which was improved when the acoustic volatility and variability of voices were reduced. Divergent neural patterns were observed from neurotypicals to autistics, emphasizing different mechanisms for perception. Accordingly, behavioral measurements on the visual task were consistent with the over-precision ascribed to the environmental variability (sensory processing) that weakened performance. Unlike autistic children, neurotypicals could differentiate emotions induced by all voices. CONCLUSIONS: This study outlines behavioral and neurophysiological mechanisms that underpin responses to sensory variability. Neurobiological insights into the processing of emotional prosodies emphasized the potential of acoustically modified emotional prosodies to improve emotion differentiation by autistics. TRIAL REGISTRATION: BioMed Central ISRCTN Registry, ISRCTN18117434. Registered on September 20, 2020.

The assessment of microbial infection in children with autism spectrum disorders and genetic folate cycle deficiency.

BACKGROUND: The results of disparate clinical studies indicate abnormally frequent cases of certain microorganisms in children with autism spectrum disorders (ASD). However, these data require clarification and systematization. The study aims to study the structure of the microbial profile in children with ASD and genetic folate cycle deficiency (GFCD) and consider differences in diagnostic approaches for identifying microorganisms of different types. METHODS: The study analyzed medical data from 240 children (187 boys and 63 girls) with GFCD aged 2 to 9 years. The children had clinical manifestations of ASD (the study group, SG). The control group (CG) included 53 clinically healthy children (37 boys and 16 girls) of the same age but without GFCD. Both groups of children were tested on active herpetic infections (HSV-1/2, VZV, EBV, CMV, HHV-6, HHV-7, HHV-8), ТТV, Streptococcus pyogenes, Candida albicans, Borrelia burgdorferi, Mycoplasma pneumoniae, Chlamydia pneumoniae, Yersinia enterocolitica, Toxoplasma gondii, congenital CMV neuroinfection and postnatal HSV-1/2 encephalitis. The testing used diagnostic methods specified in PubMed-indexed studies. RESULTS: In the SG, TTV was found in 196 children (82%), HHV-7 - in 172 (72%), HHV-6 - in 162 (68%), EBV - in 153 (64%), Streptococcus pyogenes - in 127 (53%), Candida albicans - in 116 (48%), Borrelia - in 107 (45%), Mycoplasma pneumoniae - in 94 (39%), Chlamydia pneumoniae - in 85 (35%), Yersinia entеrocolitica - in 71 (30%), Toxoplasma gondii - in 54 (23%), congenital CMV neuroinfection - in 26 (11%), and postnatal HSV-1/2 encephalitis - in 11 children (5% of cases) (p < p0.05; Z < Z0.05). In the SG, there was a higher microbial load in older children (p < p0.05; Z < Z0.05). No gender differences were found. CONCLUSIONS: The study described and characterized a specific abnormal microbial spectrum with a predominance of viral opportunistic agents in children with ASD associated with GFCD.