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Whole brain functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used in the diagnosis of brain disorders such as autism spectrum disorder (ASD). Recently, an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification. However, the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification. In this paper, we proposed a multi-scale attention-based deep neural network (MSA-DNN) model to classify FC patterns for the ASD diagnosis. The model was implemented by adding a flexible multi-scale attention (MSA) module to the auto-encoder based backbone DNN, which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning. Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability. We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leave-one-site-out cross-validations. Results showed that our model outperformed classical methods in brain disease classification and revealed robust inter-site prediction performance. We also localized important FC features and brain regions associated with ASD classification. Overall, our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis, and the proposed MSA module is flexible and easy to implement in other classification networks. [ABSTRACT FROM AUTHOR]

Machine learning (ML) based on remote video has shown ideal diagnostic value in autism spectrum disorder (ASD). Here, we conducted a meta-analysis of the diagnostic value of home video-based ML in ASD. Relevant articles were systematically searched in PubMed, Cochrane, Embase, and Web of Science from inception to September 2023 with no language restriction, and the literature search was updated in September 2024. The overall risk of bias and suitability of the ML prediction models in the included studies were assessed using PROBAST. Nineteen articles involving 89 prediction models and 9959 subjects were included. The mean video duration was 5.63 ± 1.23 min, and the mean number of behavioral features during initial modeling was 23.53. Among the 19 included studies, 13 models had been trained. Seven of the 13 models were not cross-validated (c-index = 0.92, 95% CI 0.88-0.96), while 6 of the 13 models were tenfold cross-validated (c-index = 0.95, 95% CI 0.94-0.97). There were 8 validation cohorts (c-index = 0.83, 95% CI 0.77-0.89). The pooled sensitivity and specificity were 0.87 (95% CI 0.77-0.93) and 0.79 (95% CI 0.76-0.81) in the training cohort, 0.90 (95% CI 0.85-0.94) and 0.87 (95% CI 0.72-0.94) in the cross-validation, and 0.81 (95% CI 0.74-0.86) and 0.72 (95% CI 0.68-0.75) in the validation cohort, respectively. These results indicated that this model is a highly sensitive and user-friendly tool for early ASD diagnosis., Conclusion: Remote video-based ML may improve clinical practice and future research, particularly by combining advanced technologies such as facial recognition. It is a potential tool for diagnosing ASD in children., What Is Known: • The incidence of pediatric ASD has increased in recent years. • ML based on remote video has shown ideal early diagnostic value., What Is New: • The first systematic review and meta-analysis evaluating the diagnostic performance of remote video-based ML for ASD. • Home video-based ML is a valuable diagnostic tool for the early diagnosis of ASD. • Remote video-based ML is convenient and simple to utilize., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethics approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Autism spectrum disorder is a developmental condition that affects the social and behavioral abilities of growing children. Early detection of autism spectrum disorder can help children to improve their cognitive abilities and quality of life. The research in the area of autism spectrum disorder reports that it can be detected from cognitive tests and physical activities of children. The present research reports on the detection of autism spectrum disorder from the facial attributes of children. Children with autism spectrum disorder show ambiguous facial expressions which are different from the facial attributes of normal children. To detect autism spectrum disorder from facial images, this work presents an improvised variant of the YOLOv7-tiny model. The presented model is developed by integrating a pyramid of dilated convolutional layers in the feature extraction network of the YOLOv7-tiny model. Further, its recognition abilities are enhanced by incorporating an additional YOLO detection head. The developed model can detect faces with the presence of autism features by drawing bounding boxes and confidence scores. The entire work has been carried out on a self-annotated autism face dataset. The developed model achieved a mAP value of 79.56% which was better than the baseline YOLOv7-tiny and state-of-the-art YOLOv8 Small model., Competing Interests: Declarations Competing interests The authors declare no competing interests. Ethical statement I “Dushantha Nalin K. Jayakody” hereby declare that for the manuscript: “Ambiguous Facial Expression Detection for Autism Screening Using Enhanced YOLOv7-tiny Model” following is fulfilled: (1) The submitted work is authors’ own original work and has not been published before or submitted elsewhere for publication. (2) The submitted work reflects the authors’ own research and analysis in a truthful manner. (3) The paper meaningfully credits the contributions of co-authors. (4) The results are appropriately presented in context of previous and present research. (5) All sources are cited appropriately and added to the references Sect. (6) The related data is disclosed in the data availability Sect. (7) No research has been conducted on human participants directly. Only images have been used from the cited sources/dataset. The images illustrated in the manuscript are authors original images. (8) All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content. The violation of the Ethical Statement rules may result in severe consequences. I agree with the above statements and declare that this submission follows the policies as outlined in the Guide for Authors and in the Ethical Statement., (© 2024. The Author(s).)

Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early., Competing Interests: Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that there are no competing interests., (© 2024. The Author(s).)

Background: Play is an indispensable and meaningful activity in children's daily life. Research has shown that autistic children often exhibit differences in play development. The core traits of autism, such as distinct patterns in social interaction and communication, focused interests, and repetitive behaviors, frequently manifest in their play. Therefore, play may serve as an insightful measure of these differences. Unlike previous studies focusing on play behaviors only, we explored other behaviors associated with autism during free play, and constructed a clinical prediction model for effectively screening autistic children., Methods: Participants, including 123 autistic children and 123 neurotypical children aged 1-6 years, engaged in a 1.5-min free play with fixed toys, which was videotaped. A novel behavior-coding scheme was used to code these videos for 19 autistic behaviors, including play. The coding details of the 19 behaviors were then converted and expanded to 81 digital behavior indicators, including counts, duration, and proportion., Results: The autistic children showed less functional play and imaginative play and reduced social communication and interactions, such as eye contact, facial expressions, and vocalizations, compared to the neurotypical children during free play. Furthermore, 5 behavioral indicators were selected for the prediction model through stepwise logistic regression, including 1 on socially oriented vocalizations and 4 on count and duration of functional play. The receiver operating characteristic (ROC) curve revealed a good prediction performance with an area under the curve (AUC) of 0.826, a sensitivity of 85.4%, and a specificity of 68.3%., Conclusion: Our findings highlight differences in play performance and social communication and interactions during free play among autistic children. Based on these findings, we constructed a good clinical prediction model, which might be a potential digital tool used by clinicians to effectively screen autistic children., Competing Interests: Declarations Ethics approval and consent to participate All methods were carried out in accordance with the guidelines and recommendations specified within the Helsinki declaration. The procedure was approved by the Ethics Committee of Peking University Sixth Hospital (approval no. 2021-71), and written informed consent was obtained from the parents/legal guardians of all participants before enrolling in this study. Consent for publication Not applicable. Competing interests The authors (Z.M., Y.J., JH.C., Y.G., X.L., and J.L.) declare that there were two patent applications related to this research (No. 202310300588.1 and No. 202310315701.3). Q.L., R.H., X.S., JL.C., and T.Y. have no conflicts of interest with regard to the content of this manuscript., (© 2024. The Author(s).)

TLK2 variants were identified as the cause for several neurodevelopmental disorders by impacting brain development. The incidence of mutation in TLK2 is low, which has common clinical features with other rare diseases. Herein, we reported a 5-year-old boy with TLK2 heterozygous mutation who presented distinctive facial features, gastrointestinal diseases, short stature, language delay, autism spectrum disorder, heart diseases, abnormal genitourinary system and skeletal abnormality. Moreover, we reviewed previous reported patients and our case in order to investigate more information on genotype-phenotype correlation and identify significant clinical characteristics for better diagnosis., Competing Interests: Declarations Ethics approval and consent to participate This study was approved by Ethical Committee of Children’s Hospital of Zhejiang University School of Medicine. and National Clinical Research Center for Child Health (no. 2024-IRB-0085-P-01). Consent for publication The written consent form for publication was obtained from the parents of the patient. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background: "Frank autism," recognizable through the first minutes of an interaction, describes a behavioral presentation of a subset of autistic individuals that is closely tied to social communication challenges, and may be linked to so-called "prototypical autism." To date, there is no research on frank autism presentations of autistic adolescents and young adults, nor individuals diagnosed with autism spectrum disorder (ASD) in childhood who do not meet diagnostic criteria during or after adolescence (loss of autism diagnosis, LAD). In addition, there are currently no data on the factors that drive frank autism impressions in these adolescent groups., Methods: This study quantifies initial impressions of autistic characteristics in 24 autistic, 24 LAD and 26 neurotypical (NT) individuals ages 12 to 39 years. Graduate student and expert clinicians completed five-minute impressions, rated confidence in their own impressions, and scored the atypicality of behaviors associated with impressions; impressions were compared with current gold-standard diagnostic outcomes., Results: Overall, clinicians' impressions within the first five minutes generally matched current gold-standard diagnostic status (clinical best estimate), were highly correlated with ADOS-2 CSS, and were driven primarily by prosodic and facial cues. However, this brief observation did not detect autism in all cases. While clinicians noted some subclinical atypicalities in the LAD group, impressions of the LAD and NT groups were similar., Limitations: The brief observations in this study were conducted during clinical research, including some semi-structured assessments. While results suggest overall concordance between initial impressions and diagnoses following more thorough evaluation, findings may not generalize to less structured, informal contexts. In addition, our sample was demographically homogeneous and comprised only speaking autistic participants. They were also unmatched for sex, with more females in the non-autistic group. Future studies should recruit samples that are diverse in demographic variables and ability level to replicate these findings and explore their implications., Conclusions: Results provide insights into the behavioral characteristics that contribute to the diagnosis of adolescents and young adults and may help inform diagnostic decision making in the wake of an increase in the demand for autism evaluations later than childhood. They also substantiate claims of an absence of apparent autistic characteristics in individuals who have lost the diagnosis., Competing Interests: Declarations Ethics approval and consent to participate The experimental protocol was approved by the University of Connecticut IRB. Consent for publication Not applicable. Competing interests Dr. Fein and Dr. Barton are co-owners of M-CHAT LLC, which licenses use of the M-CHAT-R in electronic products. The other authors declare that they have no conflict of interest., (© 2024. The Author(s).)

Autism spectrum disorder ("autism") is a neurodevelopmental condition characterized by substantial behavioral and neuroanatomical heterogeneity. This poses significant challenges to understanding its neurobiological mechanisms and developing effective interventions. Identifying phenotypically more homogeneous subgroups and shifting the focus from average group differences to individuals is a promising approach to addressing heterogeneity. In the present study, we aimed to parse clinical and neuroanatomical heterogeneity in autism by combining clustering of clinical features with normative modeling based on neuroanatomical measures (cortical thickness [CT] and subcortical volume) within the Autism Brain Imaging Data Exchange data sets (N autism = 861, N nonautistic individuals [N NAI] = 886, age range = 5-64). First, model-based clustering was applied to autistic symptoms as measured by the Autism Diagnostic Observation Schedule to identify clinical subgroups of autism (N autism = 499). Next, we ran normative modeling on CT and subcortical parcellations (N autism = 690, N NAI = 744) and examined whether clinical subgrouping resulted in increased neurobiological homogeneity within the subgroups compared to the entire autism group by comparing their spatial overlap of neuroanatomical deviations. We further investigated whether the identified subgroups improved the accuracy of autism classification based on the neuroanatomical deviations using supervised machine learning with cross-validation. Results yielded two clinical subgroups primarily differing in restrictive and repetitive behaviors (RRBs). Both subgroups showed increased homogeneity in localized deviations with the high-RRB subgroup showing increased volume deviations in the cerebellum and the low-RRB subgroup showing decreased CT deviations predominantly in the postcentral gyrus and fusiform cortex. Nevertheless, substantial within-group heterogeneity remained highlighted by the lack of improvement of the classifier's performance when distinguishing between the subgroups and NAI. Future research should aim to further reduce heterogeneity incorporating additional neuroanatomical clustering in even larger samples. This will eventually pave the way for more tailored behavioral interventions and improving clinical outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Problem: Diagnosing Autism Spectrum Disorder (ASD) remains a significant challenge, especially in regions where access to specialists is limited. Computer-based approaches offer a promising solution to make diagnosis more accessible. Eye tracking has emerged as a valuable technique in aiding the diagnosis of ASD. Typically, individuals' gaze patterns are monitored while they view videos designed according to established paradigms. In a previous study, we developed a method to classify individuals as having ASD or Typical Development (TD) by processing eye-tracking data using Random Forest ensembles, with a focus on a paradigm known as joint attention., Aim: This article aims to enhance our previous work by evaluating alternative algorithms and ensemble strategies, with a particular emphasis on the role of anticipation features in diagnosis., Methods: Utilizing stimuli based on joint attention and the concept of "floating regions of interest" from our earlier research, we identified features that indicate gaze anticipation or delay. We then tested seven class balancing strategies, applied seven dimensionality reduction algorithms, and combined them with five different classifier induction algorithms. Finally, we employed the stacking technique to construct an ensemble model., Results: Our findings showed a significant improvement, achieving an F1-score of 95.5%, compared to the 82% F1-score from our previous work, through the use of a heterogeneous stacking meta-classifier composed of diverse induction algorithms., Conclusion: While there remains an opportunity to explore new algorithms and features, the approach proposed in this article has the potential to be applied in clinical practice, contributing to increased accessibility to ASD diagnosis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The integration of artificial intelligence (AI) into the diagnosis and treatment of autism spectrum disorder (ASD) represents a promising frontier in healthcare. This review explores the current landscape and future prospects of AI technologies in ASD diagnostics and interventions. AI enables early detection and personalized assessment of ASD through the analysis of diverse data sources such as behavioural patterns, neuroimaging, genetics, and electronic health records. Machine learning algorithms exhibit high accuracy in distinguishing ASD from neurotypical development and other developmental disorders, facilitating timely interventions. Furthermore, AI-driven therapeutic interventions, including augmentative communication systems, virtual reality-based training, and robot-assisted therapies, show potential in improving social interactions and communication skills in individuals with ASD. Despite challenges such as data privacy and interpretability, the future of AI in ASD holds promise for refining diagnostic accuracy, deploying telehealth platforms, and tailoring treatment plans. By harnessing AI, clinicians can enhance ASD care delivery, empower patients, and advance our understanding of this complex condition., Competing Interests: Declaration of Competing Interest None Declared, (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objectives: Individuals with neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), often experience a higher prevalence of gastrointestinal (GI) symptoms but have complex medical and behavioral comorbidities that make diagnosis and treatment difficult. A multi-stakeholder conference was convened to (a) determine patient and family experiences related to GI symptoms in NDDs, (b) review the clinicians' and researchers' perspectives, and (c) determine actionable steps for future research., Methods: The Consortium for Autism, Neurodevelopmental Disorders and Digestive Diseases (CANDID; www.candidgi.com) virtually over 2 days in 2022 and consisted of four key activities: (1) an electronic family survey to assess underlying NDDs and GI symptoms, (2) a session focused on family perspectives, (3) review current clinical care and research, and (4) discussion to identify key next steps. Survey results were obtained electronically via the REDCap platform, and descriptive statistics were generated. The sessions were recorded, and themes were identified., Results: The pre-conference survey ran for ~2 months and 739 families provided responses, with 634 completing all items. 83% had a child with an NDD under age 18, and most patients were White (85%) and non-Hispanic (87%). Constipation (80%), GI reflux disease (51%), and bloating (49%) were the most frequently reported symptoms. Families gave unstructured feedback that the measures used in the surveys were often difficult to answer for patients with NDDs or who were nonspeaking. Family and clinical/scientific sessions identified several common themes, including (1) the need for less invasive diagnostic modalities, (2) the need to validate or adapt existing diagnostic measures (e.g., the Rome IV criteria) and outcome assessments, and (3) the need for enhanced attention to parent and caregiver input in treatment plans., Conclusions: Those providing care to children with NDDs, especially those with communication and cognitive challenges, should be aware of the differing needs in this community and consider family perspectives in managing, treating, and measuring GI issues. Future research should focus on adapting or creating diagnostic and research measures for those with NDDs, developing new diagnostic methods to account for diversity in neurodevelopment and communication, and improving methods for family and caregiver engagement in the care of GI disorders., (© 2024 European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition.)

Autistic individuals exhibit differences in their use and understanding of nonverbal communication; however, individual patterns of nonverbal strengths and challenges vary significantly. This heterogeneity can complicate the diagnostic and screening processes and can result in delayed or missed diagnoses. In this study, we characterize various profiles of nonverbal communication skills among 215 pre-verbal children with autism (M age  = 36.27 months, range = 18-70) and explore how these profiles are related to screening outcomes, diagnostic certainty, and developmental and behavioral features. We conducted a latent class analysis of nine items assessing nonverbal communication skills from the Toddler Module and Module 1 of the Autism Diagnostic Observation Schedule, 2nd Edition. Five nonverbal profiles were identified that differentiated children based on the form, function, and frequency of their nonverbal communication skills. Furthermore, screening outcomes and clinician certainty in autism diagnosis varied by nonverbal profile. False negative screening outcomes based on parent report were highest for children who used a range of nonverbal skills but with limited frequency or consistency. Clinicians, on the other hand, tended to have high certainty in an autism diagnosis for children with this profile, and instead rated their lowest certainty in diagnosing children who demonstrated consistent integration of eye contact with their nonverbal communication. The profiles identified in this study could be clinically useful in helping to identify children at highest likelihood of being overlooked during the screening or diagnostic processes, providing an opportunity to improve early identification and intervention for autism., (© 2024 International Society for Autism Research and Wiley Periodicals LLC.)

Introduction: Very preterm birth is an important risk factor for autism spectrum disorder (ASD). The aim of this study is the early detection of ASD risk, using a follow-up protocol, in children weighing less than 1500 g at birth or born before 32 weeks of gestation., Methods: This is a prospective longitudinal study in which a total of 133 very premature babies were monitored to the age of 2 years with the M-CHAT autism screening test and, in the event of a positive result, the Autism Diagnostic Observation Schedule (ADOS-2)., Results: 53 cases (4 out of 10) screened positive, and the rest negative. Among the positives, the ADOS-2 was administered in 50 cases, of which 24 scored above the ASD cutoff point. The average age of detection was 25.39 months. The results suggest an estimated prevalence of ASD in the very premature population of 18.46 %., Conclusions: The application of the follow-up protocol in the very premature population is effective for early detection of ASD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Objective/background: Insomnia is common in children with autism spectrum disorder (ASD). We recently developed and validated the 21-item Pediatric Autism Insomnia Rating Scale (PAIRS). This report explores the associations and agreements between actigraphy and PAIRS., Participants Methods: Children with ASD, with and without sleep problems, were assessed with a battery of parent-rated and clinician measures (N = 134). In a subset (n = 70), a wrist-worn actigraph measured sleep for five consecutive nights. Parents completed logs for scoring sleep intervals. Spearman correlations evaluated associations with the PAIRS and actigraphy indices (sleep onset latency = SOL, wake after sleep onset = WASO, total sleep time = TST, sleep efficiency = SE%). Agreements on "poor sleepers" based on PAIRS total score (≥33) and conventional thresholds for TST and SE% were evaluated with Cohen's Kappa and McNemar's test., Results: Actigraphy data were averaged over 4.64 ± 0.68 nights in 70 children (mean age = 7.3 ± 2.9, 74.3 % male). There were no significant correlations between PAIRS and any actigraphy indices. On TST, 48.6 % (n = 34) and on SE% 52.9 % (n = 37) were classified as "poor sleepers" compared to 32.9 % (n = 23) on PAIRS (kappa = 0.11 for TST and 0.27 for SE%). P-values on McNemar's Chi square test for PAIRS with TST and with SE% were 0.072 and 0.011, respectfully., Conclusions: These results suggest that actigraphy and PAIRS do not agree. Actigraphy TST captures movement and an estimate of specific sleep parameters. PAIRS is a broader measure that incorporates sleep disturbance and sleep-related impairment., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Dr. Scahill has served as a consultant to Yamo Pharmaceuticals and Cogstate. He receives book royalties from Oxford, Guilford and American Psychological Association and license fees from Yamo, Roche and Abbvie. Dr. Lecavalier receives book royalties from Oxford and license fees from Yamo, Roche and Abbvie. Dr. Johnson receives book royalties from Oxford and American Psychological Association. The authors have no other financial interests to disclose., (Copyright © 2024. Published by Elsevier B.V.)

Adolescents with Gender Incongruence - Special Case Constellations Abstract: Adolescents with gender incongruence and gender identity variants have a high rate of accompanying mental disorders, such as depression, autism spectrum disorders, or eating disorders. Yet, the interaction between gender incongruence, gender dysphoric distress, and accompanying mental disorders is complex and varies considerably from case to case. We need an individualized approach and careful professional assessment to help those affected and their guardians make informed decisions regarding possible treatment steps in complex case constellations. Maintaining careful process support and planning of the treatment steps can help to resolve blocked development processes in adolescents with gender incongruence and accompanying psychological disorders or in young people with unstable gender identity development.

In response to the global need for efficient early diagnosis of Autism Spectrum Disorder (ASD), this paper bridges the gap between traditional, time-consuming diagnostic methods and potential automated solutions. We propose a multi-atlas deep ensemble network, MADE-for-ASD, that integrates multiple atlases of the brain's functional magnetic resonance imaging (fMRI) data through a weighted deep ensemble network. Our approach integrates demographic information into the prediction workflow, which enhances ASD diagnosis performance and offers a more holistic perspective on patient profiling. We experiment with the well-known publicly available ABIDE (Autism Brain Imaging Data Exchange) I dataset, consisting of resting state fMRI data from 17 different laboratories around the globe. Our proposed system achieves 75.20% accuracy on the entire dataset and 96.40% on a specific subset - both surpassing reported ASD diagnosis accuracy in ABIDE I fMRI studies. Specifically, our model improves by 4.4 percentage points over prior works on the same amount of data. The model exhibits a sensitivity of 82.90% and a specificity of 69.70% on the entire dataset, and 91.00% and 99.50%, respectively, on the specific subset. We leverage the F-score to pinpoint the top 10 ROI in ASD diagnosis, such as precuneus and anterior cingulate/ventromedial. The proposed system can potentially pave the way for more cost-effective, efficient and scalable strategies in ASD diagnosis. Codes and evaluations are publicly available at https://github.com/hasan-rakibul/MADE-for-ASD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Recognizing Mendelian causes is crucial in molecular diagnostics and counseling for patients with autism spectrum disorder (ASD). We explored facial dysmorphism and facial asymmetry in relation to genetic causes in ASD patients and studied the potential of objective facial phenotyping in discriminating between Mendelian and multifactorial ASD. In a cohort of 152 ASD patients, 3D facial images were used to calculate three metrics: a computational dysmorphism score, a computational asymmetry score, and an expert dysmorphism score. High scores for each of the three metrics were associated with Mendelian causes of ASD. The computational dysmorphism score showed a significant correlation with the average expert dysmorphism score. However, in some patients, different dysmorphism aspects were captured making the metrics potentially complementary. The computational dysmorphism and asymmetry scores both enhanced the individual expert dysmorphism scores in differentiating Mendelian from non-Mendelian cases. Furthermore, the computational asymmetry score enhanced the average expert opinion in predicting a Mendelian cause. By design, our study does not allow to draw conclusions on the actual point-of-care use of 3D facial analysis. Nevertheless, 3D morphometric analysis is promising for developing clinical dysmorphology applications in diagnostics and training., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Within cohorts of children with autism spectrum disorder (ASD) there is considerable variation in terms of language ability. In the past, it was believed that children with ASD either had delayed articulation and phonology skills or excelled in those areas compared to other language domains. Very little is known about speech sound ability in relation to language ability and non-verbal ability in Swedish preschool children with ASD., Aim: The current study aimed to describe language variation in a group of 4-6-year-old children with ASD, focusing on in-depth analyses of speech sound error patterns with and without non-phonological language disorder and concomitant non-verbal delays., Method & Procedures: We examined and analysed the speech sound skills (including consonant inventory, percentage of correct consonants and speech sound error patterns) in relation to receptive language skills in a sample of preschool children who had screened positive for ASD in a population-based screening at 2.5 years of age. Seventy-three children diagnosed with ASD participated and were divided into subgroups based on their receptive language (i.e., non-phonological language) and non-verbal abilities., Outcomes & Results: The subgroup division revealed that 29 children (40%) had language delay/disorder without concurrent non-verbal general cognitive delay (ALD), 27 children (37%) had language delay/disorder with non-verbal general cognitive delay (AGD), and 17 children (23%) had language and non-verbal abilities within the normal range (ALN). Results revealed that children with ALD and children with AGD both had atypical speech sound error patterns significantly more often than the children with ALN., Conclusions & Implications: This study showed that many children who had screened positive for ASD before age 3 years - with or without non-verbal general cognitive delays - had deficits in language as well as in speech sound ability. However, individual differences were considerable. Our results point to speech sound error patterns as a potential clinical marker for language problems (disorder/delay) in preschool children with ASD., What This Paper Adds: What is already known on the subject Children with autism spectrum disorder (ASD) have deficits in social communication, restricted interests and repetitive behaviour. They show very considerable variation in both receptive and expressive language abilities. Previously, articulation and phonology were viewed as either delayed in children with ASD or superior compared with other (non-phonological) language domains. What this paper adds to existing knowledge Children with ASD and language disorders also have problems with speech sound error patterns. What are the potential or actual clinical implications of this work? About 75% of children with ASD experience language delays/disorders, as well as speech sound problems, related to speech sound error patterns. Understanding/acknowledging these phonological patterns and their implications can help in the diagnosis and intervention of speech sound disorders in children with ASD. Direct intervention targeting phonology might lead to language gains, but more research is needed., (© 2024 The Author(s). International Journal of Language & Communication Disorders published by John Wiley & Sons Ltd on behalf of Royal College of Speech and Language Therapists.)

Background: Favorable behavioral interactions are critical for academic and interpersonal success. An association between metopic synostosis and behavioral impairments has not been fully elucidated. Behavioral dysfunction in school-age children with surgically corrected metopic synostosis was evaluated using targeted testing to detect the most common behavioral abnormalities in this population., Methods: Parents of children 6 to 18 years of age with metopic synostosis completed the Conners Rating Scales, 3rd edition (Short Form) (Conners-3; attention-deficit/hyperactivity disorder), Social Responsiveness Scale, 2nd edition (SRS-2; autism spectrum disorder), Behavior Rating Inventory of Executive Function, 2nd edition (executive functioning), and Child's Behavioral Checklist (behavioral/emotional functioning). Children also completed neurocognitive testing. Multivariable regression was used to determine predictors of clinically significant behavioral impairments., Results: Sixty children were enrolled. Average age at surgery was 9.2 ± 7.9 months, with an average age at assessment of 10.3 ± 3.5 years. Nearly half of patients demonstrated symptoms associated with attention-deficit/hyperactivity disorder, demonstrated by reaching or exceeding borderline clinical levels for inattention and hyperactivity subscales of the Conners-3. Greater age at surgery was associated with worse executive function, measured by reaching or exceeding clinically significant levels of the executive function subscale of the Conners-3 ( P = 0.04) and subscales of the Behavior Rating Inventory of Executive Function, 2nd edition (Behavioral Regulator Index [ P = 0.05], Cognitive Regulatory Index [ P = 0.03], and Global Executive Composite [ P = 0.04])., Conclusions: Nearly half of patients with surgically corrected metopic synostosis reached borderline clinical scores for inattention and hyperactivity. Older age at surgery was associated with worse executive function. Prompt surgical correction of metopic synostosis may portend improved long-term emotional and behavioral function., Clinical Question/level of Evidence: Therapeutic, III., (Copyright © 2023 by the American Society of Plastic Surgeons.)

First-tier genetic investigations for patients with neurodevelopmental disorders (NDDs) may include chromosomal microarray, Fragile X testing, and screening for inherited metabolic diseases, but most remain undiagnosed upon completion of testing. Here, we report the diagnostic yields of genetic testing for 537 patients with at least one of autism spectrum disorder, global developmental delay, and/or intellectual disability. Patients were assessed in a single neurodevelopmental genetics clinic, and each underwent a standardized history and physical examination. Each patient was characterized as syndromic or nonsyndromic based on clinical features. Our results demonstrate that multigene sequencing (with an NDD gene panel or exome) had a higher diagnostic yield (8%; 95% confidence interval [CI]: 5%, 13%) than chromosomal microarray and Fragile X testing combined (4%; 95% CI: 3%, 7%). Biochemical screening for inherited metabolic diseases had a diagnostic yield of zero. The diagnostic yield of genetic testing was significantly higher for syndromic patients than for nonsyndromic patients (odds ratio [OR] 3.09; 95% CI: 1.46, 6.83) and higher for female patients than for male (OR 3.21; 95% CI: 1.52, 6.82). These results add to the growing evidence supporting a comprehensive genetic evaluation that includes both copy number analysis and sequencing of known NDD genes for patients with NDDs., (© 2024 Wiley Periodicals LLC.)

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by marked differences in communication patterns, reciprocal social interactions, and restricted, stereotyped, and repetitive interests and activities. Various behavioral problems in ASD, more so when accompanied by coexisting psychopathology, can sometimes lead to legal problems. In this study, the cases in which an opinion was requested in terms of criminal responsibility with the diagnosis of ASD in the 5-year period between 2018 and 2022 in the expertise department of psychiatric observation, where psychiatric cases were hospitalized and observed in the Council of Forensic Medicine (CFM), which is the official expert institution in Türkiye, were retrospectively evaluated. The mean age of the group whose criminal responsibility was reduced or removed was 22.9 years (±7.52) and the mean IQ score was 76.63 ± 18.94. The most common crime in this group was intentional injury (5/11), and it is noteworthy that the victims of these crimes were usually relatives of people with ASD (5/6). The criminal acts of people with ASD are usually single-movement, spontaneous, unplanned, impulsive acts. In addition, although there is no problem in cognitive perception in people with high functioning ASD (HF-ASD), various forensic situations may arise due to defects in emotional awareness. When we look at the practices of the CFM in Türkiye, it is seen that in cases where the diagnosis of ASD is clear and can be associated with the crime, criminal responsibility is usually completely eliminated. In HF-ASD types, although it is important to be associated with the crime, it is seen that criminal responsibility is generally reduced., (© 2024 American Academy of Forensic Sciences.)

Aim: To identify on a population basis the prevalence of autism and attention-deficit/hyperactivity disorder (ADHD) in children with Dravet syndrome and factors associated with symptoms of autism and ADHD., Method: Forty-one of 48 children with Dravet syndrome living in Sweden, born between 1st January 2000 and 31st December 2018 underwent assessment including measures of autism and ADHD. Diagnoses of autism and ADHD were made with respect to DSM-5 criteria. Factors associated with features of autism and ADHD were analysed via regression., Results: Twenty-five of the 41 children fulfilled DSM-5 criteria for autism spectrum disorder and 12 of 37 children considered for an ADHD diagnosis fulfilled DSM-5 criteria for ADHD. Severe intellectual disability was significantly associated with a greater degree of autistic features (p < 0.001) and a DSM-5 diagnosis of autism spectrum disorder (p = 0.029). Younger children had significantly more features of ADHD (p = 0.004) and features of inattention were significantly more common than features of hyperactivity/impulsivity (p < 0.001)., Interpretation: Children with Dravet syndrome often have significant features of autism and ADHD, primarily inattentive type. Screening for autism and ADHD should be routine in children with Dravet syndrome., What This Paper Adds: In total, 25 of 41 assessed children with Dravet syndrome fulfilled DSM-5 criteria for autism. Twelve of 37 assessed children with Dravet Syndrome met DSM-5 criteria for attention-deficit/hyperactivity disorder (ADHD). Severe intellectual disability was significantly associated with a greater degree of autism spectrum disorder features. Younger children had significantly more features of ADHD., (© 2024 Mac Keith Press.)

Background: Autism spectrum disorder (ASD) is a multifactorial, pervasive, neurodevelopmental disorder, of which intestinal symptoms collectively represent one of the most common comorbidities., Methods: In this study, 1,222 children with ASD and 1,206 typically developing (TD) children aged 2-7 years were enrolled from 13 cities in China. Physical measurement and basic information questionnaires were conducted in ASD and TD children. The Childhood Autism Rating Scale (CARS), Social Responsiveness Scale (SRS), and Autism Behavior Checklist (ABC) were used to evaluate the clinical symptoms of children with ASD. The six-item Gastrointestinal Severity Index (6-GSI) was used to evaluate the prevalence of intestinal symptoms in two groups., Results: The detection rates of constipation, stool odor, and total intestinal symptoms in ASD children were significantly higher than those in TD children (40.098% vs. 25.622%, 17.021% vs. 9.287%, and 53.601% vs. 41.294%, respectively). Autistic children presenting with intestinal comorbidity had significantly higher scores on the ABC, SRS, CARS, and multiple subscales than autistic children without intestinal symptoms, suggesting that intestinal comorbidity may exacerbates the core symptoms of ASD children., Conclusion: Intestinal dysfunction was significantly more common in autistic than in TD children. This dysfunction may aggravate the core symptoms of children with ASD., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Early intervention is known to improve long-term outcomes for individuals with autism spectrum disorder (ASD). Access barriers to care limit timely engagement with supportive services. This report characterized the community needs and supportive services for children and families with suspected or diagnosed ASD. Families and providers participating in outreach clinics identified available services and their attitudes about support for ASD diagnosis. Chart reviews provided referral history, insurance, and current services. Children were nearly 6 years old, 95% of families relied on public health insurance, whereas 50% reported traveling 11 miles or greater for supportive services. Most providers (83%) were medically trained in primary care and placed 1-5 referrals per month to a tertiary referral hospital. Providers reported travel difficulty as the primary reason for referring patients for evaluation. Multiple barriers for supportive services were identified, highlighting the importance to increase the capacity and availability of local ASD supportive services., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

People on the autism spectrum can learn about autism from various sources, likely differing in the information, portrayal, and discussion they offer. The present study investigates where autistic people learn about autism, and whether their information source is associated with their level of autism knowledge, perceptions of stigma, and development and expression of an autism identity. A survey of 198 Australian adults with an autism diagnosis showed that learning about autism from conventional sources (e.g., professionals, parents) was associated with more internalised stigma, lower endorsement of special abilities and autism identity, whereas online blogs and social media showed the opposite pattern as well as more accurate knowledge of autism. The findings raise questions about how authoritative sources of information discuss autism., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This study was designed to validate the Arabic version of the Autism Behavior Checklist (ABC) for the Egyptian population. A total of 500 mothers of children aged 4-14 years, of whom 150 had a diagnosis of ASD, 100 with intellectual disability, and 250 typically developing children completed the ABC. The factor analysis showed that 48 of 57 ABC items yielded a five-dimensional factor structure. The ABC-Arabic version indicated acceptable internal consistency (α = 0.85) and test - retest reliability (0.82). Also, the ABC exhibited good concurrent validity and discriminative validity. A cutoff score of 58 obtained a sensitivity of 94.7% and a specificity of 92.14% for detecting children with ASD. Our findings support the use of the ABC as a valid screening measure for ASD cases, and it may promote the use of the ABC for clinical and research purposes among Arabic-speaking communities.

Purpose: Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum., Methods: We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data., Results: The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum., Conclusion: Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs., (© 2023. The Author(s).)

When observing other people during naturally paced and dynamic interactions, it is essential to look at specific locations at the right time to extract a maximum of socially informative content. In this study, we aimed to investigate the looking behavior of typically developing toddlers and toddlers later diagnosed with autism when observing other children interact. The sample consisted of 98 toddlers; 22 in a low-likelihood of autism group, 60 in an elevated likelihood of autism group who did not receive a subsequent diagnosis, and 16 in an elevated likelihood group who did receive an autism diagnosis. Participants performed an eye tracking task at 18 months of age and were assessed for diagnostic outcome at 36 months. The video stimuli consisted of two children interacting, where a boy reaches out for a toy and a girl refuses to give it to him. The low likelihood group showed an expected increase in ratio of looking at the girl's face after the boy requested the toy, as compared to before (t(21) = -3.337, p = .003). Toddlers with later autism showed a significantly lower ratio of looking at the girl's face during this time window, as compared to the other groups (F(2,91) = 3.698, p = .029). These findings provide new leads on how social gaze may be different in children with autism in everyday life (e.g., kindergarten), and highlight the need of studying the dynamics of gaze on short time scales., (© 2023. The Author(s).)

The diagnostic criteria for autism spectrum disorder in the DSM-5-TR features the option to designate levels of support for social communication (SC) and restricted, repetitive behaviors (RRB). These levels are conceptual in nature, but research indicates standardized assessment outcomes correspond with clinician-assigned levels of support. The purpose of the present study was to identify factors that influence designated levels of support for SC and RRBs when diagnosing autism. Standardized assessment scores across intellectual functioning, adaptive skills, and ASD symptomology were analyzed to determine corresponding levels of support in SC and RRBs assigned by clinicians for 136 autistic children following a comprehensive diagnostic evaluation. At diagnosis, approximately 46% of participants were described as needing substantial support (Level 2) for SC and 49% were described as needing substantial support (Level 2) for RRB. There was a consistent pattern of higher to lower intellectual and adaptive functioning needing Level 1-Level 3 support. Autism assessment results followed a gradient of fewer to greater autism symptoms from Level 1 to Level 3 support. Findings indicated clinician-assigned levels of support for SC and RRB were associated with intellectual functioning, adaptive functioning, autism symptomology, and age, but not sex., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: The Social Responsiveness Scale (SRS) is frequently used in research settings to measure characteristics associated with autism spectrum disorders (ASD). A short version has been developed but not yet tested for certain properties of the full SRS, such as familiality. The purpose of this study was to determine if prior familiality findings for the full SRS can be replicated using the short form by measuring the associations of the parental Social Responsiveness Scale-Short Form (SRS-SF) scores with child ASD diagnoses and child SRS-SF scores., Methods: We used a nested case-control study within a longitudinal cohort study design. Participants were selected from the Nurses' Health Study II (NHS II). Cases were children of study participants who had been diagnosed with ASD, while controls had not been diagnosed with ASD and were frequency matched by year of birth to cases. 2144 out of 3161 eligible participants returned SRS forms for a child and at least one parent. Participants in NHS II completed SRS forms for their spouses and spouses completed SRS forms for NHS II participants. Parental SRS-SF scores were based on a subset of 16 questions from the SRS. ASD diagnosis among children was reported by the mothers and validated in a subset using the Autism Diagnostic Interview-Revised, as well as child SRS-SF scores., Results: Children whose parents both had elevated SRS-SF scores (those in the top 20% of the study distribution) had a higher odds of ASD diagnosis than those who did not have elevated parental scores (OR 2.25; 95% CI 1.41, 3.58). Additionally, children whose fathers had elevated SRS-SF scores had a higher odds of ASD diagnosis (OR 2.18; 95% CI 1.60, 2.97) than those whose fathers scores were not elevated. In sex-stratified analyses, male children with elevated parental SRS-SF scores had a higher odds of ASD diagnosis than those who did not have elevated parental scores. These associations were not as evident among female children. Parental SRS-SF scores also predicted child SRS-SF scores among controls., Conclusion: These findings are similar to prior findings for the full SRS and support the ability of the SRS-SF to capture familiality of ASD-related traits., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Use of telehealth assessments for toddlers at increased likelihood of autism spectrum disorder (ASD) began prior to the global COVID-19 pandemic; however, the value of telehealth assessments as an alternative to in-person assessment (IPA) became clearer during the pandemic. The Naturalistic Observation Diagnosis Assessment (NODA™), previously demonstrated as a valid and reliable tool to evaluate asynchronous behaviors for early diagnosis, was enhanced to add synchronous collection of behaviors to assist clinicians in making a differential diagnosis of ASD. This study was conducted to validate the information gathered through NODA-Enhanced (NODA-E™) as compared to a gold standard IPA. Forty-nine toddlers aged 16.0-32.1 months of age, recruited through community pediatric offices and a tertiary ASD clinic, participated in both NODA-E and IPA assessments. There was high agreement between the two assessment protocols for overall diagnosis (46 of 49 cases; 93.6%; κ = .878), specific diagnostic criteria for social communication and social interaction (SCI; range 95.9-98%; κ = .918-.959), and for two of four criteria specified for restricted and repetitive behaviors (RRB; range 87.8-98%; κ = .755 and .959). There was lower agreement for two subcategories of RRBs (range 65.3-67.3%; κ = .306 and .347). NODA-E is a tool that can assist clinicians in making reliable and valid early ASD diagnoses using both asynchronous and synchronous information gathered via telehealth and offers an additional tool within a clinician's assessment toolbox., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: To establish the utility of long-term electroencephalogram (EEG) in forecasting epilepsy onset in children with autism spectrum disorder (ASD)., Study Design: A single-institution, retrospective analysis of children with ASD, examining long-term overnight EEG recordings collected over a period of 15 years, was conducted. Clinical EEG findings, patient demographics, medical histories, and additional Autism Diagnostic Observation Schedule data were examined. Predictors for the timing of epilepsy onset were evaluated using survival analysis and Cox regression., Results: Among 151 patients, 17.2% (n = 26) developed unprovoked seizures (Sz group), while 82.8% (n = 125) did not (non-Sz group). The Sz group displayed a higher percentage of interictal epileptiform discharges (IEDs) in their initial EEGs compared with the non-Sz group (46.2% vs 20.0%, P = .01). The Sz group also exhibited a greater frequency of slowing (42.3% vs 13.6%, P < .01). The presence of IEDs or slowing predicted an earlier seizure onset, based on survival analysis. Multivariate Cox proportional hazards regression revealed that the presence of any IEDs (HR 3.83, 95% CI 1.38-10.65, P = .01) or any slowing (HR 2.78, 95% CI 1.02-7.58, P = .046 significantly increased the risk of developing unprovoked seizures., Conclusion: Long-term EEGs are valuable for predicting future epilepsy in children with ASD. These findings can guide clinicians in early education and potential interventions for epilepsy prevention., Competing Interests: Declaration of Competing Interest A.D. is supported by Uehara Memorial Foundation, and SENSHIN Medical Research Foundation to research abroad. H.N. is supported by the National Institute of Neurological Disorders and Stroke (NINDS) K23NS128318, the Sudha Neelakantan & Venky Harinarayan Charitable Fund, the Elsie and Isaac Fogelman Endowment, and the UCLA Children's Discovery and Innovation Institute (CDI) Junior Faculty Career Development Grant (#CDI-TTCF-07012021). R.S. serves on scientific advisory boards and speakers bureaus and has received honoraria and funding for travel from Eisai, Greenwich Biosciences, UCB Pharma, Sunovion, Supernus, Lundbeck Pharma, Liva Nova, West Therapeutics (advisory only); receives royalties from the publication of Pellock's Pediatric Neurology (Demos Publishing, 2016) and Epilepsy: Mechanisms, Models, and Translational Perspectives (CRC Press, 2011). S.H. has received research support from the John C. Hench Foundation, the CJDA Foundation, the Mohammed F. Alibrahim Endowment, the Elsie and Isaac Fogelman Endowment, the Epilepsy Therapy Project, the Milken Family Foundation, Paul Hughes Family Foundation, the Pediatric Epilepsy Research Foundation, Eisai, Bio-Pharm, Lundbeck, Insys, GW Pharmaceuticals, UCB Biopharma, Zogenix, Marinus, and the NIH. He has received compensation for service as a consultant to Amzell, Aquestive Therapeutics, Equilibre Biopharmaceuticals, Insys, GW Pharmaceuticals, Mallinckrodt, Marinus, MGC Pharmaceuticals, Radius, Shennox, UCB Biopharma, Upsher-Smith Laboratories, West Therapeutic Development, and Zogenix. The other authors declare no conflicts of interest. The funder/sponsor did not participate in the work., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Low-pass whole genome sequencing (LP-WGS) has been applied as alternative method to detect copy number variants (CNVs) in the clinical setting. Compared with chromosomal microarray analysis (CMA), the sequencing-based approach provides a similar resolution of CNV detection at a lower cost. In this study, we assessed the efficiency and reliability of LP-WGS as a more affordable alternative to CMA. A total of 1363 patients with unexplained neurodevelopmental delay/intellectual disability, autism spectrum disorders, and/or multiple congenital anomalies were enrolled. Those patients were referred from 15 nonprofit organizations and university centers located in different states in Brazil. The analysis of LP-WGS at 1x coverage (>50kb) revealed a positive testing result in 22% of the cases (304/1363), in which 219 and 85 correspond to pathogenic/likely pathogenic (P/LP) CNVs and variants of uncertain significance (VUS), respectively. The 16% (219/1363) diagnostic yield observed in our cohort is comparable to the 15%-20% reported for CMA in the literature. The use of commercial software, as demonstrated in this study, simplifies the implementation of the test in clinical settings. Particularly for countries like Brazil, where the cost of CMA presents a substantial barrier to most of the population, LP-WGS emerges as a cost-effective alternative for investigating copy number changes in cytogenetics., (© 2024 Wiley Periodicals LLC.)

The concept of autism has led to the inclusion of increasingly heterogeneous and atypical individuals. Clinicians find it difficult to establish clear boundaries between autism, other psychiatric and/or neurodevelopmental disorders and atypical individuals, despite standardized instruments. What's more, these standardized instruments have been built around the male phenotype, and do not allow for an accurate assessment of autism in women. Whatever the gender, the diagnosis is based on multiple, variable, disabling and persistent deficits. Finally, we'll mention a few recommendations to help clinicians make a diagnosis or invoke the precautionary principle., Competing Interests: les auteurs n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Background: The use of the deep learning (DL) approach has been suggested or applied to identify childhood autism spectrum disorder (ASD). The capacity to predict ASD, however, differs across investigations. Our study's objective was to conduct a meta-analysis to determine the DL for ASD in children's classification accuracy., Methods: Eligibility criteria were designed according to the purpose of the meta-analysis; PubMed, EMBASE, Cochrane Library, and Web of Science Database were searched for articles published up to April 16, 2023, on the accuracy of DL methods for ASD classification. Using the Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) to assess the quality of the included studies. Sensitivity, specificity, areas under the curve (AUC), summary receiver operating characteristic (SROC), and corresponding 95% confidence intervals (CIs) were compiled by using the bivariate random-effects models., Results: A total of 11 predictive trials based on DL models were included, involving 9495 ASD patients from 6 different databases. According to bivariate random-effects models' results, the overall sensitivity, specificity, and AUC of the DL technique for ASD were, 0.95 (95% CI = 0.88-0.98), 0.93 (95% CI = 0.85-0.97), and 0.98 (95%CI: 0.97-0.99), respectively. Subgroup analysis results found that different datasets did not cause heterogeneity (meta-regression P = 0.55). The Kaggle dataset's sensitivity and specificity were 0.94 (95%CI: 0.82-1.00) and 0.91 (95%CI: 0.76-1.00), and with 0.97 (95%CI: 0.92-1.00) and 0.97 (95%CI: 0.92-1.00) for ABIDE dataset., Conclusions: DL techniques has satisfactory sensitivity, specificity, and AUC in ASD classification. However, the major heterogeneity of the included studies limited the effectiveness of this meta-analysis. Further trials need to be performed to demonstrate the clinical practicability of DL diagnosis., (© 2024. The Author(s).)

Background: Gesture difficulties have been reported in later-born siblings of children with autism spectrum disorder (ASD). Careful observation of gesture development during the first two years of children at elevated likelihood (EL) of developing ASD may identify behavioral indicators that facilitate early diagnosis., Methods: This study enrolled 47 EL infants and 27 low-likelihood (LL) infants to explore gesture developmental trajectories and the predictive value of gesture to expedite the early detection of core characteristics of ASD. Gesture frequency, communication function, and integration ability were observed and coded from a semi-structured assessment administered longitudinally across 9-19 months of age. We conducted the Autism Diagnostic Observation Schedule assessment at 18-19 months for ASD's core characteristics., Results: The development of joint attention (JA) gestures was slower in the EL than in the LL group. The trajectories of the two groups began to diverge at 14-18 months. Children who reached the diagnostic cutoff point for ASD showed reductions in social interaction gestures at 12-13 months, in gestures integrated with any two communication skills (G-M) at 15-16 months; and in gestures integrated with eye contact (G-E) at 18-19 months. Overall gesture and G-M integration were associated with an overall ADOS communication and social interaction score., Conclusions: The developmental trajectories of JA gestures of EL and LL children differed. G-M gestures represent early indicators that may be a predictor of ASD., (© 2024. The Author(s).)

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder. Early diagnosis in the critical period is important for ASD children. Recent studies of neurodevelopmental behavioral features and joint attention in high-risk infants showed there are some special cues which can distinguish ASD from typical development infant. But the findings of high-risk population may not be applicable to the general population. It is necessary to "analogically" study the potential warning traits of ASD in infancy in the general population. We did a nested case-control study from June 2019 to November 2022 in Tianjin, China, including 76 general infants whom completed the neurodevelopmental evaluation, the Checklist for Autism in Toddlers-23 (CHAT-23) screening, and eye tracking task. Social behavior quotient in infancy was negatively correlated to CHAT-23 total scores in toddlerhood. Social behavior quotient in infancy was positively correlated to initiating joint attention in toddlerhood. Regression model showed that high fine motor scale and social behaviour scale quotient in infancy were associated with an decreased risk of the total score of CHAT-23 ≥ 2 in toddlerhood. The Receiver operating characteristic curve showed the social behaviour in infancy alone and the combination of fine motor and social behaviour in infancy contributed to auxiliary diagnosis of higher level of autistic traits in toddlerhood. These findings suggest that Impaired development of fine motor and social behavior in infancy are potential warning features of high autistic traits in general population., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Background: Neurodevelopmental disorders (NDDs) represent a significant challenge in pediatric genetics, often requiring advanced diagnostic tools for the accurate identification of genetic variants., Objectives: To determine the diagnostic yield of whole exome sequencing (WES) with targeted gene panels in children with neurodevelopmental disorders (NDDs)., Methods: This observational, prospective study included a total of 176 Spanish-speaking pediatric patients with neurodevelopmental disorders (NDDs), encompassing intellectual disability (ID), global developmental delay (GDD), and/or autism spectrum disorder (ASD). Participants were recruited from January 2019 to January 2023 at a University Hospital in Madrid, Spain. Clinical and sociodemographic variables were recorded, along with genetic study results. The age range of the subjects was 9 months to 16 years, and the percentage of males was 72.1%. The diagnostic yield of whole exome sequencing (WES) was calculated both before and after parental testing via Sanger DNA sequencing., Results: The study included 176 children: 67 (38.1%) with ID, 62 (35.2%) with ASD, and 47 (26.7%) with ASD + ID. The diagnostic yield of proband-only exome sequencing was 12.5% (22/176). By group, the diagnostic yield of proband-only exome sequencing was 3.2% in the ASD, 12.7% in the ASD + ID, and 20.8% in the ID group. Variants of uncertain significance (VUS) were found in 39.8% (70/176). After parental testing, some variants were reclassified as "likely pathogenic", increasing the diagnostic yield by 4.6%, with an overall diagnostic yield of 17.1%. Diagnostic yield was higher in patients with syndromic ID (70.6%% vs. 29.4%; p = 0.036)., Conclusions: A sequential approach utilizing WES followed by panel-based analysis, starting with the index case and, when appropriate, including the parents, proves to be a cost-effective strategy. WES is particularly suitable for complex conditions, as it allows for the identification of potentially causative genes beyond those covered by targeted panels, providing a more comprehensive analysis. Including parental testing enhances the diagnostic yield and improves accuracy, especially in cases with variants of uncertain significance (VUS), thereby advancing our understanding of NDDs.

A timely diagnosis of autism is paramount to allow early therapeutic intervention in preschoolers. Deep Learning tools have been increasingly used to identify specific autistic symptoms. But they also offer opportunities for broad automated detection of autism at an early age. Here, we leverage a multi-modal approach by combining two neural networks trained on video and audio features of semi-standardized social interactions in a sample of 160 children aged 1 to 5 years old. Our ensemble model performs with an accuracy of 82.5% (F1 score: 0.816, Precision: 0.775, Recall: 0.861) for screening Autism Spectrum Disorders (ASD). Additional combinations of our model were developed to achieve higher specificity (92.5%, i.e., few false negatives) or sensitivity (90%, i.e. few false positives). Finally, we found a relationship between the neural network modalities and specific audio versus video ASD characteristics, bringing evidence that our neural network implementation was effective in taking into account different features that are currently standardized under the gold standard ASD assessment., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Natraj et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Importance: An improved understanding of autism spectrum disorder (ASD) prevalence over time and across the lifespan can inform health care service delivery for the growing population of autistic children and adults., Objective: To describe trends in the prevalence of ASD diagnoses using electronic records data from a large network of health systems in the US., Design, Setting, and Participants: This cross-sectional study examined annual diagnosis rates in health records of patients in US health systems from January 1, 2011, to December 31, 2022. Eligible individuals were included in the study sample for a given calendar year if they were enrolled in a participating health system for at least 10 months out of the year. Data were extracted from 12 sites participating in the Mental Health Research Network, a consortium of research centers embedded within large, diverse health care systems., Main Outcome and Measures: Diagnoses of ASD were ascertained using International Classification of Diseases, Ninth Revision (ICD-9) and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) revision codes. Annual diagnosis rates were calculated as the number of unique members diagnosed, divided by the total members enrolled., Results: A total of 12 264 003 members were enrolled in 2022 (2 359 359 children aged 0 to 17 years [19.2%]; 6 400 222 female [52.2%]; 93 002 American Indian or Alaska Native [0.8%], 1 711 950 Asian [14.0%], 952 287 Black or African American [7.8%], 2 971 355 Hispanic [24.2%], 166 144 Native Hawaiian or Pacific Islander [1.4%], and 6 462 298 White [52.7%]). The ASD diagnosis rate was greatest among 5-to-8-year-olds throughout the study period and increased by 175% among the full sample, from 2.3 per 1000 in 2011 to 6.3 per 1000 in 2022. The greatest relative increase in diagnosis rate from 2011 to 2022 occurred among 26-to-34-year-olds (450%) and increases were greater for female vs male individuals among children (305% [estimated annual percentage change (EAPC), 13.62 percentage points; 95% CI, 12.49-14.75 percentage points] vs 185% [EAPC, 9.63 percentage points; 95% CI, 8.54-10.72 percentage points], respectively) and adults (315% [EAPC, 13.73 percentage points; 95% CI, 12.61-14.86 percentage points] vs 215% [EAPC, 10.33 percentage points; 95% CI, 9.24-11.43 percentage points]). Relative increases were greater in racial and ethnic minority groups compared with White individuals among children, but not adults., Conclusions and Relevance: In this cross-sectional study of children and adults in the US, ASD diagnosis rates increased substantially between 2011 and 2022, particularly among young adults, female children and adults, and children from some racial or ethnic minority groups. Diagnosis prevalence trends generated using health system data can inform the allocation of resources to meet the service needs of this growing, medically complex population.

This study evaluated the effectiveness of a 3-session group intervention for parents who had received a diagnosis of autism for their child within the past month. The intervention group ( N = 41) was compared to Treatment-as-Usual ( N = 40): one meeting with a social worker after the diagnosis feedback meeting. Parental stress was evaluated in both groups within a week and then a month after the diagnosis. The findings indicate an increase in the experienced parental stress for the comparison group on all six indices, while in the intervention group there was an increase only on two indices. That is to say, the intervention reduced stress that occurred in the first month after the diagnosis. Further analyses revealed that parent satisfaction with the group intervention was the single most important variable in predicting stress reduction. We argue that parent support groups immediately after their child's diagnosis are effective and important, and probably superior to a single post-diagnosis meeting., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Dravet syndrome (DS) presents a multifaceted clinical picture marked by epilepsy, cognitive impairments and behavioral disorders that progresses throughout development. Behavioral disorders include impairments in social relationships and communication, with frequent diagnosis of autism spectrum disorder. This study focused on comprehensively evaluating and comparing social communication profiles among a group of 43 children with Dravet syndrome, 30 children with level 1 autism spectrum disorder, 36 with social (pragmatic) communication disorder, and 18 with intellectual disability. Using validated tools like the Childhood Autism Spectrum Test and Children's Communication Checklist, distinct patterns of social communication deficits were delineated. Our findings indicate that children with Dravet syndrome experience challenges in social relationships, primarily due to difficulties in use of pragmatic language. Areas such as range of interests and social interaction are less affected compared to those with ASD, emphasizing differing profiles between the conditions. While children with DS and ID may have similar intellectual functioning, the different social communication deficits in DS indicate their role in the DS phenotype beyond ID. These results underscore the unique social communication profile of DS and emphasizes the importance of tailored interventions and deep phenotyping efforts for effective DS management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

To comprehensively investigate the neurodevelopmental profile and clinical characteristics associated with SETBP1 haploinsufficiency disorder (SETBP1-HD) and SETBP1-related disorders (SETBP1-RD). We reported genetic results on 34 individuals, with behavior and clinical data from 22 with SETBP1-HD and 5 with SETBP1-RD, by assessing results from medical history interviews and standardized adaptive, clinical, and social measures provided from Simons Searchlight. All individuals with SETBP1-HD and SETBP1-RD exhibited neurological impairments including intellectual disability/developmental delay (IDD), attention-deficit/hyperactivity disorder, autism spectrum disorder, and/or seizures, as well as speech and language delays. While restricted interests and repetitive behaviors present challenges, a relative strength was observed in social motivation within both cohorts. Individuals with SETBP1-RD reported a risk for heart issues and compared to SETBP1-HD greater risks for orthopedic and somatic issues with greater difficulty in bowel control. Higher rates for neonatal feeding difficulties and febrile seizures were reported for individuals with SETBP1-HD. Additional prominent characteristics included sleep, vision, and gastrointestinal issues, hypotonia, and high pain tolerance. This characterization of phenotypic overlap (IDD, speech challenges, autistic, and attention deficit traits) and differentiation (somatic and heart issue risks for SETBP1-RD) between the distinct neurodevelopmental disorders SETBP1-HD and SETBP1-RD is critical for medical management and diagnosis., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Lay Abstract: Autism is a neurodevelopmental condition, characterized by social communication alterations and restricted, repetitive behaviors. Typically diagnosed in early childhood, screening and diagnosis at a later age can be challenging, particularly in girls who exhibit a wider range of behaviors and characteristics. Our study set out to examine the effectiveness of the Hebrew translation of the Childhood Autism Spectrum Test, a parent report questionnaire, in identifying these diverse characteristics of autism within an Israeli sample of boys and girls. We examined parent reports on 403 (211 autistic, 192 non-autistic) children, aged 4-12 years. Results revealed the Childhood Autism Spectrum Test-Hebrew version was a valuable tool in differentiating between autistic and typically developing children, correctly identifying 93% of children with autism and 82% of typically developing children. In addition, specific items of the Childhood Autism Spectrum Test-Hebrew version were particularly useful in differentiating between autistic and non-autistic boys and autistic and non-autistic girls. Using these items, in addition to the overall score of the questionnaire, increased the correct identification of children as autistic or typically developing, especially in girls. The Childhood Autism Spectrum Test-Hebrew version test results corresponded well with the Autism Diagnostic Interview-Revised, which relies on parental input, but not with the clinician-administered Autism Diagnostic Observation Schedule-2. Our findings highlight the potential benefits of gender-specific tools to better support correct identification of autism in boys and in girls. More research is recommended to further explore these gender differences and to validate our findings with a larger, diverse group., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objectives: Many barriers to implementation of developmental screening in primary care exist, especially for children from under-resourced communities. Developmental screening is vital to early detection of developmental delay and autism spectrum disorder, and early intervention (EI) referral. This study sought to examine whether implementation of a standardized clinical workflow using electronic screening tools improved both rates of developmental screening, and the number of children identified at risk for developmental delay, in a federally qualified health center (FQHC)., Methods: A retrospective study was conducted at an academic-affiliated FQHC. Electronic versions of the Ages and Stages Questionnaire 3 (ASQ-3) and Modified Checklist in Autism for Toddlers Revised (M-CHAT-R) were implemented at well-child visits. New clinical workflow training on developmental screening and EI referral was provided. Chi-square and Fisher's Exact analyses were conducted., Results: ASQ-3 screening rates increased from 62.7 to 73.6% pre- to post-intervention. Post-intervention, there was a significant decrease in paper screens (p < .001), and a significant increase in the percentage of children with ASQ-3 results in the below cutoff range from 14.7 to 18.2% (p < .002). M-CHAT-R screening rates increased from 56.4 to 59.4% pre- to post-intervention. Post-intervention, there was a significant increase in electronic screens (p < .001)., Conclusions for Practice: Implementation of electronic screening tools improved universal developmental screening in a FQHC. To decrease barriers in under-resourced communities, the use of electronic tools may decrease the rate of screening error seen with paper screening and have the potential to better identify children at risk for developmental delay., (© 2024. The Author(s).)

Motor delays in children with autism spectrum disorder (ASD) are being increasingly recognized using a brief screening tool, called the Developmental Coordination Disorder-Questionnaire (DCD-Q). Further validation of these motor delays using a more robust normed, developmental measure is clearly warranted. In this analysis, a nationally representative sample from the SPARK study was used wherein parents completed the DCD-Q and a more widely used developmental/adaptive functioning measure, called the Vineland Adaptive Behavior Scales (VABS); which comprises of various developmental domains including the motor domain (N = 2,644 completed the DCD-Q and VABS). Eighty two percent children with ASD had a motor delay based on their DCD-Q scores whereas 77% children with ASD had a motor delay based on their VABS motor domain scores. Approximately 70% children with ASD had concurrent motor delay on the DCD-Q and the VABS (i.e., positive predictive value of DCD-Q). Furthermore, there was 81.2% accuracy in reporting a risk/no risk of motor delay across both measures. Overall, these statistics align with the recent reports on proportions of children with ASD having motor delays. Parents of ~70% children with ASD are reporting motor delays that are corroborated across two different motor measures. This not only validates the motor delays reported based on the DCD-Q but also indicates the need for concurrent motor screening using both DCD-Q and VABS for better detection of motor delays in children with ASD. Only 10%-32% of the current SPARK sample received any physical or recreational therapies. This mismatch between presence of motor delays and the lack of access to motor services highlights the need for more motor intervention referrals for children with ASD., (© 2024 International Society for Autism Research and Wiley Periodicals LLC.)

Altered motor and social-communicative abilities in infancy have been linked to later ASD diagnosis. Most diagnostic instruments for ASD cannot be utilized until 12 months, and the average child is diagnosed substantially later. Imitation combines motor and social-communicative skills and is commonly atypical in infants at risk for ASD. However, few measures have been developed to assess infant imitation clinically. One barrier to the diagnostic age gap of ASD is accessibility of screening and diagnostic services. Utilization of caregiver report to reliably screen for ASD mitigates such barriers and could aid in earlier detection. The present study developed and validated a caregiver-report measure of infant imitation at 4, 6, and 9 months and explored the relationship between caregiver-reported imitation and motor abilities with later ASD risk. Participants ( N  = 571) were caregivers of term and preterm infants recruited as part of a large multi-site study of PediaTrac™, a web-based tool for monitoring and tracking infant development. Caregivers completed online surveys and established questionnaires on a schedule corresponding to well-child visits from birth to 18 months, including the M-CHAT-R/F at 18 months. Distinct imitation factors were derived from PediaTrac at 4, 6, and 9 months via factor analysis. The results supported validity of the imitation factors via associations with measures of infant communication (CSBS; ASQ). Imitation and motor skills at 9 months predicted 18-month ASD risk over and above gestational age. Implications for assessment of infant imitation, detecting ASD risk in the first year, and contributing to access to care are discussed.

The reported prevalence of autism spectrum disorder (ASD) has more than tripled in the past two decades in the United States, due in part to improved screening and diagnostic techniques. Epidemiologic data on ASD, however, are largely limited to population-based surveillance systems. We examined epidemiologic patterns in ASD diagnoses among inpatients aged 1-20 years, using data from the Kids' Inpatient Database (KID) from 1997 to 2019. ASD cases were identified using ICD-9-CM and ICD-10-CM codes. Of 9,267,881 hospital discharges studied, 110,090 (1.19%) had a diagnosis of ASD. The prevalence of ASD was higher among males compared to females (1.53% vs. 0.54%) and was highest among non-Hispanic Whites (1.28% vs. 0.95% in non-Hispanic Blacks, 0.94% in Hispanics, and 1.18% in Other races). ASD prevalence increased from 0.18% to 1997 to 3.36% in 2019 (Z= -273.40, p < 0.001). The absolute increase was higher among males compared to females (0.26-4.90% vs. 0.08-1.77%) and among non-Hispanic Whites (0.18-2.88%) compared to non-Hispanic Blacks (0.23-2.72%), Hispanics (0.14-2.60%), and Other races (0.19-2.97%). The epidemiologic patterns of ASD based on inpatient data are generally consistent with reports from the community-based autism surveillance system. Our findings indicate that KID and other health services data might play a complementary role in ASD surveillance., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Preschooling is a critical time for intervention in children with autism spectrum disorder (ASD); thus, we analyzed brain tissue component volumes (BTCVs) and clinical indicators in preschool children with ASD to identify new biomarkers for early screening., Methods: Eighty preschool children (3-6 years) with ASD were retrospectively included. The whole-brain myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF brain component volumes were obtained using synthetic magnetic resonance imaging (SyMRI). Clinical data, such as intelligence scores, autism diagnostic observation schedule-calibrated severity scores, age at first production of single words (AFSW), age at first production of phrases (AFP), and age at walking onset (AWO), were also collected. The correlation between the BTCV and clinical data was evaluated, and the effect of BTCVs on clinical data was assessed by a regression model., Results: WM and GM volumes were positively correlated with intelligence scores (both P < 0.001), but WM and GM did not affect intelligence scores (P = 0.116, P = 0.290). AWO was positively correlated with AFSW and AFP (both P < 0.001). The multivariate linear regression analysis revealed that MyC, AFSW, AFP, and AWO were significantly different (P = 0.005, P < 0.001, P < 0.001)., Conclusions: This study revealed positive correlations between WM and GM volumes and intelligence scores. Whole-brain MyC affected AFSW, AFP, and AWO in preschool children with ASD. Noninvasive quantification of BTCVs via SyMRI revealed a new visualizable and quantifiable biomarker (abnormal MyC) for early ASD screening in preschool children., (© 2024. Children's Hospital, Zhejiang University School of Medicine.)