1. Akomolafe AF, Abdallah BM, Mahmood FR, Elshoeibi AM, Al-Khulaifi AA, Mahmoud E, Dweidri Y, Darwish N, Yousif D, Khalid H, Al-Theyab M, Azeem MW, Shahwar D, Kamal M, Alabdulla M, Khaled SM, Chivese T. Estimates of the prevalence of autism spectrum disorder in the Middle East and North Africa region: A systematic review and Meta-Analysis. BMC Public Health;2025 (Jul 21);25(1):2519.

BACKGROUND: Prevalence estimates for autism spectrum disorder (ASD) in the Middle East and North Africa (MENA) region are not readily available, amid a lack of recent evidence. In this study, we estimated the prevalence of ASD in the MENA region by synthesising evidence from published studies. METHODS: We conducted a systematic review and meta-analysis, searching PubMed, EMBASE, Scopus, and CINAHL for studies assessing ASD prevalence in the MENA region. Risk of bias was assessed using the Newcastle Ottawa scale. A bias-adjusted inverse variance heterogeneity meta-analysis model was used to synthesize prevalence estimates from included studies. Cochran’s Q statistic and the I(2) statistic were used to assess heterogeneity, and publication bias assessed using funnel and Doi plots. RESULTS: Of 3,739 studies identified, 19 met the inclusion criteria, published during the period 2007-2025, from Iran, Oman, Libya, Egypt, Saudi Arabia, Lebanon, United Arab Emirates, Bahrain, and Qatar, Iraq. Country specific prevalence estimates ranged from 0.01% in Oman in 2009 to 6.50% in one study from Iraq in 2024. The overall prevalence of ASD in the MENA region was 0.14% (95%CI 0.02- 0.36%), with significant heterogeneity (I(2) = 99.8%). Overall ASD prevalence was 0.04% (95%CI 0.00-0.13, I(2) = 99.4%) for studies done before 2015 and 0.45% (95%CI 0.17-0.87, I(2) = 99.4%) for studies after 2015. Overall ASD prevalence was high in studies that used the Modified Checklist for Autism in Toddlers (M-CHAT) only [1.66% (95%CI 0.15-4.33, I(2) = 97.5%)] while the overall ASD prevalence was 0.14% (95%CI 0.00-0.46, I(2) = 99.9%) for studies that used the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for diagnosis. CONCLUSION: Estimates of the prevalence of ASD vary widely across the MENA region, with variability in ASD prevalence estimates by diagnostic methods and sampling approaches. While the data suggest a possible increase in prevalence during the study period, this observation warrants further investigation through more robust, longitudinal, and methodologically consistent studies. REGISTRATION: PROSPERO registration ID CRD42024499837.

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2. Aldhyani THH, Al-Nefaie AH. DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms. Front Physiol;2025;16:1593965.

INTRODUCTION: The early detection and diagnosis of autism spectrum disorder (ASD) remain critical challenges in developmental healthcare, with traditional diagnostic methods relying heavily on subjective clinical observations. METHODS: In this paper, we introduce an innovative multi-stream framework that seamlessly integrates three state-of-the-art convolutional neural networks, namely, EfficientNetV2B0, ResNet50V2, DenseNet121, and Multi-Stream models to analyze stereotypical movements, particularly hand-flapping behaviors automatically. Our architecture incorporates sophisticated spatial and temporal attention mechanisms enhanced by hierarchical feature fusion and adaptive temporal sampling techniques designed to extract characteristics of ASD related movements across multiple scales. The system includes a custom designed temporal attention module that effectively captures the rhythmic nature of hand-flapping behaviors. The spatial attention mechanisms method was used to enhance the proposed models by focusing on the movement characteristics of the patients in the video. The experimental validation was conducted using the Self-Stimulatory Behavior Dataset (SSBD), which includes 66 videos. RESULTS: The Multi-Stream framework demonstrated exceptional performance, with 96.55% overall accuracy, 100% specificity, and 94.12% sensitivity in terms of hand-flapping detection and an impressive F1 score of 97%. DISCUSSION: This research can provide healthcare professionals with a reliable, automated tool for early ASD screening that offers objective, quantifiable metrics that complement traditional diagnostic methods.

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3. Alsubai S. MDNCT: a multi-domain neurocognitive transformer architecture approach for early prediction of autism spectrum disorders. Sci Rep;2025 (Jul 21);15(1):26378.

Intellectual disability (ID) refers to a disorder involving intelligence and adaptive behavior that meets specific criteria involving deviance from the norm in terms of degree. ID is more common in males than females, and the causes can be genetic or environmental. This population has historically been characterized by significantly decreased life expectancy because they have not been diagnosed and treated for such diseases as cardiovascular and respiratory ones. However, medical progress in the last few years has slightly narrowed this gap, highlighting that understanding ID requires its consideration as a comorbidity to neurodevelopmental and cognitive diseases like Autism Spectrum Disorder (ASD), dementia, or learning disability. Thus, this work proposes the multi-domain NeuroCognitive Transformer (MDNCT) suitable for different prediction tasks on different datasets. Therefore, MDNCT obtains high performance based on the adequate preprocessing level according to the domain data’s specific characteristics, more advanced feature extraction methods, and the use of Transformer-based neural networks. The structure of the framework incorporates common means to align multiple features across modalities and also other state-of-the-art features like multi-head self-attention and residual connections for learning. The use of the MDNCT includes important domains, including early dementia diagnostics for health purposes, social media comments toward learning disabilities, and effective identification of ASD in toddlers.

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4. An W, Ding Z, Zhu Z, Zhang M, Xiao H, Guo P, Yang F, Zhou X. Verbal working memory in children and adolescents with autism spectrum disorders: A meta-analysis review. Acta Psychol (Amst);2025 (Jul 22);259:105253.

In recent years, research has increasingly concentrated on examining verbal working memory (VWM) with autism spectrum disorders (ASD). However, the findings on whether ASD experience impairments in VWM are inconsistent, which could be attributed to various factors, including age, IQ, task variations, and participant heterogeneity. Therefore, this study undertakes a meta-analysis to review the advancements in research on VWM deficits in ASD, exploring the potential influence of factors such as age, IQ, sample size, and test tasks on VWM performance in this population. According to the Boolean algorithm, this study combined keywords related to VWM in children and adolescents with ASD, and published them in Web of Science, PsyCINFO, PubMed, Cochrane Library, Embase, CNKI, and Wanfang Database to find all research findings in ASD related VWM published before 2024, and the final meta-analysis comprised 25 studies. The research conclusion indicate that children and adolescents with ASD have deficits in VWM. In addition, age, IQ, sample size, and testing tasks may not be important factors affecting the VWM in ASD. The conclusion of this study provides theoretical basis and clinical guidance for further exploring the influencing factors and intervention methods of VWM with ASD in the future.

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5. Atlam ES, Aljuhani KO, Gad I, Abdelrahim EM, Atwa AEM, Ahmed A. Automated identification of autism spectrum disorder from facial images using explainable deep learning models. Sci Rep;2025 (Jul 22);15(1):26682.

The early and accurate detection of autism spectrum disorder (ASD) is crucial for timely interventions that can significantly improve the quality of life for individuals on the spectrum. Despite the importance of early diagnosis, current ASD diagnostic methods face several challenges, including being time-consuming, subjective, and requiring specialized expertise, which limits their accessibility and scalability. Addressing these limitations, automated ASD detection through facial image analysis offers a non-invasive, efficient, and scalable alternative. However, existing machine learning and deep learning techniques frequently face challenges such as limited generalizability, inadequate interpretability, and insufficient performance on diverse datasets. This study introduces an effective deep learning framework for automated ASD detection that leverages pre-trained convolutional neural networks (CNNs), including VGG16, VGG19, InceptionV3, VGGFace, and MobileNet. The proposed framework integrates advanced preprocessing techniques, data augmentation, and Explainable AI (XAI) methods, such as Local Interpretable Model-agnostic Explanations (LIME), to enhance both accuracy and interpretability. The experimental results demonstrate the effectiveness of the proposed framework, with the VGG19 model achieving an accuracy of 98.2%, outperforming many state-of-the-art methods. This work represents a significant step forward in automated ASD diagnostics, offering a reliable, efficient, and interpretable solution that can aid clinicians in making timely and accurate diagnoses.

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6. Goad BS, Rodda J, Allen M, Bamborschke D, Overmars I, Kerr RJ, Bushlin I, Chopra S, Coorg R, Dabscheck G, Freeman JL, Mackay MT, Devinsky O, Guerrini R, Parrini E, Bölsterli B, Hughes I, Huh LL, Kamate M, Kunz AB, Melikishvili G, Miteff C, Myers KA, Olson HE, Poduri A, Pillai S, Riney CK, Sinclair A, Calvert S, Reynolds TQ, Martinez AR, Russo A, Sadleir LG, Sanchez-Albisua I, Sartori S, Shea S, Smith-Hicks CL, Spooner CG, Thomas RH, Ardern-Holmes SL, Webster RI, Valeriani M, Veggiotti P, Masnada S, Ware TL, Yoong M, Berecki G, De Dominicis A, Specchio N, Trivisano M, Møller RS, Wolff M, Fazeli W, Scheffer I, Howell KB. Development and Adaptive Function in Individuals With SCN2A-Related Disorders. Neurology;2025 (Aug 12);105(3):e213868.

BACKGROUND AND OBJECTIVES: Developmental impairment is common in individuals with SCN2A-related disorders, although descriptions are limited. We aimed to determine trajectories and outcomes of development and adaptive function. METHODS: This was a mixed retrospective cross-sectional study of individuals from an international SCN2A Natural History Study, who had neurologic/neurodevelopmental disorders due to an SCN2A variant. Individuals with SCN2A intragenic variants were grouped into early-onset (EO) and late-onset (LO) phenotypic groups; those with SCN2A-containing 2q24.3 copy number variants (CNVs) were considered separately. We collected medical and developmental history from parents/caregivers and medical records. Adaptive function and behavior were characterized using functional classification system levels and Vineland Adaptive Behavior Scales-3 (VABS-3) Parent/Caregiver Form. We repeated analyses on individuals with variants known to result in gain-of-function (GOF, typically EO phenotypes) or loss-of-function (LOF, typically LO phenotypes). RESULTS: A total of 100 individuals (age 0.1-21.9 years, 39% female) were studied. Phenotypic groups were EO (n = 44), LO (n = 48), and 2q24.3 CNV (n = 8). Developmental delay/intellectual disability was present in 91 of 100, and 23 of 80 individuals (29%) older than 2 years had autism spectrum disorder. Of people older than the typical age for skill attainment, 59 of 95 (62%) could sit and 48 of 88 (55%) could walk. In addition, 27 of 86 individuals (31%) spoke more than 1-5 single words, and 24 of 74 (32%) followed two-step commands. Median VABS-3 Adaptive Behavior Composite (ABC) scores were as follows: the EO phenotypic group had a score of 56 (range 21-110), the LO phenotypic group had a score of 45 (range 20-89), and 5 of 6 with a 2q24.3 CNV had an ABC score of <45. The EO phenotypic group had 3 distinct subgroups, consistent with "benign," "intermediate," and "severe" definitions previously published. The LO phenotypic group showed a continuum of severity, without distinct clusters. However, clinically relevant differences in motor function were evident when subgrouped by seizure-onset age; a lower proportion with earlier seizure onset (age <18 months) were independently ambulant than those with later onset or no seizures (5/15 [33%] vs 10/12 [83%] vs 14/15 [93%], p < 0.01). Analyses of individuals with confirmed GOF/LOF variants (n = 57) showed similar results to the EO/LO analyses. DISCUSSION: The spectrum of developmental impairments and adaptive function in SCN2A-related disorders is extremely broad. Phenotypic subgroups provide prognostic information and critically inform clinical trial design.

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7. Hu XD, Flores LY, Nowell KP. Psychological Well-Being in Autistic College Students: Testing and Extending the Social Cognitive Well-Being Model. J Autism Dev Disord;2025 (Jul 21)

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8. Iwasaki S, Yoshimura Y, Hasegawa C, Tanaka S, Ikeda T, Yaoi K, Hirosawa T, Kikuchi M. Sleep problems and sensory features in children with low-average cognitive abilities and autism spectrum disorder. Sci Rep;2025 (Jul 21);15(1):24509.

Children with Autism Spectrum Disorder (ASD) often have more sleep disturbances than typically developing children. These sleep disturbances have been suggested to be associated with atypical sensory features in children with ASD. Sleep habits have also been linked to intelligence and cognitive function in children. However, it remains unclear whether sleep disturbances in children with ASD are related to intelligence or sensory features. This study examined whether sleep disturbances in children can be explained by the presence or absence of ASD characteristics, sensory features, and cognitive skills. Sleep disturbances and atypical sensory features were determined using the Japanese Sleep Questionnaire for Preschoolers and the Caregiver Sensory Profile, as reported by their caregivers. Cognitive skills were assessed using the Japanese translation of the Kaufman Assessment Battery for Children. Consequently, children with below-average cognitive scores demonstrated that higher sensory scores were associated with poorer sleep quality; children with above-average cognitive scores showed no such patterns. These findings may aid in the development of support for sleep disturbances in various subtypes of ASD.

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9. Lee D, McNulty L, Kathiravan S, Mauriello ML, Vasudevan V, Bhat A, Obrusnikova I, Suminski R. A Gamified mHealth App to Promote Physical Activity and Reduce Sedentary Behavior in Autistic Adults: Protocol for a Remotely Delivered Pilot Intervention Study. JMIR Res Protoc;2025 (Jul 22);14:e71631.

BACKGROUND: Research indicates that many autistic adults are insufficiently active and overly sedentary. There is limited evidence on effective strategies to increase physical activity (PA) and reduce sedentary behavior (SB) in this population. Gamified mobile health (mHealth) interventions show promise for addressing these challenges by leveraging autistic individuals’ strengths in visuospatial learning and their affinity for digital gaming. Despite this potential, it remains unclear how well these interventions translate to real-world settings. This gap is compounded by the lack of community-based participatory approaches in the development of mHealth intervention for autistic adults. OBJECTIVE: This study aims to (1) formulate gamification and behavior change strategies for the PuzzleWalk v2 app using a community-based participatory approach and (2) evaluate its feasibility and acceptability for increasing PA and reducing SB among autistic adults, including those with mild intellectual disability, in real-world settings. METHODS: This study, consisting of 2 sequential phases, will be conducted entirely remotely: (1) online community-based design workshops to refine the PuzzleWalk gamified mHealth system with input from key autism stakeholders, including autistic adults and caregivers, and (2) an 8-week field deployment to assess real-world usability and engagement. In Phase I (completed), weekly workshops and usability testing focused on understanding autistic adults’ technology preferences, evaluating PuzzleWalk v1 and v2 prototypes, and incorporating stakeholder feedback into iterative app development (n=9). Phase II (in progress) will involve a single-arm clinical trial where approximately 70 participants will use the app alongside research-grade activity-tracking accelerometers to measure PA and SB. Outcome measures, including sedentary time, step counts, PA intensities, and app engagement (eg, time spent using the app), will be collected across 4 specific time points (ie, baseline and weeks 3, 5, and 8). Repeated measures ANCOVA will be performed to examine changes in participants’ objective levels of PA and sedentary time before, during, and after the intervention. RESULTS: Phase I of the study, involving community-based participatory design workshops and usability testing, was completed in November 2024. Key autism stakeholders recognized the gamified PuzzleWalk app as a viable tool for enhancing motivation toward PA and SB changes among autistic adults. Data collection for Phase II, the field deployment, is currently underway and is expected to end in August 2025. As of July 2025, we had enrolled 69 participants in the study. The findings of these studies will be shared in a subsequent peer-reviewed publication. CONCLUSIONS: Results of the ongoing field deployment study (Phase II) will further clarify the app’s effectiveness and real-world applicability. TRIAL REGISTRATION: ClinicalTrials.gov NCT06566131; https://clinicaltrials.gov/study/NCT06566131. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71631.

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10. Li C, Geng Z, Liu Y, Li X, Wang T, Ahmad M, Luo H, Zhou H, Cui Y. Two-hit immune activation induced autism-like phenotypes in mice: The underlying mechanism may involve the lung-brain axis. Brain Res;2025 (Jul 22);1865:149850.

Neuroinflammation plays important roles in the pathogenesis and development of autism spectrum disorder (ASD). However, the mechanism by which peripheral organ inflammation affects neuroinflammation is still unclear. This study aimed to investigate that the interaction between the lungs and the brain as a potential mechanism underlying this effect. Ovalbumin (OVA) can induce neuroinflammation and cause neurotoxicity, leading to tissue damage or cognitive memory impairment. OVA – induced maternal immune activation (MIA) provides a stable animal model for studying ASD and other human neurodevelopmental disorders. Postnatal reinfection is an additional risk factor for ASD and may lead to pathological and physiological changes. Here we compared the expression of cytokines in the hippocampus and lung tissues of MIA offspring after the second acute immune stimulation at three times post birth, as well as the correlation between cytokines and autism-like phenotypes.Interestingly, our research findings suggest that maternal and postpartum OVA-induced immune activation and lung injury may produce an autistic phenotype, with potential mechanisms involving the lung- brain axis.

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11. Lu HH, Nguyen NTK, Panwar R, Lin CI, Cross TL, Lin SH. Ameliorating Gastrointestinal Symptoms in Children With Autism Spectrum Disorder by Modulating the Gut Microbiota: A Systematic Review and Meta-Analysis. Autism Res;2025 (Jul 22)

Children with autism spectrum disorder (ASD) exhibit a high prevalence (55%) of gastrointestinal symptoms (GISs) and gut dysbiosis. Most studies involving children with ASD have focused on behavioral symptoms but not GISs. This systematic review and meta-analysis investigated the effects of gut microbiota-modulating interventions (GMMIs) on GISs and gut microbial composition in children with ASD. Five databases were searched for relevant domestic and international articles published from database inception until July 15, 2024. The meta-analysis included human trials wherein children with ASD received prebiotics, probiotics, synbiotics, or fecal microbiota transplantation. Intervention effects were measured on the basis of α-diversity, and genus- and phylum-level data were analyzed using a random-effects model and forest plots. This study included 19 trials (n = 1154). The results indicated that GMMIs significantly ameliorated GISs (p = 0.0017), reduced six-item Gastrointestinal Symptom Index scores by 1.86 points (p = 0.0187), and significantly increased the relative abundance of Bifidobacterium spp. (p = 0.0205). Longer interventions (≥ 8 weeks) were more effective in ameliorating GISs. Limitations in this investigation include the fact that the included studies neither incorporated any dietary control groups nor collected relevant dietary data, and the relatively small sample size (19 studies) may have hindered the identification of sources of heterogeneity in the pooled results. Overall, our findings suggest that GMMIs, especially probiotics, ameliorate GISs in children with ASD by modulating gut microbial composition, particularly by increasing the relative abundance of Bifidobacterium spp. These interventions may alleviate symptoms such as constipation, diarrhea, abnormal stool consistency and smell, flatulence, and abdominal pain. Our evidence supports that treatments involving GMMIs can be considered for children with ASD.

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12. Ma C, Lei M. Time series prediction for monitoring cardiovascular health in autistic patients. Front Psychiatry;2025;16:1623986.

INTRODUCTION: Monitoring cardiovascular health in autistic patients presents unique challenges due to atypical sensory profiles, altered autonomic regulation, and communication difficulties. As cardiovascular comorbidities rise in this population, there is an urgent need for tailored computational strategies to enable continuous monitoring and predictive care planning. Traditional time series methods-including statistical autoregressive models and recurrent neural networks-are constrained by opaque decision processes, limited personalization, and insufficient handling of multimodal data, restricting their utility where transparency and individualized modeling are critical. METHODS: We introduce a structurally-aware, semantically-grounded framework for time series prediction tailored to cardiovascular trajectories in autistic patients. Our approach departs from black-box modeling by integrating symbolic clinical abstractions, causal event dynamics, and intervention-response coupling within a graph-based paradigm. Central to our method is the CardioGraph Synaptic Encoder (CGSE), a generative model that fuses multimodal data-such as ECG waveforms, blood pressure signals, and structured clinical annotations-into a unified latent space. The CGSE employs dual-level temporal attention to capture patient-specific micro-patterns and population-level structures. To improve generalization and robustness, we propose the Dynamic Cardiovascular Trajectory Alignment (DCTA), which combines task-adaptive curriculum learning with multi-resolution consistency loss. RESULTS: Our approach effectively addresses challenges such as scarcity of labeled data and clinical heterogeneity common in autistic populations. Experimental results demonstrate that our system significantly outperforms baselines in predictive accuracy, temporal coherence, and interpretability. DISCUSSION: This work offers a novel, clinically-aligned pipeline for real-time cardiovascular risk monitoring in autistic individuals. By advancing personalized and interpretable healthcare analytics, our method has the potential to support more accurate and transparent decision-making in cardiovascular care pathways for this vulnerable population.

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13. Pagán AF, Loveland KA, Acierno R. The Adaptation and Translation of a Transition Readiness Scale for Young Adults with Autism Spectrum Disorder. J Autism Dev Disord;2025 (Jul 22)

Autistic young adults often face significant challenges during the transition to adulthood, yet few tools exist to assess their readiness in a developmentally appropriate and culturally responsive manner. This study presents the development and initial validation of the Transition Readiness Scale for Autistic Adolescents and Adults (TRS-A), a brief caregiver- and self-report measure designed to assess key domains of transition readiness. Guided by qualitative input from Latino families, community stakeholders, and clinicians, the TRS-A was piloted with 114 autistic young adults, 66 Spanish-speaking parents, and 48 English-speaking parents. Psychometric analyses supported the scale’s internal consistency and construct validity, mirroring the results from the initial development of the measure, with TRS-A scores correlating with adaptive functioning and mental health outcomes. The TRS-A offers a promising way to identify strengths and needs in autistic youth as they prepare for adult roles and responsibilities. Future research should explore its use across broader populations and its predictive value in transition-related interventions and clinical decision-making.

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14. Pais ML, Sereno J, Tomé VA, Fonseca C, Seco C, Ribeiro I, Martins J, Fortuna A, Abrunhosa A, Pinto L, Castelo-Branco M, Gonçalves J. Sex-specific cortical networks drive social behavior differences in an autism spectrum disorder model. Transl Psychiatry;2025 (Jul 21);15(1):251.

Social behavior is highly sensitive to brain network dysfunction caused by neuropsychiatric conditions like autism spectrum disorders (ASDs). Some studies suggest that autistic females show fewer social skill impairments than autistic males. However, the relationship between sex differences in social behavior and sexually dimorphic brain neurophysiology in ASD remains unclear. We hypothesize that sex-specific changes in cortical neurophysiology drive the sexual dimorphism observed in social behavior for ASD. To test this, we used male and female Tsc2(+/-) mice, a genetic ASD model, to examine cortical neuron morphology, the serotonergic system, E/I balance, structural connectivity, and social behavior. At the cellular level, transgenic males had shorter and less complex cortical basal dendrites, while transgenic females showed the opposite in apical dendrites. Notably, only Tsc2(+/-) females exhibited changes in the serotonergic system and E/I balance, with reduced cortical 5-HT(1A) receptor density and increased excitability. Additionally, activation of these serotonin receptors in transgenic animals correlated with E/I imbalance, highlighting inherent sexual dimorphisms in neuronal connectivity. In parallel, the TSC2 mouse model displayed sex-dependent changes in the structural connectivity of the cortex-amygdala-hippocampus circuit and social behavior: females showed a reduced number of axonal fiber pathways and reduced sociability, while males exhibited a loss of tissue density and deficits in social novelty. Moreover, in our ASD mouse model, better social performance correlated with the cortical serotonergic system. Our findings suggest that sex-dependent alterations in cortical neurophysiology, particularly in the serotonergic system, may contribute to the sexually dimorphic social behaviors observed in ASD.

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15. Qi K, Sun Z, Shi Y, Xiong X, Liu Y, Cai K, Chen A, Białas M. Effects of ball combination training program combined with cTBS intervention on motor disorder in children with autism spectrum disorder. Sci Rep;2025 (Jul 21);15(1):26418.

Purpose In children with Autism Spectrum Disorder (ASD), motor disorders (MD) are a common occurrence, and developing effective interventions continues to be challenging. This study aims to investigate the effects of a 12-week ball combination training program (BCTP) combined with continuous theta burst stimulation (cTBS) on MD in children with ASD. METHODS: The study employed a 4 (cTBS, BCTP, cTBS*BCTP, control group) × 2 (pre-test, post-test) mixed design methodology. 50 participants were allocated to three experimental groups(38) and one control group(12), receiving interventions for a period of 12 weeks. MD were measured using the Movement Assessment Battery for Children – Second Edition (MABC-2). RESULTS: Results indicated that the cTBS*BCTP group significantly improved the overall MABC-2 scores (P < 0.05) and Manual dexterity scores (P < 0.05) in children with ASD, and the BCTP group also significantly improved overall MABC-2 scores. Compared to the control group, the effect size for the cTBS*BCTP group was 1.03 (95% CI: 0.13 to 1.94), showing the best intervention effect, outperforming the BCTP group (effect size: 0.82, 95% CI: -0.09 to 1.72) and the cTBS group (effect size: 0.43, 95% CI: -0.38 to 1.23). CONCLUSION: Overall, the BCTP*cTBS group showed the most significant intervention effects across various dimensions, demonstrating the efficacy of combined interventions in improving MD in children with ASD, confirming that combined interventions are superior to single interventions.

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16. Rivard M, Mello C, Sanchez C, Grenier-Martin J, Lefebvre C, Forget J, Mestari Z, Morin D. Barriers and facilitators to implementing prevent-teach-reinforce for young children in community-based early intervention services for autism. Eval Program Plann;2025 (Jul 16);112:102657.

The present study examined barriers and facilitators to the implementation of evidence-based practices to manage challenging behavior under real-world conditions within community-based autism services. Parents, early intervention practitioners, and administrators at a public agency who participated in Prevent-Teach-Reinforce for Young Children (PTR-YC), an intervention based on positive behavior support principles, shared their perspectives on the factors that helped or hindered the implementation of the program. Barriers and facilitators were identified at the macrosystemic (community), services (organization and program), and case (parent, family, child, practitioner) levels. Of note, the governmental response and lockdown measures of the Covid-19 pandemic highlighted both challenges and opportunities for the planned large-scale deployment of the intervention. While the program itself includes built-in facilitating elements (e.g., peer support, flexibility, efficiency), results also underscored the importance of robust change management practices, administrative support, responsive clinical supervision, and organizational commitment to professional development. Parents and practitioners, the core members of the intervention team, bring to bear many positive personal qualities but benefit from support in adopting the new approach and its procedures.

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17. Stahmer AC, Brookman-Frazee L. Bridging the Leadership Gap in Autism Care: A Global Imperative for Effective Implementation-Authors Reply. J Am Acad Child Adolesc Psychiatry;2025 (Jul 22)

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18. Tschida J, Peeran I, Drahota A. Using community engagement to adapt anxiety cognitive behavioural therapy for autistic youth receiving services in Michigan community-based organisations: protocol for a mixed methods study. BMJ Open;2025 (Jul 22);15(7):e095564.

INTRODUCTION: Anxiety disorders are among the most common co-occurring mental health conditions experienced by autistic youth. Without appropriate intervention, anxiety disorders and related difficulties experienced by autistic youth can remain well into adulthood, causing reduced quality of life. Behavioral Interventions for Anxiety in Children with Autism (BIACA) is a manualised, modular evidence-based cognitive behavioural therapy with demonstrated efficacy in reducing or fully remitting anxiety symptoms and improving overall adaptive functioning for autistic youth. However, BIACA has been developed and tested mostly in academic research laboratories and has involved a limited number of community clinicians. Thus, certain characteristics (eg, length, complexity) may require adaptation to facilitate adoption and use in community settings. METHODS AND ANALYSIS: This mixed methods study will use and evaluate a community-engaged, intervention adaptation method (ie, Adapted version of the Method for Program Adaptation through Community Engagement (AM-PACE)) to develop an adapted version of BIACA for community use. In the current study, the AM-PACE method will involve: (1) a Community Advisory Board (CAB), (2) structured process to identify core components, (3) community feedback via surveys and semistructured interviews and (4) role play exercises with intended clients. Thereafter, community-based providers (N=200) will be asked to evaluate the feasibility, acceptability, appropriateness, usability and intent to use for the original BIACA intervention and adapted BIACA intervention. Repeated measures Analysis of Variance (ANOVA) will be conducted to determine whether programme type predicts provider ratings. Higher provider ratings for the adapted BIACA intervention may indicate adaptations identified through AM-PACE-enhanced potential for BIACA to be equitably implemented in community settings. ETHICS AND DISSEMINATION: Ethics approval was obtained from the Michigan State University Institutional Review Board. Research findings will be published in peer-reviewed journals, presented at international conferences and disseminated in alignment with CAB recommendations. REGISTRATION: This study has been registered on Open Science Framework: https://doi.org/10.17605/OSF.IO/Z54MD.

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19. Widiastuti AA, Atmoko A, Eva N, Ervina I, Leylasari HT, Rustam HK, Juraidin I. Bridging the Leadership Gap in Autism Care: A Global Imperative for Effective Implementation. J Am Acad Child Adolesc Psychiatry;2025 (Jul 22)

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20. Zhai Q, Wu Y, Wu Y, Ji Y, Li Y, Xu R, Zhong Y, Xiao B, Zhou L. Cognitive behavioural therapy and related interventions for sleep disorders in children and adults with autism spectrum disorder: protocol for a systematic review and meta-analysis. BMJ Open;2025 (Jul 22);15(7):e101084.

INTRODUCTION: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterised by deficits in social communication and repetitive behaviours, often accompanied by sleep disturbances. These sleep problems, including prolonged sleep latency and fragmented sleep, affect more than half of autistic individuals, exacerbating functional impairments and diminishing quality of life. Cognitive behavioural therapy (CBT) has shown promise in addressing sleep disturbances in ASD, with preliminary studies indicating improvements in sleep quality. However, no systematic review has comprehensively summarised the effects of CBT on sleep in autistic individuals. METHODS AND ANALYSIS: This systematic review and meta-analysis will synthesise evidence on the efficacy of CBT for improving sleep quality in individuals with ASD. We will search multiple databases (eg, PubMed, Web of Science) for studies published until May 2025. Inclusion criteria encompass randomised controlled trials, single-arm studies and observational studies involving children and adults with ASD and moderate sleep problems. Interventions targeting sleep quality using CBT techniques will be considered. Data extraction will focus on study details, participant information, intervention specifics and sleep outcome measures (eg, total sleep time, sleep onset latency, etc). Risk of bias will be assessed using tools such as Cochrane Risk of Bias Tool V.2, Risk Of Bias In Non-randomised Studies-of Interventions and Review Manager 5.3. A meta-analysis will be conducted using Stata 18, with heterogeneity evaluated using the I² statistic and Cochran’s Q test. ETHICS AND DISSEMINATION: Given that the dataset for this investigation is derived from publicly accessible databases, there is no direct interaction with patients; thus, ethical approval is not required. PROSPERO REGISTRATION NUMBER: CRD42025643701.

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