1. Atefrad S, Yousefnejad A, Faraji N, Keshavarz P. The association between NLGN4 gene variants and the incidence of autism spectrum disorders in Guilan, Iran. IBRO Neurosci Rep;2025 (Jun);18:306-310.

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by impaired social interaction, communication skills, and repetitive behaviours. This study aimed to investigate the association between variants of the Neuroligin-4 (NLGN4) gene (rs1882260 and rs3810688) and the incidence of ASD in North of Iran in the ASD group (n = 60) and control group (n = 60). DNA was isolated from whole blood, saliva, or hair samples. The targeted variants were genotyped using the Amplification Refractory Mutation System-Polymerase Chain Reaction (ARMS-PCR) technique. Genetic analyses were conducted using SNPAlyze ver. 8.1. Results revealed a significant difference of rs3810688 polymorphism in the NLGN4 gene in both genotypic and allelic frequency distributions between the ASD and control groups (P < 0.05). The GG genotype of rs3810688 polymorphism exhibited a significant association with an elevated risk of ASD in contrast to the CC genotype, as revealed under the co-dominant model (OR=4.2; 95 %CI, 1.25-14.05; P = 0.019). The study illustrated the possible role of rs3810688 polymorphism of NLGN4 in increasing the incidence of ASD among newborns in Guilan province. Also, the G-C haplotype was found to be a protective variant against ASD.

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2. Blackhurst T, Warmelink L, Roestorf A, Hartley C. Exploring lie frequency and emotional experiences of deceptive decision-making in autistic adults. Autism;2025 (Mar 3):13623613251315892.

Lying, a universal social behaviour, is frequent in everyday communication. Due to differences in social communication and experiences, autistic and non-autistic adults may react differently in situations where they must decide whether to lie or tell the truth. We investigated whether autistic and non-autistic adults differ in their general lying behaviour (e.g. how often they lie) and their likelihood of lying in a range of hypothetical social scenarios with different motivations (why people lie – to benefit or protect) and orientations (who people lie for; themselves, other, a group). We also examined participants’ emotional experiences of lying and truth-telling. We found that autistic and non-autistic adults’ general lying frequencies and emotional experiences were similar. However, the social scenario responses revealed that autistic adults would be less likely to lie to benefit or protect a social group they are part of. Moreover, autistic adults indicated that they would find lying more difficult across all social scenarios, experience more guilt, and would be less confident that their lie would be believed. This research highlights how autistic adults’ lying may be context-dependent and considers how a reduction in the likelihood of lying for their social group could increase strain on autistic adults’ social relationships.

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3. Bussu G, Portugal AM, Falck-Ytter T. Different sensory dimensions in infancy are associated with separable etiological influences and with autistic traits in toddlerhood. J Child Psychol Psychiatry;2025 (Mar 4)

BACKGROUND: Infants vary significantly in the way they process and respond to sensory stimuli, and altered sensory processing has been reported among infants later diagnosed with autism. Previous work with adolescents and adults suggests that variability in sensory processing may have a strong genetic basis. Yet, little is known about the etiological factors influencing sensory differences in infancy, when brain circuits supporting social and non-social cognition are sculpted and learning about the world via sensory input largely occurs in interaction with caregivers. METHODS: We analysed data from a community sample of monozygotic (MZ) and dizygotic (DZ) 5-month-old same-sex twins (n = 285 pairs, n = 158 MZ pairs, n = 150 male pairs) from the BabyTwins Study in Sweden (BATSS) using exploratory factor analysis, generalised estimating equations and multivariate twin models to delineate the phenotypic and etiological structure of individual variability across different sensory processing dimensions, as measured by the Infant/Toddler Sensory Profile. Developmental links to later autistic traits were also assessed, as measured by total scores from the Quantitative Checklist for Autism in Toddlers at 36 months. RESULTS: Results suggested separability between sensory processing dimensions (i.e. sensation seeking, sensation avoiding, sensory sensitivity and low registration) at a phenotypic and etiological level, with significant contributions from additive genetics and family environment that were unique to each sensory dimension and significant but smaller contributions from shared influences. Sensory domains also showed etiological separability, with unique genetic influences to each domain, while contributions from shared environment were in part shared across domains. A higher incidence of tactile-related behaviours and behaviours associated with sensory sensitivity, sensation avoiding, and low registration were significantly associated with higher levels of autistic traits in toddlerhood. CONCLUSIONS: This study provides a map of the phenotypic and etiological structure of sensory processing in infancy, which will be informative for studies of both typical and atypical development.

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4. Chen J, Du S, Zhu Y, Li D, Hu C, Mei L, Zhu Y, Chen H, Wang S, Xu X, Dong X, Zhou W, Xu Q. Facial characteristics description and classification based on 3D images of Fragile X syndrome in a retrospective cohort of young Chinese males. Comput Biol Med;2025 (Mar 2);189:109912.

PURPOSE: Fragile X syndrome (FXS) is a common cause of intellectual disability and autism. FXS presents with abnormal facial features, which in pediatric patients are subtler than what is seen in adults. The three-dimensional (3D) facial images, which contain more stereoscopic and subtle information than two-dimensional (2D) photographs, are increasingly being used to classify genetic syndromes. Here, we used 3D facial images to describe facial features and construct a classification model, especially in male patients with FXS. METHODS: We registered the 3D facial images of 40 Chinese boys with FXS and 40 healthy boys. We utilized seven machine learning models with different features extracted from dense point cloud and sparse landmarks. A linear regression model was performed between feature reduction of regional point cloud and genomic as well as methylation subtypes. RESULTS: The typical and subtle differences between 3D average faces of patients and controls could be quantitatively visualized. The projection of patients and controls in Fragile X-liked vectors are significantly different. The random forests model using coordinates of regional facial points (chin, eye, forehead, nose and upper lip) could perform better than expert clinicians in binary classification. Among the 63 hierarchical facial segmentation, significantly associations were found in 8 segments with genetic subtypes, and 2 segments with methylation subtypes. CONCLUSION: The 3D facial images could assist to distinguish male patients with FXS by machine learning, in which the selected regional features performed better than the global features and sparse landmarks. The genetic and methylation status might affect regional facial features differently.

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5. Cheng M, Chukoskie L. Impact of Visual Clutter in VR on Visuomotor Integration in Autistic Individuals. IEEE Trans Neural Syst Rehabil Eng;2025;33:829-840.

Autistic individuals often exhibit superior local visual sensitivity but may struggle with global visual processing, affecting their visuomotor integration (VMI). Goal-directed overhand throwing is common in both the physical environment (PE) and virtual reality (VR) games, demanding spatial and temporal accuracy to perceive position and motion, and precise VMI. Understanding VMI in autistic individuals and exploring supportive designs in VR are crucial for rehabilitation and improving accessibility. We assessed static visuospatial accuracy and VMI with autistic ( ) and non-autistic ( ) adults using spatial estimation and overhand throwing tasks with eye and hand tracking, comparing VR to PE. In VR, all participants exhibited reduced visual accuracy, increased visual scanning, and shortened quiet eye duration and eye following duration after the ball release, which led to decreased throwing performance. However, simplifying visual information in VR throwing improved these measures, and resulted in autistic individuals outperforming non-autistic peers.

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6. Donahue MM, Robson E, Colgin LL. Hippocampal place cell sequences are impaired in a rat model of Fragile X Syndrome. J Neurosci;2025 (Mar 3)

Fragile X Syndrome (FXS) is a neurodevelopmental disorder that can cause impairments in spatial cognition and memory. The hippocampus is thought to support spatial cognition through the activity of place cells, neurons with spatial receptive fields. Coordinated firing of place cell populations is organized by different oscillatory patterns in the hippocampus during specific behavioral states. Theta rhythms organize place cell populations during awake exploration. Sharp wave-ripples organize place cell population reactivation during waking rest. Here, we examined the coordination of CA1 place cell populations during active behavior and subsequent rest in a rat model of FXS (Fmr1 knockout rats). While the organization of individual place cells by the theta rhythm was normal, the coordinated activation of sequences of place cells during individual theta cycles was impaired in Fmr1 knockout rats. Further, the subsequent replay of place cell sequences was impaired during waking rest following active exploration. Together, these results expand our understanding of how genetic modifications that model those observed in FXS affect hippocampal physiology and suggest a potential mechanism underlying impaired spatial cognition in FXS.Significance Statement Fragile X Syndrome (FXS) is a neurodevelopmental disorder that can cause impaired memory and atypical spatial behaviors such as « elopement » (i.e., wandering off and becoming lost). Activity in the CA1 subregion of the hippocampus supports spatial memory and spatial cognition, making it an important candidate to study in the context of FXS; however, how neuronal population activity in CA1 is affected by FXS is poorly understood. In this study, we found that the coordination of populations of CA1 neurons during active behavior and waking rest was impaired in a rat model of FXS. These results reveal hippocampal physiological deficits that may contribute to cognitive impairments in FXS.

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7. Gao J, Song S. A Hierarchical Feature Extraction and Multimodal Deep Feature Integration-Based Model for Autism Spectrum Disorder Identification. IEEE J Biomed Health Inform;2025 (Feb 12);Pp

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, and precise prediction using imaging or other biological information is of great significance. However, predicting ASD in individuals presents the following challenges: first, there is extensive heterogeneity among subjects; second, existing models fail to fully utilize rs-fMRI and non-imaging information, resulting in less accurate classification results. Therefore, this paper proposes a novel framework, named HE-MF, which consists of a Hierarchical Feature Extraction Module and a Multimodal Deep Feature Integration Module. The Hierarchical Feature Extraction Module aims to achieve multi-level, fine-grained feature extraction and enhance the model’s discriminative ability by progressively extracting the most discriminative functional connectivity features at both the intra-group and overall subject levels. The Multimodal Deep Integration Module extracts common and distinctive features based on rs-fMRI and non-imaging information through two separate channels, and utilizes an attention mechanism for dynamic weight allocation, thereby achieving deep feature fusion and significantly improving the model’s predictive performance. Experimental results on the ABIDE public dataset show that the HE-MF model achieves an accuracy of 95.17% in the ASD identification task, significantly outperforming existing state-of-the-art methods, demonstrating its effectiveness and superiority. To verify the model’s generalization capability, we successfully applied it to relevant tasks in the ADNI dataset, further demonstrating the HE-MF model’s outstanding performance in feature learning and generalization capabilities.

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8. Glanville B, Oates J, Foley KR, Hurem A, Osmetti L, Allen K. Harmonizing Identities: A Scoping Review on Voice and Communication Supports and Challenges for Autistic Trans and Gender Diverse Individuals. J Autism Dev Disord;2025 (Mar 3)

Autistic trans and gender diverse (TGD) individuals face unique voice and communication challenges compounded by minority stressors, impacting wellbeing and access to care. Speech pathologists are crucial in providing gender-affirming and neurodiversity-affirming support; however, guidance for working with this intersectional population remains limited. This scoping review mapped current knowledge on voice and communication challenges, identifies available supports, and demonstrates the limitations of existing guidance for speech pathologists. This review draws on 40 sources, including 29 peer-reviewed articles, 8 clinical guidelines, 2 books, and 1 position statement, identified through comprehensive searches of databases such as CINAHL, ERIC, Medline, APA PsycINFO, and grey literature in May 2024. Findings indicated that 96.8% of identified challenges pertained to communication, while fewer (3.2%) focused on voice-specific issues. Similarly, supports primarily addressed communication (91.3%), with 8.7% targeting voice needs. Communication challenges included barriers faced by autistic TGD individuals and those interacting with them, including healthcare professionals, peers, and family. Supports were identified as strategies and resources to enhance service delivery and client wellbeing, such as using visual aids or offering multiple communication options. However, significant gaps remain in addressing the unique voice and communication needs of this population, particularly regarding voice dysphoria, camouflaging versus identity disclosure, and communication within healthcare settings. Autistic TGD individuals represent a unique population whose needs are not sufficiently addressed by current guidance. This review highlights significant gaps in research and clinical practice and calls for improved clinical guidelines and specialized training for speech pathologists to enhance care.

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9. Hassan I, Nahid N, Islam M, Hossain S, Schuller B, Ahad MAR. Automated Autism Assessment with Multimodal Data and Ensemble Learning: A Scalable and Consistent Robot-Enhanced Therapy Framework. IEEE Trans Neural Syst Rehabil Eng;2025 (Feb 27);Pp

Navigating the complexities of Autism Spectrum Disorder (ASD) diagnosis and intervention requires a nuanced approach that addresses both the inherent variability in therapeutic practices and the imperative for scalable solutions. This paper presents a transformative Robot-Enhanced Therapy (RET) framework, leveraging an intricate amalgamation of an Adaptive Boosted 3D biomarker approach and Saliency Maps generated through Kernel Density Estimation. By seamlessly integrating these methodologies through majority voting, the framework pioneers a new frontier in automating the assessment of ASD levels and Autism Diagnostic Observation Schedule (ADOS) scores, offering unprecedented precision and efficiency. Drawing upon the rich tapestry of the DREAM Dataset, encompassing data from 61 children, this study meticulously crafts novel features derived from diverse modalities including body skeleton, head movement, and eye gaze data. Our 3D bio-marker approach achieves a remarkable predictive prowess, boasting a staggering 95.59% accuracy and an F1 score of 92.75% for ASD level prediction, alongside an RMSE of 1.78 and an R-squared value of 0.74 for ADOS score prediction. Furthermore, the introduction of a pioneering saliency map generation method, harnessing gaze data, further enhances predictive models, elevating ASD level prediction accuracy to an impressive 97.36%, with a corresponding F1 score of 95.56%. Beyond technical achievements, this study underscores RET’s transformative potential in reshaping ASD intervention paradigms, offering a promising alternative to Standard Human Therapy (SHT) by mitigating therapist variability and providing scalable therapeutic approaches. While acknowledging limitations in the research, such as sample constraints and model generalizability, our findings underscore RET’s capacity to revolutionize ASD management.

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10. Khan A, Ghasemi AR, Ingram KK, Ay A. Machine learning uncovers novel sex-specific dementia biomarkers linked to autism and eye diseases. J Alzheimers Dis Rep;2025 (Jan-Dec);9:25424823251317177.

BACKGROUND: Recently, microRNAs (miRNAs) have attracted significant interest as predictive biomarkers for various types of dementia, including Alzheimer’s disease (AD), vascular dementia (VaD), dementia with Lewy bodies (DLB), normal pressure hydrocephalus (NPH), and mild cognitive impairment (MCI). Machine learning (ML) methods enable the integration of miRNAs into highly accurate predictive models of dementia. OBJECTIVE: To investigate the differential expression of miRNAs across dementia subtypes compared to normal controls (NC) and analyze their enriched biological and disease pathways. Additionally, to evaluate the use of these miRNAs in binary and multiclass ML models for dementia prediction in both overall and sex-specific datasets. METHODS: Using data comprising 1685 Japanese individuals (GSE120584 and GSE167559), we performed differential expression analysis to identify miRNAs associated with five dementia groups in both overall and sex-specific datasets. Pathway enrichment analyses were conducted to further analyze these miRNAs. ML classifiers were used to create predictive models of dementia. RESULTS: We identified novel differentially expressed miRNA biomarkers distinguishing NC from five dementia subtypes. Incorporating these miRNAs into ML classifiers resulted in up to a 27% improvement in dementia risk prediction. Pathway analysis highlighted neuronal and eye disease pathways associated with dementia risk. Sex-specific analyses revealed unique biomarkers for males and females, with miR-128-1-5 as a protective factor for males in AD, VaD, and DLB, and miR-4488 as a risk factor for female AD, highlighting distinct pathways and potential therapeutic targets for each sex. CONCLUSIONS: Our findings support existing dementia etiology research and introduce new potential and sex-specific miRNA biomarkers.

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11. Kusumoto Y, Takahashi E, Takaki K, Matsuda T, Nitta O. Main complaints identified by parents of children with developmental delays during the initial consultation: a 10-year all-case study. PeerJ;2025;13:e19044.

BACKGROUND: In Japan, the child development support initiative is one of the government’s daycare support programs for children with disabilities. Children, aged 0-6 years, who are not attending elementary school can participate in the initiative and receive various support. Reports on the approaches taken by private child development support centers and the guardians’ perceptions are increasing. Conversely, information from public child development support centers, which serve as places for initial developmental consultation, is extremely scarce. Moreover, there are no nationwide reports on the main complaints from each region, which are of concern to the parents. This study aimed to clarify children’s gender and age, presence of referral sources, and characteristics of the main complaints obtained during the initial consultation with parents of children with developmental delays, who used a public developmental support center in a medium-sized city in Tokyo. METHODS: This study included 1,241 parents of children with developmental delays (average 40.3 months, range 2-87 months). Five questions regarding each child’s characteristics (gender, age in months, and medical diagnosis), referral sources for the use of support centers, and main complaints that they would like to discuss at the initial consultation, were asked. The participants were asked to describe their main complaints (specific consultation details) as precisely as possible. From the free-form descriptions of the main complaints, 137 codes were extracted and grouped into 13 categories. Participants were divided into two groups according to the presence (n = 122) or absence (n = 1,119) of a medical diagnosis. The t-test, chi-square test, and Fisher’s exact probability test were used to examine differences between the two groups. Logistic regression analysis with forced entry was performed to examine whether the factors related to the main complaints raised by parents of children with developmental delays differed depending on the presence or absence of a medical diagnosis. RESULTS: The most common chief complaint at the time of the initial consultation was « language development » (43.9%), followed by « childcare and preschool counseling » (15.4%), « hyperactivity/inattention » (13.9%), and « general developmental issues » (13.6%). The regression analysis revealed that gender, age (months), and general developmental issues were factors associated with the presence or absence of a medical diagnosis were gender, age in months, and general developmental issues. The odds ratios (95% confidence intervals) were 1.573 (1.056-2.343) for gender, 0.988 (0.976-1.000) for age (months), and 0.421 (0.200-0.886) for general developmental issues. CONCLUSION: Professionals involved in child development support are expected to have broad knowledge of various developmental issues as well as comprehensive knowledge of local childcare and schooling systems.

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12. Lai YYL, Zafar S, Leonard HM, Walsh LJ, Downs JA. Access to Oral Healthcare in Individuals With Rett Syndrome: A Qualitative Study of Parent Perspectives. J Intellect Disabil Res;2025 (Mar 3)

BACKGROUND: Intellectual and developmental disabilities (IDD) are varied in their nature and presentation. Barriers to oral healthcare are reported in studies of general populations with IDD but these may not reflect the barriers experienced by individuals with rare disorders such as Rett syndrome (RTT). There are also few peer-reviewed studies in the Australian context exploring barriers to dental care access for patients living with a disability. This qualitative study explored caregivers’ perceptions and experiences regarding oral health and access to dental care for those with RTT in Australia. METHODS: Parents of 31 individuals with a confirmed MECP2 mutation were sampled purposively from the Australian Rett Syndrome Database. Interview questions were based on earlier studies used in other disability populations and queried identification and management of dental pain and influence of other comorbidities in their child’s oral care. Interviews were audio-recorded, transcribed and analysed using NVivo (Version 12 Plus). Directed content analysis was used to code data to a framework constructed from a literature review of factors affecting access to professional oral healthcare systems and factors affecting access to optimal at-home oral care in disability. RESULTS: The most frequently cited barriers to professional dental care were dentist-related, while caregiver related financial barriers were cited by a minority of families. Dentist-related financial barriers were not present in these data. Most factors affecting access to optimal at-home oral care coded to the existing framework, with further enablers identified under training for the caregiver or parent. CONCLUSIONS: The findings of this study provide a point of reference to understand factors affecting provision of at-home dental care and professional services to enable optimal oral health in RTT. Future research could explore the provision of targeted oral health information on RTT to carers and clinicians.

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13. Ong JH, Zhang L, Liu F. Do autistic individuals show atypical performance in probabilistic learning? A comparison of cue-number, predictive strength, and prediction error. Mol Autism;2025 (Mar 4);16(1):15.

BACKGROUND: According to recent models of autism, autistic individuals may find learning probabilistic cue-outcome associations more challenging than deterministic learning, though empirical evidence for this is mixed. Here we examined the mechanism of probabilistic learning more closely by comparing autistic and non-autistic adults on inferring a target cue from multiple cues or integrating multiple target cues and learning from associations with various predictive strengths. METHODS: 52 autistic and 52 non-autistic participants completed three tasks: (i) single-cue probabilistic learning, in which they had to infer a single target cue from multiple cues to learn cue-outcome associations; (ii) multi-cue probabilistic learning, in which they had to learn associations of various predictive strengths via integration of multiple cues; and (iii) reinforcement learning, which required learning the contingencies of two stimuli with a probabilistic reinforcement schedule. Accuracy on the two probabilistic learning tasks was modelled separately using a binomial mixed effects model whereas computational modelling was performed on the reinforcement learning data to obtain a model parameter on prediction error integration (i.e., learning rate). RESULTS: No group differences were found in the single-cue probabilistic learning task. Group differences were evident for the multi-cue probabilistic learning task for associations that are weakly predictive (between 40 and 60%) but not when they are strongly predictive (10-20% or 80-90%). Computational modelling on the reinforcement learning task revealed that, as a group, autistic individuals had a higher learning rate than non-autistic individuals. LIMITATIONS: Due to the online nature of the study, we could not confirm the diagnosis of our autistic sample. The autistic participants were likely to have typical intelligence, and so our findings may not be generalisable to the entire autistic population. The learning tasks are constrained by a relatively small number of trials, and so it is unclear whether group differences will still be seen when given more trials. CONCLUSIONS: Autistic adults showed similar performance as non-autistic adults in learning associations by inferring a single cue or integrating multiple cues when the predictive strength was strong. However, non-autistic adults outperformed autistic adults when the predictive strength was weak, but only in the later phase. Autistic individuals were also more likely to incorporate prediction errors during decision making, which may explain their atypical performance on the weakly predictive associations. Our findings have implications for understanding differences in social cognition, which is often noisy and weakly predictive, among autistic individuals.

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14. Pan W, Ling G, Liu F. mGNN-bw: Multi-Scale Graph Neural Network Based on Biased Random Walk Path Aggregation for ASD Diagnosis. IEEE Trans Neural Syst Rehabil Eng;2025;33:900-910.

In recent years, computationally assisted diagnosis for classifying autism spectrum disorder (ASD) and typically developing (TD) individuals based on neuroimaging data, such as functional magnetic resonance imaging (fMRI), has garnered significant attention. Studies have shown that long-range functional connectivity patterns in ASD patients exhibit significant abnormalities, and individual brain networks display considerable heterogeneity. However, current graph neural networks (GNNs) used in ASD research have failed to adequately capture long-range connectivity and have overlooked individual differences. To address these limitations, this study proposes a novel multi-scale graph neural network based on biased random walks (mGNN-bw). The model introduces a co-optimization strategy between sub-models and the main model, leveraging node pooling scores from sub-models to guide biased random walks, effectively capturing long-range connectivity. By constructing high-order brain networks through path encoding and aggregation, and integrating them with low-order brain networks based on Pearson correlation, the model achieves a robust multi-scale feature representation. Experimental results on the publicly available ABIDE I dataset demonstrate the superior performance of our approach, achieving accuracy rates of 74.8% and 73.2% using CC200 and AAL atlases, respectively, outperforming existing methods. Additionally, the model identifies key ASD-associated brain regions, including the frontal lobe, insula, cingulate, and calcarine, supported by existing research. The proposed method significantly contributes to the clinical diagnosis of ASD.

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15. Pusil S, Laguna A, Chino B, Zegarra JA, Orlandi S. Early Identification of Autism Using Cry Analysis: A Systematic Review and Meta-analysis of Retrospective and Prospective Studies. J Autism Dev Disord;2025 (Mar 3)

Cry analysis is emerging as a promising tool for early autism identification. Acoustic features such as fundamental frequency (F0), cry duration, and phonation have shown potential as early vocal biomarkers. This systematic review and meta-analysis aimed to evaluate the diagnostic value of cry characteristics and the role of Machine Learning (ML) in improving autism screening. A comprehensive search of relevant databases was conducted to identify studies examining acoustic cry features in infants with an elevated likelihood of autism. Inclusion criteria focused on retrospective and prospective studies with clear cry feature extraction methods. A meta-analysis was performed to synthesize findings, particularly focusing on differences in F0, and assessing the role of ML-based cry analysis. The review identified eleven studies with consistent acoustic markers, including F0, phonation, duration, amplitude, and voice quality, as reliable indicators of neurodevelopmental differences associated with autism. ML approaches significantly improved screening precision by capturing non-linear patterns in cry data. The meta-analysis of six studies revealed a trend toward higher F0 in autistic infants, although the pooled effect size was not statistically significant. Methodological heterogeneity and small sample sizes were notable limitations across studies. Cry analysis holds promise as a non-invasive, accessible tool for early autism screening, with ML integration enhancing its diagnostic potential. However, the findings emphasize the need for large-scale, longitudinal studies with standardized methodologies to validate its utility and ensure its applicability across diverse populations. Addressing these gaps could establish cry analysis as a cornerstone of early autism identification.

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16. Ricchiuti G, Taillieu A, Tuerlinckx E, Prinsen J, Debbaut E, Steyaert J, Boets B, Alaerts K. Oxytocin’s social and stress-regulatory effects in children with autism and intellectual disability: a protocol for a randomized placebo-controlled trial. BMC Psychiatry;2025 (Mar 3);25(1):192.

BACKGROUND: Oxytocin is increasingly considered as a new pharmacological option for mitigating social difficulties and regulating stress in autism spectrum disorder. However, in prior trials, autistic individuals with co-occurring intellectual disability (ID) have largely been overlooked, despite their high prevalence, poorer outcome, and the enhanced need but reduced availability of therapeutic interventions. Prior studies have also overlooked the importance of standardizing the context in which oxytocin is administered, rendering outcomes from prior trials inconclusive. METHODS: To meet these limitations, we propose a double-blind, randomized, placebo-controlled trial investigating the effects of intermittent multiple-dose intranasal oxytocin administration (4 weeks, 24 IU 3x/week), administered within a standardized psychosocial stimulating context at special need schools, in 80 children with autism and co-occurring ID (4-13 years old). Clinical-behavioral as well as stress-regulatory effects of oxytocin will be evaluated using the Autism Treatment Evaluation Checklist (ATEC), the Brief Observation of Social Communication Change (BOSCC) expert rating scale, and measurements of high-frequency heart rate variability (HF-HRV), a validated index of parasympathetic autonomic nervous system activity. To assess the possibility of retention and/or late-emerging effects, outcomes will be assessed immediately after the administration regime, and at two follow-up sessions, four-weeks and six months after administration. DISCUSSION: Significant clinical-behavioral improvements on the ATEC and BOSCC, and significantly higher parasympathetic HF-HRV power in the oxytocin compared to the placebo group would confirm beneficial clinical-behavioral and stress-regulatory effects of oxytocin in autistic children with co-occurring ID. This would also corroborate the use of intermittent dosing schemes in combination with concomitant psychosocial stimulation. TRIAL REGISTRATION: The trial was registered on the 9th of December 2022 at the European Clinical Trial Registry (EudraCT 2022-002423-36) and on 20th of September the trial was transferred to the EU Clinical Trial Register (EU CT 2024-513436-14).

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17. Saigh BH. Breastfeeding duration and neurodevelopment: insights into autism spectrum disorders and weaning practices. J Health Popul Nutr;2025 (Mar 4);44(1):62.

This paper examines the complex relationship between breastfeeding duration and the incidence of autism spectrum disorders (ASDs), focusing on identifying the most beneficial weaning period and its subsequent effects on child development. Breastfeeding is widely recognized for its role in promoting early health, strengthening the immune system, and supporting neurodevelopment. However, the debate over its optimal duration persists. Integrating insights from current scientific studies with interpretations of Qur’anic teachings, this study advocates for a breastfeeding duration of 21 months. This duration balances the benefits of extended breastfeeding with potential risks associated with prolonged exposure, reflecting both ancient wisdom and contemporary evidence. Key findings suggest that breastfeeding may play a preventive role in mitigating ASD symptoms and enhancing neurodevelopment through mechanisms such as immune regulation, microbiome diversity, and hormonal pathways. These insights underline the need for further specialized research to explore the long-term impacts of breastfeeding on ASD-related outcomes.

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18. Son HJ, Park SA. The impact of an agro-healing program on family resilience, parental stress, and social skills of children with developmental disabilities. Heliyon;2025 (Feb 28);11(4):e42389.

Developmental disabilities pose challenges for the entire family, making it crucial to enhance family resilience and promote a positive outlook for all members. This study aimed to investigate the impact of an agro-healing program based on horticultural activities using a family resilience framework among families with children with developmental disabilities in South Korea. A total of 15 participants, including children with developmental disabilities, from 6 families were recruited for the research. The program entailed conducting a 90-min agro-healing program based on horticultural activities for 8 weeks; a total of 8 sessions were delivered weekly. The Family Resilience Scale, Family Adaptability Scale, Korean Perceived Stress Scale, Social Skills Rating Scale, and Satisfaction Survey Questionnaire were utilized to evaluate the program’s impact. The analysis of the agro-healing program’s effects revealed that family resilience in families with children with developmental disabilities improved significantly. Additionally, the parents’ stress levels decreased, and social skills of the children with developmental disabilities improved. The agro-healing program based on horticultural activities can serve as a social support measure to enhance psychological and social well-being and aid recovery in families with children with developmental disabilities.

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19. Tian P, Zhu X, Liu Z, Bian B, Jia F, Dou L, Jie Y, Lv X, Zhao T, Li D. Effects of vitamin D on brain function in preschool children with autism spectrum disorder: a resting-state functional MRI study. BMC Psychiatry;2025 (Mar 3);25(1):198.

BACKGROUND: Previous studies indicate vitamin D impacts autism spectrum disorder (ASD), but its relationship with brain function is unclear. This study investigated the association between serum 25-hydroxyvitamin D [25(OH)D] levels and brain function in preschool children with ASD using resting-state functional magnetic resonance imaging (rs-fMRI), and explored correlations with clinical symptoms. METHODS: A total of 226 ASD patients underwent rs-fMRI scanning and serum 25(OH)D testing. Clinical symptoms were assessed using Childhood Autism Rating Scale (CARS) and Autism Behavior Checklist (ABC). Patients were categorized into mild and severe groups based on the CARS, and further divided into normal (NVD), insufficient (VDI), and deficient (VDD) serum 25(OH)D levels. Changes in brain function among these groups were analyzed using regional homogeneity (ReHo), with ABC scores used for correlation analysis. RESULTS: In mild ASD, ReHo increased in the right postcentral gyrus and left precuneus in the VDI and VDD groups compared to NVD, and decreased in the bilateral middle cingulate gyrus and left superior frontal gyrus in the VDD group compared to VDI. In severe ASD, ReHo decreased in the right middle occipital gyrus and increased in the right insula in the VDI group compared to NVD, and increased in the right superior frontal gyrus in the VDD group compared to VDI. Correlation analysis revealed that in mild ASD, ReHo in the right postcentral gyrus was positively correlated with body and object use scores in the NVD and VDI groups, while ReHo in the right middle cingulate gyrus was negatively correlated with relating scores in the VDD and VDI groups. In severe ASD, ReHo in the right insula was positively correlated with language scores in the NVD and VDI groups. CONCLUSIONS: ASD patients with lower serum 25(OH)D levels show multiple brain functional abnormalities, with specific brain region alterations linked to symptom severity. These findings enhance our understanding of vitamin D’s impact on ASD and suggest that future research may explore its therapeutic potential.

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20. Traipidok P, Srisombundit P, Tassanakijpanich N, Charleowsak P, Thongseiratch T. Evaluating ChatGPT-4omni in paediatric developmental screening: direct versus sequential prompts. BMJ Paediatr Open;2025 (Mar 3);9(1)

Integrating Large Language Models like ChatGPT-4omni (ChatGPT-4o) into paediatric healthcare could revolutionise developmental screening. This study evaluated ChatGPT-4o’s efficacy in paediatric developmental screening using Direct and Sequential Prompting methods compared with the Bayley Scales of Infant Development, Third Edition. Among 106 paediatric cases, Direct Prompting showed a sensitivity of 73.42% and overall accuracy of 69.81%, while Sequential Prompting had a specificity of 62.96% and overall accuracy of 67.92%. Both methods demonstrate potential for improving the efficiency and accessibility of paediatric developmental screening, with Direct Prompts being more sensitive and Sequential Prompts more specific.

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21. Wang X, Shen LL, Pan SL, Jing J, Shi L, Weng XC, Li XH, Lin LZ, Pan N. A latent profile analysis of empathizing-systemizing cognitive style among Chinese children aged 6 – 12 years: Links to intelligence, executive function, and autistic traits. Int J Clin Health Psychol;2025 (Jan-Mar);25(1):100554.

BACKGROUND: Empathizing and systemizing abilities are respectively associated with key developmental outcomes like intelligence, executive function, and autistic traits, particularly in typically developing (TD) children. However, how specific cognitive styles-defined by the balance between empathizing and systemizing-relate to these outcomes remains unclear. METHODS: We conducted a latent profile analysis on 502 TD children aged 6‒12 years to identify cognitive styles based on multiple dimensions of empathizing and systemizing, measured by the Children’s Empathy Quotient and Systemizing Quotient. Intelligence, executive function, and autistic traits were assessed using the Wechsler Intelligence Scale for Children (Fourth Edition), the Behavior Rating Inventory of Executive Function, and the Social Responsiveness Scale, respectively. RESULTS: Four cognitive styles emerged: High B (high empathizing and systemizing), E-dominance (empathizing-dominant), S-dominance (systemizing-dominant), and Low B (low empathizing and systemizing). The High B and E-dominance groups showed higher full-scale intelligence and verbal comprehension scores compared to the Low B group. In executive function, the Low B and S-dominance groups displayed more impairments, particularly in inhibitory control, emotional regulation, and overall executive function. For autistic traits, the S-dominance group showed higher levels of both social-communication difficulties and autistic mannerisms, while the Low B group primarily displayed increased social-communication challenges. CONCLUSION: Cognitive styles marked by high empathizing and systemizing ability correlate with stronger intelligence and social-communication skills, while a systemizing-dominant profile may lead to executive function difficulties and elevated autistic traits. These findings emphasize the role of cognitive styles in developmental outcomes, with implications for tailored educational and clinical interventions.

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22. Wang Z, Qin H, Liu J, Zhou B, Wang X, Li H, Xu Q, Xu X, Liu H. Early Screening of Autism in Toddlers via Express-Needs-with-Pointing Protocol. IEEE J Biomed Health Inform;2025 (Jan 13);Pp

The incidence of autism spectrum disorders (ASD), a neurodevelopmental condition associated with challenges in social communication, has witnessed a remarkable surge in recent years, with adverse effects on individuals, families, and society at large. Early screening for autism ensures timely access to interventions, yet screening lacks systematic and methodical approaches for objectively quantifying social behaviors. In response to this, we propose a protocol for early assistive screening, termed the Express-Needs-with-Pointing (ENP), which employs a multi-sensor platform to quantify the one of the social skills of toddler. A vision-based pointing behavior detection method is proposed, combining gaze estimation and pointing estimation, where the pointing estimation integrates forearm orientation and finger direction. We conduct an experiment involving twenty toddlers aged between 16 and 32 months, 4 of whom are typically developing (TD) children, 6 diagnosed with ASD, 8 diagnosed with global developmental delay (GDD), and 5 diagnosed with language disorders (LD). The results demonstrate that the automated assessment methods for pointing behavior achieved an impressive accuracy rate of 93.9%. These findings provide compelling evidence that the ENP is one of the highly effective protocols and holds significant implications for assisting in early autism screening.

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23. Zhang F, Colizzi M. Editorial: Case reports in autism: 2023. Front Psychiatry;2025;16:1563977.

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24. Zhang H, Hu C, Wang Z, Zhou B, Wang X, Nie W, Ye Q, Lin R, Xu X, Liu H. Exploring Eye-tracking based Biomarkers to Assess Cognitive Abilities in Autistic Children: A Feasibility Study. IEEE J Biomed Health Inform;2025 (Jan 20);Pp

Cognitive assessment can reveal a person’s cognitive processing and behavioral patterns, making it an indispensable component of autism intervention and prognosis. Existing machine-assisted cognitive assessment methods primarily focus on children’s performance outcomes, overlooking distinctive behavioral models, particularly characteristics of eye movement behavior, which have been demonstrated as the most direct indicators of cognitive abilities. In this study, we explore eye-tracking biomarkers for assisting cognitive assessment through a series of meticulously designed multi-level human-computer interaction protocols, encompassing three cognitive abilities: pairing and categorization, emotion recognition, and social interaction. A platform embedded with an eye-tracking module has been developed to reliably collect and analyze eye movement data, even in the presence of unrestricted large head movements in children. Experimental results indicate that there are significant group differences between autism and typically developing children in the eye-tracking features of total fixation duration, response latency, time to first fixation, mean fixation duration, and visit count in the absence of significant intergroup differences in the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) and Wechsler Intelligence Scale for Children (WISC) assessment results. In addition, certain eye movement features in each group are correlated with WPPSI/WISC scale scores, enabling clinical cognitive assessments within each group based on these eye movement features. This study suggests that using eye-tracking features as biomarkers to assist detailed cognitive assessments holds significant potential for the intervention and prognosis of autism.

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25. Zhang Y, Huang Y, Qi J, Zhang S, Tian M, Tian Y, Meng F, Guan L, Chang T. Uncertainty Inspired Early Autism Spectrum Disorder Screening via Contrastive Image-viewing Paradigm. IEEE Trans Neural Syst Rehabil Eng;2024 (Dec 16);Pp

Eye-tracking technology is found effective in revealing the specific visual preference of Autism Spectrum Disorder (ASD) which can be characterized by high systemizing and low empathizing abilities. Early diagnosis is vital for ASD’s subsequent treatment. However, existing eye-tracking-based methods suffer from long diagnostic times and low diagnostic accuracy due to the lack of awareness of gaze preference derived from individual differences. Moreover, there is only one publicly available eye-tracking dataset that employs a simple image free-viewing paradigm to collect the gaze patterns of ASD and typically developed (TD) subjects with an average age of 8 years, thus can not effectively support the early diagnosis for preschool children. To tackle the difficulties, in this paper, we first propose an Uncertainty-inspired ASD Screening Network (UASN) that dynamically estimates the contribution of each stimulus viewed by different subjects, and secondly, we design a contrastive image-viewing paradigm and further collect eye movement data from preschool children to reveal the visual behaviors of ASD children accordingly. Specifically, in UASN, we estimate the uncertainty of each stimulus and use it for more efficient model training and a more simplified personalized diagnosis procedure. Besides, by synthesizing two images with the opposite semantic representations and recruiting ASD and TD subjects aged 2-6, we construct a new CI4ASD dataset, which offers a novel contrastive image-viewing paradigm for better diagnosis of ASD in children. Comprehensive experiments are conducted and results have evidenced the effectiveness of the proposed UASN and eye-tracking paradigm.

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26. Zhao Z, Chung E, Chung KM, Park CH. AV-FOS: A Transformer-Based Audio-Visual Multi-modal Interaction Style Recognition for Children with Autism Based on the Family Observation Schedule (FOS-II). IEEE J Biomed Health Inform;2025 (Feb 13);Pp

Challenging behaviors in children with autism is a serious clinical condition, oftentimes leading to aggression or self-injurious actions The Revised Family Observation Schedule 3rd Edition (FOS-R-III) is an intensive and fine-grained scale used to observe and analyze the behaviors of individuals with autism, which facilitates the diagnosis and monitoring of autism severity. Previous AI-based approaches for automated behavior analysis in autism often focused on predicting facial expressions and body movements without generating a clinically meaningful scale, mostly utilizing visual information. In this study, we propose a deep-learning based algorithm with audio-visual multimodal-data clinically coded with the FOS-R-III, named AV-FOS model. Our proposed AV-FOS model leverages transformer-based structure and self-supervised learning to intelligently recognize Interaction Styles (IS) in the FOS-R-III scale from subjects’ video recordings. This enables the automatic generation of the FOS-R-III measures with clinically acceptable accuracy. We explore the IS recognition using a multimodal large language model, GPT4V, with prompt engineering provided with FOS-R-III measure definitions as the baseline for this study and compare with other vision-based deep learning algorithms. We believe this research represents a significant advancement in autism research and clinical accessibility. The proposed AV-FOS and our FOS-R-III dataset will serve as a gateway toward the digital health era for future AI models related to autism.

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