1. Amaral JZ, Kallur A, Dolan LA, Nguyen AQ, Schultz RJ, Martin BM, Coello P, Scioscia JP, Chhabra BN, Hanson DS, Gerow FT, Smith BG. Bracing Outcomes and Risk of Curve Progression in Adolescents with Idiopathic Scoliosis and Autism Spectrum Disorder. J Bone Joint Surg Am. 2025.

BACKGROUND: Whether the sensory and behavioral traits of autism spectrum disorder (ASD) affect bracing outcomes in adolescent idiopathic scoliosis (AIS) remains unclear. This study evaluated the impact of ASD on bracing success, curve progression, and patient-reported outcomes in patients with AIS. METHODS: This retrospective study included patients 10 to 18 years of age who were treated for AIS with bracing between 2011 and 2024. A total of 58 patients with ASD were matched in a 1:2 ratio to 116 controls with use of nearest-neighbor matching based on BrAIST-Calc predicted probabilities. Exclusions included non-idiopathic scoliosis, early-onset scoliosis, kyphoscoliosis, a Risser stage of >2, pre-treatment curves of <25° or >40°, and inadequate follow-up. Progression to the surgical threshold was defined as a major curve of ≥45°. Firth logistic regression was used to model the association between ASD and progression to the surgical threshold, adjusting for residual imbalances. RESULTS: The matched cohort (n = 174; 51% male; 40% White, 25% Hispanic, 21% Black, 10% Asian, and 5% not specified) demonstrated balanced propensity scores (SMD = 0.006). Compared with patients without ASD, those with ASD had higher rates of progression to the surgical threshold (40% versus 20%; p = 0.005), a curve progression of ≥6° (60% versus 38%; p = 0.005), noncompliance (36% versus 22%; p = 0.04), brace-related issues (22% versus 8%; p = 0.006), and surgery being recommended or performed (33% versus 13%; p = 0.002). In the multivariable analysis, ASD (odds ratio [OR], 3.12 [95% confidence interval (CI), 1.32 to 7.35]; p = 0.009), noncompliance (OR, 4.00 [95% CI, 1.65 to 9.71]; p = 0.002), and a greater initial curve magnitude (OR per degree, 1.26 [95% CI, 1.15 to 1.38]; p < 0.001) significantly increased the odds of progression to the surgical threshold. Within the ASD group, Scoliosis Research Society-22 revised (SRS-22r) self-image, management, and total scores improved significantly over time. No significant between-group differences in change scores were observed. CONCLUSIONS: Adolescents with ASD were >3 times more likely to progress to the surgical threshold and had higher rates of noncompliance, brace-related issues, and surgery being recommended or performed. ASD may represent a risk factor for bracing failure, potentially related to sensory or behavioral intolerance. Nonetheless, 60% of patients with ASD avoided progression to the surgical threshold, and within-group improvements in SRS-22r scores were observed. These findings support bracing as a viable treatment option for patients with ASD, although it is likely best paired with individualized care and closer follow-up. Future studies should aim to improve brace tolerance and adherence in this population. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

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2. Brown CE, Brossard RF, Kline GC, Borduin CM. Sibling Relationship Profiles of Autistic Youths in a Social-Ecological Context. J Autism Dev Disord. 2025.

PURPOSE: This study aimed to identify profiles of autistic youths’ sibling relations and examined if social-ecological variables (i.e., youth characteristics, family and caregiver functioning, peer relations, academic performance) were associated with these profiles. METHOD: Caregivers (N = 2,142; 88.1% mothers) of autistic youths aged 6-17 years (M = 11.07 years; SD = 3.17; 80.1% male) completed electronic measures assessing social-ecological variables and youths’ sibling relations. We used a latent profile analysis (LPA) to define sibling relation profiles based on the qualities (emotional support, companionship, conflict, and criticism) of relations between autistic youths and their closest-in-age siblings. We performed ANOVAs to compare sibling profiles on social-ecological variables. RESULTS: The LPA yielded a 3-profile solution: a positive group (18.2%), a negative group (17.2%), and a low engagement group (64.5%). ANOVAs and χ(2) analyses revealed no between-group differences on age, gender, history of co-occurring cognitive impairment, or autism characteristics. Youths with positive sibling relations had more adaptive family and caregiver relations than did youths with low engagement sibling relations, who, in turn, had more adaptive family and caregiver relations than the youths with negative sibling relations. Youths with positive sibling relations had more friends and closeness with friends than did youths with low engagement or negative sibling relations. CONCLUSION: Most sibling relations among autistic youth fit a low engagement profile based on caregiver report. Positive sibling relations were linked with positive functioning in other social-ecological domains. The nature of these linkages warrants further investigation, particularly using longitudinal, multi-informant, and mixed-method designs.

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3. Carpita B, Bonelli C, Pini S, Cappiello C, Nardi B, Pustynnikova M, Del Grande F, Pioltino M, Massimetti G, Cremone IM, Luciano M, Fiorillo A, Dell’Osso L. The mediating effect of trauma and loss spectrum on the relationship between autistic traits and eating disorder symptoms among patients with Borderline personality disorder. CNS Spectr. 2025: 1-22.

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4. Caruso A, Rasga C, Fulceri F, Scattoni ML, Micai M. Editorial: Empowering early career researchers in psychiatry: advancing autism research. Front Psychiatry. 2025; 16: 1718609.

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5. Cazares C, Hutton A, Paez G, Trauner D, Voytek B. Cannabidiol blood metabolite levels after cannabidiol treatment are associated with broadband EEG changes and improvements in visuomotor and non-verbal cognitive abilities in boys with autism requiring higher levels of support. medRxiv. 2025.

Oral cannabidiol (CBD) treatment has been suggested to alleviate severe symptoms of autism spectrum disorder (ASD). While many CBD preparations have been studied in clinical trials involving ASD, none has used purified CBD preparations or preparations approved by the U.S. Food and Drug Administration, nor have they focused on children with ASD with higher support needs. Previous studies have identified several candidate electrophysiological biomarkers of cognitive and behavioral disabilities in ASD, with emerging biomarkers including periodic (oscillatory) and aperiodic measures of neural activity. We analyzed electroencephalography (EEG) recordings from 24 boys with ASD and higher support needs (aged 7-14 years) from a prior double-blind, placebo-controlled, crossover Phase II Clinical Trial ( NCT04517799 ) that investigated whether 8 weeks of daily CBD treatment (up to 20 mg/kg/day) improved severe behavioral problems, measured at baseline, post-CBD, post-placebo, and post-washout. Using linear mixed effect models, we found that aperiodic EEG measures varied with CBD metabolite levels in blood, as evidenced by a larger aperiodic offset across the scalp and a decreased aperiodic exponent across occipital electrodes. Furthermore, CBD metabolite levels in blood had a positive association with receptive vocabulary, nonverbal intelligence and visuomotor coordination. Our data suggest that this daily CBD preparation and administration schedule produced mixed effects, with some children showing improvements in cognitive and behavioral abilities while others demonstrated limited changes. Our findings support the inclusion of aperiodic EEG measures alongside traditional oscillatory EEG measures as candidate biomarkers for tracking the variable clinical impact of purified CBD treatment in children with ASD.

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6. Cirnigliaro M, Lowe JK, Flynn-Carroll AO, Kumagai ME, Gibson DS, Fu JM, Dong S, Hou K, Pillalamarri V, Abbacchi AM, Gulsrud AC, Miller J, Zhang Y, Graham ET, Akinyemi EO, Adams MF, Clay AN, Arteaga SA, Choi H, Kochis RM, Peña-Velasco JE, Hoekstra JN, Besterman AD, Mehta S, Hadzic T, Wilson RB, Brown TR, Hernandez LM, Marrus N, Molholm S, Klaiman C, Cantor RM, Talkowski ME, Sanders SJ, Arking DE, Pasaniuc B, Klin A, Constantino JN, Geschwind DH. Common and rare variant genetic contributions in African Americans with autism. medRxiv. 2025.

The absence of non-European cohorts in genetic studies of neurodevelopmental and neuropsychiatric disorders severely limits the understanding of their full genetic architecture and undermines implementation of precision medicine. Here, we directly addressed this issue by recruiting African Americans (AfrAms) with autism spectrum disorder (ASD) and analyzing their rare and common genetic variation. We performed both global and local ancestry analyses to characterize the complex patterns of admixture at the individual level and compare genetic factors between European (EUR) and African (AFR) genetically inferred ancestries (GIAs) across multiple cohorts in a total of 38,483 autistic individuals. We showed consistent common variant genetic effect sizes for ASD in EUR and AFR GIAs through genome-wide association studies. We demonstrated the limited transferability of EUR-derived polygenic scores (PGSs) based on polygenic transmission disequilibrium and ancestry partial PGS analysis. We found significant autism association for high-impact rare copy number variants in both GIAs. We identified a set of candidate ASD loci based on rare deletions observed in AFR GIA carriers, including SMC2 , DMTN , SORCS1 , and ROGDI , and detected a signal for de novo missense variants of predicted low impact in AFR GIA individuals. Finally, we uncovered significant depletion of AFR GIA autistic carriers of rare variants in known associated genes found in EUR cohort studies. These findings are the first to detail common and rare variant genetic contributions to ASD in AfrAms and demonstrate that their involvement in neurodevelopmental and neuropsychiatric disorders’ genomic research is essential to advance discovery.

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7. Coburn KL, Girolamo T. Introduction to the Forum: Intersectional Approaches to Language in Autism. Perspect ASHA Spec Interest Groups. 2025; 10(3): 651-4.

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8. Cohen JS, Patel P, Finney A, Zheng M, Badaki O, Prichett L. Prolonged Emergency Department Stays for Patients With Autism and Acute Mental Health Concerns. J Emerg Med. 2025; 80: 253-65.

BACKGROUND: Children with autism spectrum disorders face high rates of mental health emergencies often requiring hospitalization, but limited psychiatric unit access often leaves them waiting in emergency departments (EDs). OBJECTIVE: To determine if autism is an independent risk factor for prolonged emergency department (ED) stays (>90%), ED psychiatric boarding (>24 hours), and prolonged ED boarding (>48 or 72 hours) for children with mental health concerns requiring hospitalization METHODS: This was a retrospective cohort study using the PECARN Registry Dataset from 2016 through 2021. All ED encounters for patients ages 5 to 18 years of age requiring admission for a primary mental health diagnosis were included. The primary outcome was ED length of stay, the primary exposure variable was autism, and potential confounding variables were age, sex, race/ethnicity, and insurance status. RESULTS: A total of 73,624 ED visits were included, 66,113 visits without autism and 7511 with autism. When adjusted for age, sex, race and insurance, admitted patients with autism had higher odds of a prolonged ED stay (aOR 1.26, 95% CI 1.15-1.38), ED boarding (aOR 1.68, 95% CI 1.41-2.00), and prolonged ED boarding > 48 hours (aOR 3.91, 95% CI 2.62-5.84) and > 72 hours (aOR 3.91, 95% CI 2.14-7.14). CONCLUSION: Autism is an independent risk factor for having a prolonged ED stay, for boarding in the ED, and for prolonged ED boarding when presenting to the ED with a mental health crisis that requires hospitalization.

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9. Das M, Halder A, Mahadevappa M. Deep-Fusion of Scalogram and Spatio-Temporal EEG Features with Attention Mechanism for Autism Spectrum Disorder Identification. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-6.

Autism Spectrum Disorder is a neurodevelopmental condition characterised by social and communication challenges, repetitive behaviours, and sensory sensitivities. Early diagnosis allows for timely intervention, improving quality of life. This study introduces a novel approach to Autism Spectrum Disorder detection using resting-state electroencephalography (EEG), which captures neuronal activity to identify Autism Spectrum Disorder-related abnormalities. Our methodology integrates spatiotemporal EEG features with image-based features extracted from scalograms. To improve classification performance, we proposed a novel deep-learning pipeline that combined scalogram and EEG signal features to emphasise feature fusion. This fusion was processed through an EfficientNet-based model, enhanced with a Convolutional Block Attention Mechanism to capture comprehensive spatial and temporal representations. The attention module leveraged both channel and spatial attention to refine feature extraction. Our model achieved an accuracy of 84%, with an F1-score of 82%, recall of 82%, precision of 82%, and an AUC-ROC of 0.89, demonstrating the potential of EEG in distinguishing ASD from healthy controls.Clinical relevance-EEG offers a non-invasive, cost-effective, and objective approach to early Autism Spectrum Disorder detection. Unlike subjective behavioural assessments, it provides direct neural insights, enabling scalable screening, especially in young children. This study enhances the diagnostic accuracy of Autism Spectrum Disorder by integrating single channel scalogram and spatiotemporal-based deep feature fusion with attention mechanism, establishing EEG signatures as a promising biomarker and a valuable clinical tool.

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10. Dickinson M, Karaminis T. The relationship between newspaper reading preferences and attitudes towards autism. Autism. 2025: 13623613251394523.

Newspapers – particularly certain tabloid and right-leaning publications – often perpetuate negative stereotypes of Autistic people. This study examined how newspaper reading preferences relate to public attitudes towards autism. A sample of 277 UK-based non-autistic adults completed an online survey reporting demographic information, newspaper reading habits (print or online) and trustworthiness ratings for 10 major British newspapers. Participants also completed measures of autism-related knowledge and explicit attitudes towards autism, and an implicit bias task. Data were analysed using generalized additive models weighted by newspaper exposure, along with hierarchical partitioning to estimate the variance explained by reading preferences and other factors. Newspaper-related factors accounted for 4.5% of the variance in explicit attitudes and 10.7% in implicit attitudes. Crucially, selective engagement with right-leaning tabloids was associated with more negative implicit attitudes. Furthermore, selective trust in these outlets predicted relatively favourable explicit but relatively negative implicit attitudes. Overall trust in newspapers was also linked to less accurate autism knowledge. These findings suggest that engagement with media sources portraying autism more negatively is linked to more negative implicit biases towards Autistic people, even when explicit attitudes remain relatively favourable. Future research should explore causal mechanisms and the broader influence of media ecosystems on public perceptions of autism.Lay abstractWhen newspapers discuss Autistic people, they often focus on their challenges rather than their strengths. This kind of reporting – especially in some tabloids and right-leaning newspapers – can reinforce negative stereotypes, making it harder to build a more inclusive society for Autistic people. However, we do not yet fully understand how newspaper coverage relates to neurotypical people’s attitudes towards autism, particularly when considering their background, knowledge of autism and personal experiences with Autistic people. This study investigated whether there is a connection between the newspapers people read and trust, and their feelings about autism. We examined both openly expressed opinions (explicit attitudes) and more instinctive, less conscious reactions (implicit attitudes). We surveyed 277 non-autistic adults in the United Kingdom. Participants reported how often they read 10 major British newspapers (in print or online) and how much they trusted them. They also answered questions about their knowledge of autism and their attitudes towards Autistic people. In addition, participants completed a short word-based task designed to reveal more subtle, instinctive responses. The results showed that individuals who regularly read right-leaning tabloids – which more frequently feature negative coverage of autism – tended to display more negative automatic responses towards autism. Interestingly, some participants who highly trusted these outlets expressed relatively positive explicit views, while their task responses suggested they might still hold relatively negative unconscious biases. Finally, greater overall trust in newspapers was linked to lower levels of autism knowledge. Taken together, these findings highlight a potential relationship between the media we consume and trust and not only what we know, believe and openly say about autism, but also our deeper, less conscious attitudes and reactions. While this study does not prove that news media directly shape or cause changes in attitudes, it underscores the importance of respectful, balanced reporting in fostering greater understanding and acceptance of Autistic people.

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11. Djiko T, Phelps K, Acosta A, Platt E, Franke K, Wippold G, Bradshaw J. The Role of Stigma in the Autism Diagnostic and Intervention Process: Perspectives of Black Families in the Southeastern US. J Autism Dev Disord. 2025.

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12. Fodstad JC, Russell R, Bryant LO, Tadevich LJ, Dwenger D, Gray MA. Improving Care for Autistic Youth in Correctional Settings. J Am Acad Psychiatry Law. 2025; 53(4): 363-72.

Youth on the autism spectrum who engage in delinquent or violent crimes can be adjudicated to juvenile correctional settings. These settings, which are meant to successfully reintegrate youth back into the community through education, counseling, and skills programs, are often ill-equipped to navigate the unique needs of youth on the autism spectrum. As a result, autistic youth in juvenile correctional settings often do poorly, minimizing the likelihood that successful reintegration occurs. The purpose of this review is to summarize the literature on the prevalence of autistic youth in correctional settings and their needs, as well as the standard of care often afforded to them in these settings. Finally, we will present suggested strategies informed by the literature whereby adjudicated autistic youth are provided services and support that are feasible in a correctional setting and align with autism-informed, evidence-based practices.

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13. Horien C, Mandino F, Greene AS, Shen X, Powell K, Vernetti A, O’Connor D, Adkinson BD, Tejavibulya L, McPartland J, Volkmar F, Chun M, Chawarska K, Lake EMR, Rosenberg M, Satterthwaite T, Scheinost D, Finn ES, Constable T. Do in-scanner tasks outperform rest for predicting autistic traits using functional connectivity data?. medRxiv. 2025.

Autism is a heterogeneous condition, and functional magnetic resonance imaging-based studies have advanced understanding of neurobiological correlates of autistic features. Nevertheless, little work has focused on the optimal brain states to reveal brain-phenotype relationships. In addition, there is a need to better understand the relevance of attentional abilities in mediating autistic features. Here, we used connectome-based predictive modelling to identify brain-behavior relationships. We analyzed four datasets to determine scanning conditions that can boost prediction of clinically relevant phenotypes and assess generalizability. Across all four samples, we observed successful prediction. Specifically, in dataset one, a sample of youth with autism and neurotypical participants (n = 63), we found that a sustained attention task (the gradual onset continuous performance task) resulted in high prediction performance of autistic traits compared to a free-viewing social attention task and a resting-state condition. In dataset two (n = 25), we observed the predictive network model of autistic traits generated from the sustained attention task generalized to predict measures of attention in neurotypical adults. In datasets three and four, we determined the same predictive network model of autistic traits from dataset one further generalized to predict measures of social responsiveness in data from the Autism Brain Imaging Data Exchange (n = 229) and the Healthy Brain Network (n = 643). We further generated predictive models of social responsiveness in the Healthy Brain Network sample, finding task-based models outperformed rest-based models. A consensus model from the Healthy Brain network subsequently generalized to predict ADOS scores in dataset one. In sum, our data suggest that an in-scanner sustained attention challenge can help delineate robust markers of autistic traits and support the continued investigation of the optimal brain states under which to predict phenotypes in psychiatric conditions.

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14. Khamenehei N, Tokarskaya L. Comparative Visual Perception Patterns in Autism Spectrum Disorder and Mild Intellectual Disability: A Cross-Sectional Study. Consort Psychiatr. 2025; 6(3): 23-34.

BACKGROUND: Visual perception plays a crucial role in cognitive and behavioral development. Individuals with autism spectrum disorder (ASD) and mild intellectual disability (ID) exhibit distinct patterns of visual processing that influence their learning and interaction with the environment. AIM: This study aims to compare the visual perception abilities of children with ASD and those with mild ID. METHODS: This study employed an experimental comparative design. The Bender Visual-Motor Gestalt Test was administered to assess visual-motor integration, perceptual organization, and spatial processing abilities. It was scored based on standard qualitative and quantitative criteria. Group comparisons were conducted using descriptive statistics and cross-group performance patterns. RESULTS: A total of 15 children (8 with ASD and 7 with mild ID), aged between 7 to 12 years, participated in the study. Children with ASD demonstrated superior spatial organization and attention to local details, whereas children with mild ID demonstrated significant difficulties in perceptual coherence, spatial alignment, and motor coordination. CONCLUSION: The study highlights the importance of developing tailored intervention strategies that address the distinct perceptual processing styles associated with ASD and mild ID. However, limitations such as a lack of detailed diagnostic criteria, absence of symptom severity differentiation, and failure to control for developmental age must be considered when interpreting the findings. Future research should aim to overcome these limitations by including standardized diagnostic measures, creating a larger and more diverse sample, and involving additional assessment tools for a more comprehensive analysis.

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15. Ladani FG, Karimi N, Mirmahboub B, Sobhaninia Z, Shirani S, Samavi S. Using fMRI Time Series and Functional Connectivity for Autism Classification: Integrating Mamba and KAN in Domain-Adversarial Neural Networks. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-6.

Domain differences in fMRI analysis often cause biases that negatively affect Autism classification. To address this, we propose a novel pipeline leveraging a Domain Adversarial Neural Network (DANN) architecture to extract domain-invariant yet classification-informative features by integrating Mamba and Kolmogorov-Arnold Network (KAN) models. The DANN framework consists of an extractor, a domain classifier, and a label classifier, trained in an adversarial way to reduce domain bias while maintaining classification accuracy. The extractor employs two parallel paths: one processes fMRI time series with the Mamba model, and the other analyzes functional connectivity data using the KAN. The extracted features are concatenated and utilized by KAN-based domain and label classifiers. Adversarial training ensures the domain classifier cannot distinguish domain labels, confirming the domain invariance of the features. Experimental results show that this method achieves an accuracy of 72.56% and an AUC of 72.46%, demonstrating comparability to state-of-the-art methods which rely solely on fMRI data without utilizing phenotype information. Source code and implementation details are available at https://github.com/fatemehghanadi/fMRI-Based-Autism-Classification-Mamba-KAN-with-DANN.Clinical Relevance- The proposed approach effectively mitigates domain-induced biases and offers a robust solution for fMRI-based Autism classification tasks.

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16. Lamoglia FM, Bastos GS. The Impact of rs-fMRI Preprocessing on the Quality of Machine Learning Models for Autism Spectrum Disorder Diagnosis. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-5.

Tools for aiding in the diagnosis of Autism Spectrum Disorder (ASD) using machine learning (ML) and resting-state rs-fMRI (rs-fMRI) must encompass different phases such as data collection, preprocessing, feature extraction, model training, and validation. Many studies rely on a single preprocessing pipeline or use preprocessed data, which might not be optimal for the task at hand. This study investigates the impact of rs-fMRI preprocessing on the performance of ML models for ASD diagnosis. Using a subset of the Autism Brain Imaging Data Exchange (ABIDE) dataset, 72 subjects were preprocessed with 108 different configurations, and features were extracted to train 13 ML classifiers. Results indicate that preprocessing choices significantly influence model accuracy, with the best configurations achieving up to 95.83% accuracy. However, generalization tests on an extended dataset revealed a substantial performance drop, highlighting challenges in model robustness. Findings emphasize the need for adaptive preprocessing strategies and gender-balanced datasets to improve ASD classification reliability.

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17. Leroy G, Bisht P, Kandula SM, Maltman N, Rice S. Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach. Artif Intell Med. 2025; 172: 103318.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose rising prevalence places increasing demands on a lengthy diagnostic process. Machine learning (ML) has shown promise in automating ASD diagnosis, but most existing models operate as black boxes and are typically trained on a single dataset, limiting their generalizability. In this study, we introduce a transparent and interpretable ML approach that leverages BioBERT, a state-of-the-art language model, to analyze unstructured clinical text. The model is trained to label descriptions of behaviors and map them to diagnostic criteria, which are then used to assign a final label (ASD or not). We evaluate transfer learning, the ability to transfer knowledge to new data, using two distinct real-world datasets. We trained on datasets sequentially and mixed together and compared the performance of the best models and their ability to transfer to new data. We also created a black-box approach and repeated this transfer process for comparison. Our transparent model demonstrated robust performance, with the mixed-data training strategy yielding the best results (97 % sensitivity, 98 % specificity). Sequential training across datasets led to a slight drop in performance, highlighting the importance of training data order. The black-box model performed worse (90 % sensitivity, 96 % specificity) when trained sequentially or with mixed data. Overall, our transparent approach outperformed the black-box approach. Mixing datasets during training resulted in slightly better performance and should be the preferred approach when practically possible. This work paves the way for more trustworthy, generalizable, and clinically actionable AI tools in neurodevelopmental diagnostics.

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18. Lira FFL, Martins M, Santos A, Chalegre LC, Bittencourt IGS. Sex education for adolescents with autism spectrum disorder: an integrative literature review. Cien Saude Colet. 2025; 30(11): e10992025.

The present study aimed to identify scientific approaches to teaching sex education to adolescents with autism spectrum disorder (ASD), analyzing social implications and the roles of parents, caregivers, and professionals. This is an integrative literature review based on the Joanna Briggs Institute methodology, which allows for mapping key concepts and identifying gaps in the literature. The research question was developed using the Population, Concept, and Context (PCC) strategy for the review, as follows: P – parents, caregivers, and health professionals; C – how to adequately implement sexuality education during adolescence; C – adolescents with ASD. The final sample included 24 studies, which were organized into four thematic axes: bodily changes, sexual relationships, affective relationships, and self-protection. The findings revealed divergences regarding who should address the topic with adolescents (parents, schools, or health professionals), in addition to the scarcity of personalized and effective programs. In this light, our study highlighted the need for initiatives that ensure sexuality education adapted to the particularities of adolescents with ASD, promoting their healthy development and protection.

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19. Liu X, Niu B, Cao T, Chen F. A Deep Learning Method for Autism Spectrum Disorder Classification Based on Multimodal Neuroimaging Data(). Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-5.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication skills. Accurate, early-stage differentiation of individuals with ASD from typically developing controls (TC) is essential for timely intervention and treatment. In this paper, we propose a predictive model based on multimodal feature fusion, using both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) data to improve the classification of ASD. By integrating complementary information from these two modalities, our method constructs a more comprehensive feature space, capturing complex neuropathological signatures that a single modality cannot provide. We evaluated the proposed approach using imaging data from the ABIDE NYU site under a five-fold cross-validation scheme. The experimental results show that the proposed method achieved an average accuracy of 82.63%, an area under the receiver operating characteristic curve (AUC) of 89.31%, a sensitivity of 81.45%, and a specificity of 82.86%. These findings suggest that the proposed multimodal feature fusion strategy significantly enhances ASD identification, offering a promising approach to the precise diagnosis of brain disorders.Clinical Relevance- We proposed a learning framework that integrates multi-modality neuroimaging data, addressing the heterogeneity of ASD-related brain features and the challenges posed by limited training data. This framework contributes to improving diagnostic accuracy and supports early clinical decision-making for ASD, thereby facilitating timely intervention and the development of personalized treatment strategies in clinical practice.

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20. Lokray S, Zikopoulos B, Yazdanbakhsh A. Structural changes in autism reflect atypical brain network organization and phenotypical heterogeneity: a hybrid deep network approach. bioRxiv. 2025.

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by heterogeneity in social and emotional responses, language, and behavior. Assessments such as the Social Responsiveness Scale (SRS) can quantify this variability but understanding underlying mechanisms and identifying distinct and shared atypical organization and function of brain networks remains a challenge. Convolutional Neural Networks (CNNs) have been used to analyze imaging data. However, the relationship between structural brain changes observed in structural MRI (sMRI), the affected brain functional networks inferred from these structural changes, and their connection to ASD phenotypes and scores still requires systematic investigation. In this study, we ensembled 3D CNNs with other artificial intelligence (AI) methods to conduct a comprehensive analysis of macrostructural changes in ASD. We found consistently dominant involvement of (a) the left hemisphere, (b) the frontal and temporal lobe, and (c) the default mode, salience, and language networks in ASD. Our findings highlighted brain network similarities and differences between high and low severity ASD and showed that typically developed brains fall at the low-severity end of the high-to-low severity spectrum of ASD. Our systematic AI approach utilized the phenotypic heterogeneity and spectral nature of ASD to uncover significant structural changes across brain regions and functional networks, correlating the structural, functional, and phenotypical heterogeneity of individuals with ASD. This enabled us to identify known and novel global and local brain region and network changes in ASD in relation to phenotypes and clinical scores that can guide diagnostic subtyping.

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21. Long J, Liao X, Chen J, Han K, Tang Z, Dong L, Wang X, Liu J, Zhang Y, Zhang H. New insights into mechanisms of valproic acid-induced neurodevelopmental toxicity in autism spectrum disorder: An integrative network toxicology approach combined with in vivo validation. Food Chem Toxicol. 2025; 208: 115882.

Autism spectrum disorder (ASD) is a neurodevelopmental condition with growing evidence linking its etiology to chemical exposures, including valproic acid. This study employed an integrative approach combining network toxicology, molecular docking, Mendelian randomization (MR), GEO data analysis, and experimental validation to systematically elucidate the mechanisms of valproic acid (VPA)-induced neurodevelopmental toxicity in ASD. Computational toxicity predictions identified hepatotoxicity, respiratory toxicity, and pronounced blood-brain barrier penetration as key features of VPA. Network toxicology analysis predicted core targets, including epigenetic regulators (HDAC1, SIRT1), drug-metabolizing enzymes (CYP3A4, CYP2C19), and signaling mediators (RXRA, ESR1, RELA, NOS1), as pivotal in mediating VPA’s neurodevelopmental toxicity. KEGG enrichment analysis highlighted alterations in neuroactive ligand-receptor interactions, CYP450 metabolism, and estrogen signaling. MR analysis only suggested a weak causal link between SIRT1 and ASD risk (OR = 1.073, p = 0.022), underscoring that VPA may contribute to ASD more through environmental perturbation than through strong genetic predisposition. Prenatal VPA-exposed rats exhibited core autistic-like behaviors (social deficits and repetitive behaviors), with neuronal degeneration in the prefrontal cortex, hippocampal dentate gyrus, and striatum. Western blotting and GEO data revealed downregulated HDAC1 and ESR1 expression, while fecal metabolomics identified 383 differentially abundant metabolites. Importantly, we establish CYP450-dependent metabolic dysregulation as a novel core mechanism in VPA-induced ASD, significantly affecting steroid hormone biosynthesis and arachidonic acid metabolism. This study underscores VPA’s multi-target toxicity in ASD via epigenetic dysregulation, neuroinflammation, CYP450-dependent pathways, and gut-brain axis neurotransmitter disturbances, providing a framework for understanding chemical contributions to ASD pathogenesis.

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22. Maabreh RS, Rababa M, Al-Za’areer MS, Elshatarat RA, Saleh ZT, Eltayeb MM, Oleimat B, Alhumaidi BN, Aboelmagd AN, Ebeid IA. Prevalence, severity, and associated factors of post-traumatic stress disorder (PTSD) symptoms among parents of children diagnosed with autism: A cross-sectional study. J Educ Health Promot. 2025; 14: 435.

BACKGROUND: Parents of children diagnosed with autism spectrum disorder (ASD) face significant challenges, often resulting in heightened stress levels. Emerging evidence suggests that these parents may experience post-traumatic stress disorder (PTSD) symptoms due to their child’s diagnosis. However, the role of perceived social support in mitigating these symptoms remains unclear. This study aimed to examine the relationship between the determinants of PTSD among parents of children with ASD and to explore the moderating effect of perceived social support on PTSD severity. MATERIALS AND METHODS: A cross-sectional correlational design was employed. A convenience sample of 142 parents of children diagnosed with ASD was recruited from specialized centers in Amman, Jordan between August 2023 and March 2024. Parents undergoing treatment for mental health conditions were excluded. Data were collected through structured self-reported questionnaires, including the PTSD Checklist for DSM-5 (PCL-5) and the Multidimensional Scale of Perceived Social Support (MSPSS). Descriptive statistics, t-tests, and ANOVA were used for analysis. RESULTS: The study found that parents of children with ASD exhibited significant PTSD symptoms, with an average PCL-5 score of 42.08 (±15.61 out of 80). Key sociodemographic factors, such as marital status, education level, and employment, were significantly associated with PTSD scores (P < 0.05). The study also revealed that perceived social support, as measured by the MSPSS, had a moderating effect on PTSD symptoms, with parents reporting higher social support experiencing lower PTSD severity. CONCLUSION: Parents of children with ASD are at increased risk for PTSD, but perceived social support plays a critical role in reducing symptom severity. These findings underscore the importance of social support interventions for this population.

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23. Manoj G, Saini P, Ratnaik R, Sengar SS, Ganapathy N, Pa K, Agastinose Ronickom JF. Age-Stratified Differences in Morphological Connectivity Patterns in ASD: An sMRI and Machine Learning Approach. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-5.

Autism spectrum disorder (ASD) is one of the most common neurological disorders, and its early detection is extremely difficult. Researchers use different physiological and medical imaging signals to diagnose ASD based on the severity level and the age of the patient. In this study, morphological features (MF) and morphological connectivity features (MCF) are used to investigate the influence of age on the diagnosis of autism spectrum disorders (ASD). In this work, we have utilized structural magnetic resonance imaging (sMRI) data from ABIDE-I and ABIDE-II databases, divided into 6-11, 11-18, and 6-18 age groups, were pre processed and yielded 592 MF and 10,878 MCF per subject. As a result, the 6-11 age group outperformed the others in both feature types, especially in MCF, with a random forest (RF) classifier achieving 75.8% accuracy, 83.1% F1 score, 86% recall, and 80.4% precision, respectively. Based on this, it can be concluded that an age-specific morphological connectivity approach holds promise for effective diagnosis of autism spectrum disorders.

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24. Mayrose N, Eni M, Bilik I, Zigel Y. Sound Source Localization for Autistic Children’s Session Recordings. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-6.

This work addresses the problem of localizing simulated sound sources for the Autism Diagnostic Observation Schedule 2(nd) edition (ADOS-2) observation room. The main challenge stems from the unconventional, nonuniform infrastructure-installed microphone array that prevents the adoption of the conventional beamforming approaches. The proposed deep neural network (DNN)-based source localization approach is formulated as a classification problem, where the sound source location is classified into one of four zones within the ADOS-2 room. Two architectures were introduced based on a bidirectional long short-term memory (BiLSTM) network and a hybrid BiLSTM with a transformer encoder. The classification accuracy of 85-89% was demonstrated in diverse acoustic environments using various microphone array configurations. It was shown that the proposed approach could achieve an efficient localization performance in clinical settings, indicating its potential applications in autism diagnosis and treatment.Clinical relevance-Speech-based sound source localization (SSL) in autism evaluation sessions can provide valuable insights into children’s spatial behavior and interaction patterns. Additionally, it has the potential to improve the quality of speech recordings, thereby supporting more accurate autism diagnosis and intervention strategies.

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25. Midya V, Bello GA, Gomez LA, Marin MR, Piyankarage SC, Elhlou S, Chumber J, Jaramilo J, Dessalle S, Yitshak-Sade M, Cantoral A, Wright RJ, Wright R, Nakayama S, Bennett DH, Schmidt RJ, Bölte S, Arora M. Rule-out test for autism using machine-learning analysis of molecular temporal dynamics in hair – a multicenter study. medRxiv. 2025.

BACKGROUND: Early intervention can improve autism-related outcomes. However, no valid biosignature test exists yet for detecting or excluding autism. In addition, most behavior-based assessments of autism are developed for children aged 18 months and older. We developed a hair-strand-based biomarker test (diagnostic aid) to assist clinicians in ruling out autism in children aged 1 month and older. METHODS: In a multi-national sample of 1697 (from California (two studies, n= 1112), New York City (n= 123), Sweden (n= 306), Japan (n= 110), and Mexico City (n= 46), with 97% below 21 years-of-age), autism was assessed using DSM-5 criteria for autism spectrum disorder or gold standard diagnostic instruments (ADOS-2 and/or ADI-R). A single hair-strand from children collected at 1 month or older was analyzed using laser ablation-inductively coupled plasma-mass spectrometry to sample down the shaft, generating time-series data at a resolution of ∼800 timepoints (on average) for 12 elemental intensities. We employed a multi-layered machine learning architecture to leverage the temporality of elemental intensities and optimized the test negative predictive value (NPV) and sensitivity. Models were trained, ensembled, and tuned on participants from California and Sweden, then tested on 580 participants (within-population replication in California and Sweden, and external population testing in New York, Mexico City, and Japan). RESULTS: The diagnostic aid showed an AUC of 0.75 (95%CI: 0.70-0.79), 97% NPV (95%CI: 0.92-0.99), and 96% sensitivity (95%CI: 0.91-0.98), respectively (estimated prevalence for NPV set at 14%). Moreover, for those 36 months or younger, the AUC was 0.79 (95%CI: 0.73-0.85), with 97% NPV (95%CI: 0.87-0.99), and 96% sensitivity (95%CI: 0.88-0.99), respectively. Compared to the baseline odds of autism before taking the test (pre-test odds), those who test negative are, on average, ∼82% lower in odds of autism diagnosis, whereas those who test positive are ∼25% higher. CONCLUSIONS: By estimating autism likelihood as early as 1 month after birth, early intervention can be delivered with higher precision to young children with developmental support needs. Clinicians may use this diagnostic aid to support early autism diagnosis, improve clinical workflows, and significantly reduce wait times for services.

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26. Morales-Marín ME, Náfate-López O, Gómez-Cotero AG, Cid-Soto MA, Nicolini H, Castro-Martínez XH. MTHFR C677T is not associated with autism spectrum disorder in a Mexican cohort. Front Psychiatry. 2025; 16: 1452940.

Using genetic approaches to study autism spectrum disorder (ASD) is essential to understanding the etiology of the condition. The C677T variant has emerged as a risk factor, and here we present the first association study of this variant in a Mexican population with ASD. Our objective was to assess the variant MTHFR C677T (rs1801133) in a group of Mexican patients with ASD through a case-control association analysis. We found no significant association of MTHFR C677T and ASD, with no rate differences between cases and controls (C vs T: odds ratio = 0.9698, 95% confidence interval = 0.7773-1.21, P = 0.7858). Results of this and other studies evaluating the link between ASD and this variant have been controversial. Our findings suggest that other ancestry-related factors may play a role.

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27. Moreno RJ, Rose D, Ashwood P. Altered Phenotype and Gene Expression of Regulatory T cells (Tregs) in Children with Autism, and the Relationship with Comorbid Gastrointestinal Symptoms. Res Sq. 2025.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by social deficits and stereotypic behaviors. Increased numbers of inflammatory cells and their mediators have been found in peripheral blood, brain, and gastrointestinal (GI) tissues of individuals with ASD. Regulatory T cells (Tregs) play a crucial role in suppressing inflammatory processes that, if disrupted, can result in inflammation and development of a variety of immunological conditions. In this study, we sought to characterize Tregs populations using flow cytometry and gene transcriptomic approaches in children with ASD (n = 36) and typically developing (TD) children (n = 18) enrolled in the Childhood Autism Risk from Genetics and Environment (CHARGE) study. We also examined differences in the frequencies of activated Tregs in ASD groups when stratified by co-occurring GI status. The frequency of gut homing α4b7 (+) Tregs positive for inhibitory receptors GITR, LAP, and/or GARP were altered based on the presence of GI symptoms. Analysis of mRNA isolated from CD4 (+) CD25 (+) Tregs, showed 213 differentially expressed genes (DEGs) between the ASD and TD children. Upregulated DEGs were enriched in Gene Ontology (GO) Biological Processes involved in epigenetic regulation including ‘chromatin organization’, whereas Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were involved in metabolism, including ‘lipid and atherosclerosis’ and ‘pantothenate and CoA biosynthesis’. Upregulation of immune signaling genes (MAPK3, JAK2, and CASP3) was also noted in the ASD Tregs. Downregulated DEGs consisted of genes enriched in immune terms including the GO term ‘leukocyte differentiation’ and KEGG pathways ‘Parkinson disease’ and ‘protein processing in endoplasmic reticulum’. Correlation analysis revealed a relationship between inappropriate speech scores and lower frequencies of Tregs in ASD children. Overall, these data support the hypothesis of altered Tregs cell biology in ASD, including their lower frequencies and altered gene expression. Furthermore, altered metabolic and immune signaling processes may contribute to changes in frequencies of Tregs in ASD based on co-morbidities, ultimately driving the immune status in ASD from a balanced to a dysregulated/inflammatory state.

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28. Mottron L, McGuire OL, Gagnon D. Autism-ness Does Not Exist, but Autism Does. Part 1: A Critic of the « Spectrum » Position Used to Describe, Diagnose, and Research Autism, and Its Alternative. Autism Dev Lang Impair. 2025; 10: 23969415251404764.

This article presents the historical roots of the dimensional perspective on autism, the epistemological and clinical critics of its assumptions and effects, and offers an alternative to it. Autism is increasingly being described as the « extreme far end » of a spectrum of traits distributed continuously and heterogeneously throughout the general population, and various comorbid neurodevelopmental conditions. This dimensional perspective, initially a response to the excesses of nominalism in the DSM, creates its own heuristic and clinical dead ends. In contrast with this dimensional paradigm, clinical experts recognize and diagnose prototypical autism based on the high similarity of specific clinical signs that are present during the preschool period. We propose viewing autism as a universal and evolutionarily stable, quasi-categorical possibility of human development, offering a prototypical presentation within a certain age range. We argue that prototypical autism needs to be further clinically described and scientifically investigated before anticipating the inclusion of nonprototypical presentations in an informative « autism spectrum. » To achieve this, instruments based on qualitatively defined signs, with weighted diagnostic value, and universally associated with clinical certainty, must be developed. In the meantime, we recommend that all clinicians suspend the use of DSM-5 clinical specifiers to focus on clinical certainty and the application of differential diagnoses, rather than on the diagnostic thresholds of DSM-5 and of standardized instruments.

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29. Nimbley E, Bradley S, Pickard A, Sader M, Maloney E, Moore F, Suratwala T, Sharpe H, Duffy F, Gillespie-Smith K. Towards Identifying Autistic Adults at Risk for Eating Disorders: A Brief Report Into Clustering of Social Camouflaging and Sensory Processing Differences. Eur Eat Disord Rev. 2025.

BACKGROUND: Autistic people with an eating disorder (ED) are at higher risk of poorer treatment outcomes and experiences, perhaps due to a lack of understanding surrounding underlying mechanisms. Several factors have been implicated, such as sensory processing and social camouflaging; however, there has been little empirical investigation into how such mechanisms group or cluster together, and if certain clusters place the individual at greater risk of ED severity. METHOD: A secondary data analysis was conducted on an online survey of n = 180 Autistic adults (mean age = 38 years). Participants completed self-reported measures of sensory processing, social camouflaging and ED symptoms. Hierarchal clustering analyses (HCA) was conducted to explore clustering on sensory and social camouflaging behaviours, and a one-way ANOVA was conducted to explore between-cluster differences on ED symptoms. RESULTS: Three distinct clusters were identified: Cluster 1 (high camouflaging, low sensory); Cluster 2 (high camouflaging, high sensory); and Cluster 3 (low camouflaging, average sensory). Participants in Cluster 2 reported significantly higher ED symptoms that those in Cluster 3. There were no significant differences between remaining clusters. CONCLUSION: Findings suggest the combination of these factors may place Autistic individuals at higher ED risk, although future longitudinal, mixed-method and more representative research, which considers a wider range of risk mechanisms, is urgently needed before conclusions can be drawn.

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30. Oliveira LM, Aslam MS, Ramirez JM, Huff A. Mecp2 deficiency induces dysphagia in a preclinical model of Rett Syndrome. bioRxiv. 2025.

Rett Syndrome is a rare, x-linked genetic neurological disorder caused by MECP2 gene mutations. This progressive neurodevelopmental disorder hinders patients’ ability to breathe and eat normally. It is unclear how Mecp2- deficiency results in a high percentage of dysphagia and aspiration pneumonia in patients with Rett syndrome. We aim to determine the effects of Mecp2 -deficiency on swallow related neuromuscular mechanisms contributing to dysphagia in Rett syndrome. Swallow and breathing were detected using electrophysiology in the submental and laryngeal muscle complexes and the hypoglossal and vagus nerves. Several medullary motoneuron populations involved in swallowing were examined by immunohistochemistry in pre and post symptomatic Mecp2 -deficient male and female mice. Swallow-related submental complex duration and amplitude were significantly decreased in both Mecp2 -/y and Mecp2 +/-compared to wild-type, due to decreased motor unit activation. In both Mecp2- deficient mice, cholinergic staining in hypoglossal, facial, and trigeminal nuclei were decreased. We noted a significant increase in the transition time from inspiration to swallow, swallow to the subsequent inspiration, and impaired respiratory rhythm regeneration in Mecp2 -/y, but not Mecp2 +/- mice. Mecp2- deficiency resulted in impaired brainstem cholinergic signaling, which contribute to weakened submental muscle complex activity, and impaired swallow related laryngeal vestibular closure. These results suggest Mecp2- deficient mice are a viable pre-clinical model to further study dysphagia in Rett syndrome.

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31. Pang L, Zhao M, Ma C, Zhao X, Zhao L, Wang H, Liu C. A Multimodal Data-Driven Assessment System for Autism Spectrum Disorder in Children: Development and Pilot Validation of a Multimodal Acquisition Platform. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-5.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition affecting children’s social communication, behavioral patterns, and emotional expressions. Conventional ASD diagnoses rely heavily on subjective observational tools such as the Autism Diagnostic Observation Schedule (ADOS) and the Childhood Autism Rating Scale (CARS), whose accuracy and efficiency can be limited. In this work, we develop a portable multi-modal data acquisition platform for ASD assessment and conduct a pilot validation of its effectiveness in early screening. The system integrates EEG, ECG, speech, facial expressions, and rating-scale data via wearable devices and smart terminals, employing an algorithmic framework for data fusion and analysis to generate individualized diagnostic reports. In a pilot study with seven participants, the platform identified significant feature differences between the ASD and control groups, including shorter speech pause duration (41.8% reduction, p < 0.001), higher EEG δ-band power (226% increase, p = 0.0015), and lower approximate entropy (38.5% reduction, p < 0.0001). These pilot findings support the effectiveness of multi-modal data fusion. Compared to existing diagnostic tools, this system has notable advantages in low cost, high accuracy, and user-friendliness, indicating strong potential for widespread application in family-based ASD screening and early intervention. With further data expansion and technical optimization, this multi-modal system holds broad clinical prospects.Clinical Relevance- The multi-modal ASD assessment system offers clinicians a low-cost and efficient tool for early screening and diagnosis of ASD. By integrating objective physiological and behavioral measurements, it helps practitioners develop individualized intervention strategies and improve diagnostic accuracy and treatment outcomes, thus showing great promise for clinical applications.

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32. Peltekidi A, Jotautis V, Tzitiridou-Chatzopoulou M, Georgakopoulou VE, Sousamli A, Diamanti A, Vivilaki V, Orovou E, Sarantaki A. Maternal Smoking During Pregnancy and Risk of Autism Spectrum Disorder in Offspring: A Systematic Review and Meta-Analysis. J Clin Med. 2025; 14(23).

Background/Objectives: Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by persistent social-communication deficits and repetitive behaviors. While genetic factors play a major role, prenatal environmental exposures may also contribute. Maternal smoking during pregnancy is a known risk factor for adverse perinatal outcomes, but its association with ASD remains unclear. Methods: We conducted a systematic review and meta-analysis following PRISMA 2020 guidelines. A comprehensive literature search was performed in PubMed, Embase, Web of Science, Scopus, PsycINFO, and Google Scholar up to September 2025. Eligible observational studies evaluated maternal active smoking during pregnancy and ASD diagnosis in offspring. Effect estimates were pooled using a random-effects model and expressed as relative risks (RR) with 95% confidence intervals (CI). Heterogeneity was quantified using I(2), with subgroup and sensitivity analyses performed. Results: Twenty-one studies including several million mother-child pairs met the inclusion criteria. The pooled RR for ASD associated with maternal smoking was 1.01 (95% CI: 0.95-1.08), indicating no significant association. Subgroup and sensitivity analyses confirmed the robustness of the findings, with no evidence of publication bias. Conclusions: Maternal smoking during pregnancy does not appear to increase ASD risk in offspring. Nevertheless, smoking cessation remains critical due to established adverse fetal effects.

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33. Ranaweera K, Tran M, Nguyen BA, Ngo T, Pathirana PN, Milne SC, Horne M, Delatycki MB, Corben LA. Objective Assessment of Friedreich Ataxia in Children: Accounting for Developmental Deficits. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-7.

The immature nervous system of children creates challenges for assessing the severity of Friedreich’s Ataxia (FRDA). Erratic movements and postural sway from a maturing nervous system are difficult to disentangle from the impact of FRDA yet both are reflected in clinical rating score. To address this issue, we propose a novel correction framework for scores of severity derived from regression models trained on Inertial Measurement Unit (IMU) data. These are collected with three specialized devices: a cup-shaped IMU (Ataxia Instrumented Measure (AIM)-C), a spoon-shaped IMU (AIM-S), and a pendant-shaped IMU (AIM-P). A tailored algorithm applied to the data from each device is designed to isolate and remove developmental effects: a reinforcement learning-based technique for AIM-C, Bayesian optimization for AIM-S, and a multi-layer perceptron framework for AIM-P. Using these correction methods, ataxia severity scores of the movement deficit related to FRDA alone were produced, which were free from development related confounders. These scores provide greater precision for clinicians when assessing FRDA progression in children. This work demonstrates the capacity of advanced signal processing and machine learning techniques to enhance the clinical utility of severity assessments in paediatric FRDA participants.Clinical relevance- By accounting for developmental effects, clinicians can more accurately measure the ataxia in Friedreich ataxia and more reliably track disease progression and evaluate treatment efficacy.

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34. Ratnaik R, P SK, Ronickom JFA. Automated Autism Spectrum Disorder Diagnosis using Graph Metrics from Diffusion Tensor Imaging and Machine Learning. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-6.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with an increasing global prevalence, yet its diagnosis remains challenging due to the absence of objective biomarkers and reliance on subjective behavioral assessments. This study aims to bridge this gap by integrating advanced neuroimaging techniques, graph theory, and machine learning algorithms to develop a diagnostic classification model for ASD. Initially, diffusion tensor imaging (DTI) data from individuals with ASD and typically developing (TD) participants were obtained from the Autism Brain Imaging Data Exchange-II (ABIDE-II) database. The data were preprocessed, followed by the extraction of DTI-derived parameters from various white matter regions of the brain. A structural correlation matrix was constructed using a Pearson correlation method. Further, graphs were generated from the matrix to model brain organization by representing regions as nodes and their structural correlations as edges. We computed six graph metrics, including betweenness centrality, closeness centrality, clustering coefficient, degree centrality, participation coefficient, and strength from the graph network, which leads to a total of 300 features per individual. Finally, we built the diagnostic classification models using logistic regression and support vector machines (SVM) and the performance of the models were evaluated. Our results revealed that SVM produced the highest classification accuracy of 82.34% with 225 graph-theoretical features. The top three distinguishing features for ASD classification were strength of the cingulum left, closeness centrality of the anterior corona radiata left, and betweenness centrality of the genu of the corpus callosum. Our approach provides insights into ASD-related alterations in brain structural networks and contributes toward the development of objective diagnostic tools.Clinical Relevance-This study highlights the potential of DTI-based graph-theoretical metrics combined with machine learning classifiers to differentiate ASD from TD participants.

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35. Sadeh H, Razi T, Arbel R, Netzer D, Meiri G. Medical Cannabis Use in Autism: Insights from an Israeli HMO on Patient Characteristics and Alignment with National Guidelines. J Child Adolesc Psychopharmacol. 2025.

Objective: Evidence for medical cannabis use and effectiveness in autism has begun to accumulate but remains limited, even as clinical interest has rapidly increased. In Israel, medical cannabis may be prescribed for autism with severe behavioral disturbances under strict Ministry of Health criteria requiring prior trials of two Food and Drug Administration (FDA)-approved antipsychotics. Using a large real-world dataset, this study aimed to characterize autistic individuals prescribed medical cannabis and evaluate adherence to national guidelines. Methods: A retrospective cohort study was conducted using electronic medical records from Clalit Health Services, Israel’s largest Health Maintenance Organization. All individuals with a documented autism diagnosis between 1990 and 2025 were identified (N = 36,610) and classified as cannabis-prescribed (N = 462) or not-prescribed (N = 36,148). Demographic and clinical characteristics were compared, including prior use of FDA-approved antipsychotics. Results: Only 1.2% of individuals with autism were prescribed medical cannabis. Of these, 4.3% of prescriptions were issued for children under 5 years of age. The cannabis-prescribed group was diagnosed earlier (median 3 vs. 5 years, p < 0.001) and had higher rates of ADHD (42% vs. 30%), intellectual disability (12% vs. 5%), and epilepsy (14% vs. 6%) (all p < 0.001). While 69% had used at least one FDA-approved antipsychotic medication prior to cannabis initiation, only 28% had documented trials of both, as required by national guidelines. Marked sociodemographic disparities were also observed: the cannabis-prescribed group had a higher socioeconomic status (median SES 7 vs. 6, p < 0.001) and lower representation of Arab individuals (2.7% vs. 11%, p < 0.001). Conclusions: Medical cannabis use among autistic individuals was rare and mainly observed among those with more complex clinical profiles and higher socioeconomic backgrounds. Most prescriptions did not fully comply with guidelines requiring prior antipsychotic trials. These findings underscore the need for enhanced regulatory oversight, equitable access, and longitudinal research to evaluate real-world outcomes and guide evidence-based clinical practice.

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36. Shield A. Attention-regulation strategies used by Deaf parents of deaf autistic children: A pilot study. Enfance. 2025; 2025(3): 353-66.

Children with autism often struggle with the establishment of joint attention, a skill strongly linked to language outcomes for both typically-developing and autistic children. Increasing the amount of time spent in episodes of joint attention between parents and their autistic children is a worthy goal that could improve eventual language outcomes. This paper investigates a novel research population: Deaf parents of deaf autistic children. We look to Deaf parents because of previous work documenting numerous advantages that Deaf parents have over hearing parents in their ability to gain and maintain their children’s visual attention. Using a semi-structured play-based protocol, we developed an exploratory coding scheme for defining an inventory of attention-getting strategies deployed by Deaf parents while interacting with their autistic children. We describe these strategies, including several that may be unique to Deaf parents. While much remains unknown, including the key question of which strategies are most effective in leading to increases in joint attention, this study represents a first step towards that important goal.

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37. Shu Y, Yang J, Zhang P, Zhang X, Zhao X, Yan J, Zhang B, Du J. Selenium improves behavioral performance in a rat model of autism spectrum disorder by mitigating oxidative stress and modulating the Sirt1/Keap1/Nrf2/HO-1 signaling pathway. Int Immunopharmacol. 2025; 166: 115556.

Autism spectrum disorder (ASD) is a diverse collection of neurodevelopmental disorders often accompanied by excessive oxidative stress and chronic inflammatory responses. Selenium (Se), a trace element, has demonstrated anti-inflammatory, antioxidant, and neuroprotective effects. The present study aimed to examine the effects of sodium selenite, a Se supplement, on a rat model of ASD. The valproic acid intervention method was used during pregnancy to construct an ASD rat model. Rats were then treated with sodium selenite. Behavioral tests, morphological assessments, and measurements of antioxidant enzymes, oxidative stress indicators, and inflammatory factors in the hippocampal tissues and serum were conducted. Se supplementation mitigated inflammatory responses and oxidative stress in ASD rats while preserving neuronal morphology and function. In addition to Se supplementation, rats received specific inhibitors targeting the signaling pathway. Protein and mRNA expression levels, as well as the tissue distribution of sirtuin 1 (Sirt1), heme oxygenase-1 (HO-1), nuclear factor erythroid 2-related factor 2 (Nrf2), and Kelch-like ECH-associated protein 1 (Keap1), were evaluated. The results demonstrated that Se treatment upregulated the expression levels of Sirt1, Nrf2, and HO-1, while downregulating Keap1 expression. These findings demonstrate that Se attenuates inflammatory damage and oxidative stress in the brain, and this protective effect is associated with the Sirt1/Keap1/Nrf2/HO-1 signaling pathway.

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38. Tafolla M, Lord C. Expanding the autism evidence base: Strategies to increase participant representation. Autism. 2025: 13623613251393505.

There is a lack of representation of racially diverse individuals who are multilingual from low-income households in autism research. This calls into question the generalizability of research findings derived from predominantly White, English-speaking samples. In this article, we bring forth an important argument about why we as an autism field should work to expand representation in research samples. We also discuss strategies that can be used to work toward this goal. We detail the recruitment and retention of 94 Spanish-English bilingual Latinx (primarily Mexican and Central American) families from low-income households across a large urban city and its surrounding communities in the United States for an assessment validation study. We use the method of this study as an example of how to engage and include underrepresented populations in autism research, describing the efforts that were implemented to engage families and community-based organizations serving this population. We conclude the report by summarizing culturally sensitive strategies researchers can use to engage populations of different races and ethnicities from low-income households in their own research studies, in hopes of increasing representation in the autism science field and ensuring that research findings are applicable across populations, including those who have been historically underrepresented.Lay AbstractIndividuals of different races and identities from low-income households and their families are not adequately represented in research. This makes it difficult to know whether autism research findings apply to traditionally underserved individuals of color, since participants included in studies are usually White and speak English. We use our own study, where we successfully recruited 94 Spanish-English bilingual participants who are from Mexico and Central America but are currently living in the United States in low-income households for an assessment study, as an example to describe the strategies that were helpful to recruit participants with these sociodemographic characteristics. We end the article by discussing strategies that are culturally appropriate for researchers to consider when working with autistic populations of color who are from predominantly low-income households and their families.

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39. Taylor BJ, Pedersen KA, Riddell ER, Andalib Y, Tory B, Siegel M. Beyond challenging behaviors, sleep maintenance problems in autistic youth at the time of hospitalization are associated with increased caregiver strain. Sleep Med. 2025; 138: 108695.

PURPOSE: Autistic children are at increased risk for psychiatric hospitalization for challenging behaviors. Sleep problems may increase this risk due to the added strain they place on caregivers and their ability to maintain safety at home. We evaluate if caregiver-reported sleep problems in autistic children at the time of the child’s hospital admission predict caregiver reported stress and self-efficacy, above and beyond the severity of the child’s challenging behaviors. METHODS: Participants included 598 autistic children (ages 4-20) admitted to specialized psychiatric inpatient units and a primary caregiver. Caregivers reported if their child had sleep initiation and/or sleep maintenance problems. Caregivers completed the Parenting Stress Index-Short Form and Difficult Behavior Self-Efficacy Scale. Hierarchical linear regression models examined associations between sleep problems and caregiver outcomes, controlling for child irritability, and both household- and child-level characteristics. RESULTS: Fifty-nine percent of children had a caregiver-reported sleep problem. Sleep maintenance problems were significantly associated with increased parental distress, more dysfunctional parent-child interactions, and greater perception of the child as difficult to manage, above and beyond challenging behavior severity and other covariates. Sleep initiation problems were unrelated to caregiver outcomes. Self-efficacy in single caregivers and those in lower-income households was more negatively impacted by sleep problems than other caregivers. CONCLUSIONS: Sleep maintenance problems may independently contribute to caregiver strain above and beyond challenging behaviors. Social and financial resources may buffer against the negative effect of sleep problems. Preventative interventions targeting sleep may provide needed support for vulnerable families.

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40. Tien I, Wolpe S, Huang Y, Sozeri S, Lee M, Jeong M. How Diagnostically Accurate are #Autism Portrayals? A Latent Space Item Response Modeling Approach. J Autism Dev Disord. 2025.

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41. Ullah MZ, Yu D. EEG Insights into Visual Attention and Affective Prosody Processing in Children with Autism Spectrum Disorder: Examining the Theta/Alpha Ratio. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-7.

Attention is a fundamental cognitive process, and individuals with Autism Spectrum Disorder (ASD) often experience challenges in both cognitive and emotional regulation. Although multiple object tracking (MOT) tasks are frequently used to assess attention impairments in ASD, most studies focus solely on behavioral responses and overlook physiological measures such as electroencephalogram (EEG). Moreover, the interaction between cognition and emotion during MOT tasks remains underexplored. This study addresses these gaps by examining cognitive load variations using the Theta/Alpha Ratio (TAR) in 31 ASD and 31 typically developing (TD) children, based on publicly available EEG data. Participants completed three tasks: neutral image viewing (IPAS), one-target 4-disc (MOT4), and one-target 8-disc (MOT8), representing low, intermediate, and high attentional loads, respectively, while listening to happiness and sadness prosodies. For each participant, TAR was calculated, and five EEG features were extracted: averaged TAR across all channels (allTAR) and TAR values from the frontal (fTAR), temporal (tTAR), parietal (pTAR), and central (cTAR) regions. A three-factor ANOVA (emotion types × attention levels × subject groups) was conducted, followed by post-hoc t-tests for multiple comparisons, and correlation analyses were performed between the five EEG features and clinical measures. Results revealed: (1) a significant interaction between attention level and emotional prosody for tTAR; (2) significant main effects of attention level in fTAR and cTAR, but no interaction with emotional prosody; (3) higher allTAR, fTAR, tTAR, cTAR, and pTAR in the ASD group compared to TD; and (4) stronger EEG clinical correlations in the ASD group during MOT-4. These findings confirm visual attention deficits and highlight the interplay between emotion and cognition in ASD, suggesting TAR as a potential biomarker for cognitive load and emotional modulation.

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42. Vicioso C, Alon N, Adler MJ, Schlachte B, Kolevzon A, Ranade SC. A Medical Student’s Reflections on Autism, Movement, and Orthopaedic Collaboration: Beyond the Clinic. J Bone Joint Surg Am. 2025; 107(23): 2605-7.

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43. Wills CD. Rehabilitating Youth in Juvenile Corrections. J Am Acad Psychiatry Law. 2025; 53(4): 373-6.

Juvenile correctional programs that focus solely on safety, education, and structure yield suboptimal outcomes. Youth in these facilities often have learning disorders, and adaptive challenges, have been exposed to severe trauma, and have mental disorders, including autism spectrum disorder. Consequently, rehabilitation programs must be comprehensive, individualized, developmentally informed, and trauma-informed to achieve better outcomes. It is imperative that staff receive training to identify, intervene in, and report specific behaviors. This approach broadens staff skill sets, addresses the rehabilitation needs of a larger group of youth, generates data that facilitate accurate diagnoses and treatment planning, and enhances the likelihood of equitable rehabilitation for all youth.

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44. Zhang T, Zhang S, Jiang Y. Automatic pupillary responses to pain perception in adults and children: The influence of race and autistic traits. Cognition. 2025; 268: 106384.

The ability to understand and share others’ emotional states (e.g., feeling of pain) plays a fundamental role in survival and prosocial behavior. The current study utilized pupillometry to assess automatic psychophysiological responses to others’ painful facial expressions in both adults and children (N = 72). Results revealed that pupil size significantly increased when perceiving painful versus neutral expressions, independent of low-level visual features. Notably, both adults and children exhibited a racial in-group bias, with pupil dilation effects observed only for same-race painful faces. Furthermore, individuals’ Autism Spectrum Quotient scores were negatively correlated with pupil dilation effects toward painful expressions of same-race faces. These findings suggest that pupillary responses might reflect automatic empathic arousal to others’ pain and are modulated by racial group membership and autistic traits, providing a potential physiological indicator, at least at the group level, for probing affective resonance in children or individuals with socio-cognitive disorders (e.g., autism spectrum disorder).

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45. Zhang Y, Liang C, Adali T, Ji Y, Jiang R, Zhang D, Calhoun VD, Qi S. Site Common Information-Guided Site-to-Individual-to-Global Multi-View GCN for Psychiatric Diagnosis. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-5.

Magnetic resonance neuroimaging (MRI)-based brain functional connections show significant potential in aiding the diagnosis and assessment of psychiatric disorders. Current neuroimaging studies utilize substantial amounts of data, frequently from multiple centers, to enhance the confidence of the results. However, multi-center neuroimaging studies face challenges due to inter-site variability caused by differences in scanner configurations and acquisition protocols, leading to low data consistency with limited generality. To address these challenges, we propose a novel site common information guided Site-to-Individual-to-Global Multi-View graph convolutional network GCN (sigGCN) framework. SigGCN extracts the shared site common information to mitigate site-effects, constructs individual multi-view representations, and integrates individual and site-common features to generate global Chebyshev networks. Results show that sigGCN achieves the highest accuracy in diagnosing autism comparing with other exiting models. Moreover, sigGCN successfully classifies autism/schizophrenia from controls across 5 brain atlases with 3 independent datasets, demonstrating its robustness and stability. By systematically leveraging the hierarchical information among sites, individuals, and global networks, sigGCN offers a reliable new way to harmonize multi-center data to improve the performance of neuroimaging based psychiatric diagnosis.Clinical Relevance-The proposed sigGCN establishes a site-to-individual-to-global framework to effectively and reliably harmonize multi-center data and to improve the performance of neuroimaging based psychiatric diagnosis.

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46. Zhao J, Bao M, Li H, Wang M, Yao L, Wang Y. Resting-State Heartbeat-Evoked Potentials in Children With ADHD and its Comorbidity With ASD: A Pilot Study. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-4.

The interaction between the heart and brain is crucial for the normal functioning of the human body, and heartbeat-evoked potentials (HEPs) provide a way to reflect this heart-brain interaction. Individuals with attention-deficit/hyperactivity disorder (ADHD) have been reported to exhibit abnormalities in heart rate variability, suggesting that cardiac autonomic dysfunction may affect the afferent signals reaching the brain. To the best of our knowledge, the study of HEPs in children with ADHD has not been fully explored. In this pilot study, we recruited eight children diagnosed with ADHD and seven children with comorbid ADHD and autism spectrum disorder (ADHD+ASD). Electrocardiogram (ECG) and electroencephalogram (EEG) data were collected during resting state conditions to investigate potential HEP abnormalities in both groups. Peak and mean amplitudes of HEP curves were extracted as features. Our results revealed that the HEPs of the ADHD+ASD group exhibited higher amplitudes than those of the ADHD-only group, particularly in the time windows around 200 ms and 400 ms after the R wave. The parietal-occipital regions may serve as key areas for further investigation. This study offers a novel perspective for understanding the neural mechanisms and interoceptive states in ADHD and ASD, with potential implications for diagnostic approaches.

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47. Zoromba MA, El-Gazar HE, Mohammed HH, Shaban M. Beyond the Monitor: Integrating Family Expertise Into ICU Care for Patients With Autism Spectrum Disorder. Nurs Crit Care. 2026; 31(1): e70261.

BACKGROUND: Autistic individuals admitted to intensive care units (ICUs) face unique challenges due to sensory sensitivities, communication barriers and behavioural complexities. In such high-acuity environments, families play a critical role in advocating for their loved ones’ needs, yet their experiences remain underexplored-particularly in Middle Eastern contexts, where caregiving is deeply embedded in cultural and spiritual responsibilities. AIM: To explore the lived experiences of family caregivers supporting autistic relatives during ICU admissions, with particular attention to communication challenges, emotional burdens, cultural values and ethical considerations. STUDY DESIGN: This qualitative phenomenological study was conducted at a hospital in Saudi Arabia. In-depth, semi-structured interviews were carried out with family caregivers of autistic ICU patients. Thematic analysis was performed using Braun and Clarke’s six-phase framework, and the study followed the Standards for Reporting Qualitative Research (SRQR) guidelines to ensure rigour and transparency. FINDINGS: In total, 14 caregivers were interviewed. Three major themes were identified: (1) Advocacy Amid Uncertainty, reflecting the emotional strain, communication barriers and isolation experienced by caregivers advocating in high-stress environments; (2) Negotiating Care Roles, capturing how families collaboratively distributed advocacy tasks while navigating generational and interpersonal tensions; and (3) Cultural and Ethical Framing, illustrating how deeply held values, religious beliefs and moral obligations shaped families’ interpretations and decisions. CONCLUSIONS: Families of autistic ICU patients navigate multifaceted roles as advocates, interpreters and emotional supports within a culturally complex landscape. Their experiences reflect a need for more inclusive, autism-informed and family-centred critical care practices that respect both neurodiversity and cultural context. RELEVANCE TO CLINICAL PRACTICE: ICU nurses and interdisciplinary teams can improve care for autistic patients by engaging families as expert partners, adapting communication strategies and supporting culturally and spiritually grounded advocacy. Integrating family-informed care planning, reducing sensory overstimulation and honouring faith-based practices can enhance trust, emotional stability and care outcomes in neurodiverse ICU populations.

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48. Zuo S, Li Y, Wen J, Chen X, Liu A. Adaptive Hypergraph Contrastive Learning for ASD Classification Using fMRI Connectome. Annu Int Conf IEEE Eng Med Biol Soc. 2025; 2025: 1-7.

Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental condition with numerous symptoms, making accurate diagnosis and the identification of reliable biomarkers particularly challenging. Recent advances in deep neural networks using connectivity features derived from resting-state functional magnetic resonance imaging have greatly extended our understanding of ASD and improved its diagnostic accuracy. However, most existing methods primarily focus on pairwise connections, limiting their ability to capture higher-order interactions in brain networks and resulting in suboptimal predictive performance. In this paper, to enhance the learning of higher-order relationships and improve model interpretability, we propose an Adaptive Hypergraph Contrastive Learning (AHCL) framework for ASD classification. Specifically, AHCL employs a trainable masking mechanism to adaptively estimate latent hyperedges, allowing the generation of two hypergraph views with distinct topological structures. Additionally, AHCL incorporates low-rank loss to improve the compactness of intra-class samples, effectively addressing the limitation of traditional contrastive learning in distinguishing negative samples. By jointly optimizing view similarity loss and contrastive loss, the framework ensures semantic consistency across views while enhancing topological differences, leading to robust and noise-resistant feature representations with minimal information redundancy. Experimental results demonstrate that AHCL outperforms competing methods in ASD classification. Furthermore, it identifies disease-related connections and regions, providing valuable insights into ASD and offering potential techniques for more precise and interpretable diagnostic strategies.

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