1. Anderer S. Autism Incidence Has Remained Stable, Study Finds. Jama. 2026.

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2. Brys I, van Esch L, Erdogan M, Mađarević M, Moerman F, Roeyers H, Segers J, Zink I, Warreyn P, Noens I. Prediction of language abilities in children at elevated likelihood of autism: The role of self-reported and observed parenting behaviours. Infant Behav Dev. 2026; 85: 102252.

Parenting behaviours are thought to play an important role in children’s language development. Yet, this relationship is understudied in children at elevated likelihood (EL) of autism, whose language abilities are highly variable. Moreover, few studies examine whether associations differ between EL-children with and without autism, and most rely exclusively on observational measures despite the added value of self-report questionnaires. This study aimed to 1) examine the predictive value of self-reported and observed positive and negative parenting behaviours for children’s receptive and expressive language development, 2) explore whether these associations differ across outcome groups (i.e., no autism, broad autism phenotype, autism), and 3) investigate group differences in parenting behaviours and language outcomes. Participants were children at EL of autism, either siblings of an autistic child (n = 68) or children born preterm (n = 41), and their mothers. Parenting behaviours were assessed at 24 months, and language abilities and outcome diagnosis were determined at 36 months. Neither self-reported nor observed positive or negative parenting behaviours predicted language outcomes, whereas early language abilities did. Relationships did not differ significantly across outcome groups. No group differences were found in parenting behaviours, but children in the autism group showed slightly lower expressive language outcomes compared to children in the no autism group. These findings highlight the need for further research to identify which factors predict language outcomes for whom, and under which circumstances. In the meantime, they underscore the importance of early identification of language difficulties and incorporating outcome diagnosis into research on EL-children.

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3. Cleary M, West S, Kornhaber R. Performing Arts and Autism: Intervention, Inclusion and Improving Mental Health. Issues Ment Health Nurs. 2026: 1-3.

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4. Gao M, Gao Y, Xu C, Huang Z, Jin T, Shen Z, Chen D. Autism spectrum disorder burden in China and worldwide, 1990-2021: A comparative ecological time-series study using global burden of disease 2021 data. Medicine (Baltimore). 2026; 105(40): e50964.

Autism spectrum disorder (ASD) imposes a substantial public health burden. This study quantified and compared the ASD burden in China and globally from 1990 to 2021 to inform policy and planning. We analyzed Global Burden of Disease 2021 estimates of age-standardized prevalence rates (ASPR) and age-standardized disability-adjusted life-year rates (ASDR) in China and worldwide, with analyses by sex and age. Joinpoint regression characterized historical trends. Autoregressive integrated moving average (ARIMA) models generated exploratory projections for 2022 to 2031; no temporal holdout validation or comparison with alternative forecasting approaches was performed. In 2021, ASPR per 100,000 population was 655.75 (95% uncertainty interval [UI] 545.06-780.37) in China and 788.34 (95% UI 663.76-927.21) globally; ASDR was 124.19 (95% UI 83.70-175.10) and 147.56 (95% UI 100.21-208.16), respectively. From 1990 to 2021, the average annual percent changes in ASPR and ASDR were 0.22% (95% confidence interval [CI] 0.21-0.24) and 0.23% (95% CI 0.21-0.25) in China, compared with 0.06% (95% CI 0.06-0.07) and 0.07% (95% CI 0.06-0.08) globally. The estimated burden was higher in males. The exploratory ARIMA point projections suggested a modest decline in ASPR in both settings and in global ASDR but an increase in China’s ASDR. These directions have not been validated out of sample. Historical estimates indicate an increasing ASD burden in China and worldwide. The ARIMA results represent model-based scenarios, not validated future trajectories. The existing burden supports sustained attention to early identification, long-term care, and family support; resource planning should not rely on these projections alone.

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5. Gibula-Tarlowska E, Grochecki P, Lubinska J, Sliwa M, Smaga I, Marszalek-Grabska M, Lubec G, Slowik T, Slotwinska W, Kotlinski R, Listos J, Kedzierska E, Filip M, Kotlinska JH. Effects of early postnatal dopamine transporter (DAT) inhibition on social and cognitive behavior in a rat model of autism. Behav Brain Res. 2026; 514: 116400.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social behavior and cognition. Increasing evidence suggests that alterations in dopaminergic neurotransmission, including changes in dopamine transporter (DAT) function, may contribute to ASD-related behavioral abnormalities. This study investigated whether selective DAT inhibition during early postnatal development influences behavioral and molecular alterations in a rat model of ASD induced by prenatal exposure to sodium valproate (NaVP). Pregnant Wistar rats received NaVP (600 mg/kg, i.p.) on gestational day 12.5. Male offspring were treated with the selective DAT inhibitor CE-123 (10 mg/kg, i.p.) once daily from postnatal day (PND) 10-23. Behavioral assessments during adolescence (PND25-42) evaluated social interaction, recognition memory, spatial preference, anxiety-like behavior, locomotor activity, and aversive memory. DAT, dopamine D2 receptor, brain-derived neurotrophic factor (BDNF), and interleukin-1β (IL-1β) protein expression were assessed in selected brain regions. Prenatal NaVP exposure impaired social novelty discrimination, declarative, spatial, and aversive memory, reduced locomotor activity, and increased anxiety-like behavior. These behavioral alterations were accompanied by elevated DAT and dopamine D2 receptor expression. Early postnatal CE-123 treatment attenuated several ASD-like behavioral abnormalities, normalized DAT and dopamine D2 receptor expression, increased BDNF levels in the prefrontal cortex (PFC), and increased IL-1β expression, without inducing non-specific locomotor stimulation. Together, these findings demonstrate that early postnatal CE-123 treatment was associated with long-lasting behavioral improvements accompanied by alterations in dopaminergic markers and BDNF expression. Although these findings support a role for dopaminergic regulation and neuroplasticity in the observed behavioral effects, the underlying mechanisms require further investigation.

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6. Karalar Pekuz OK, Teke Kisa P, Yildiz S, Kocabey M, Zubarioglu T, Arslan Gulten Z, Sezen ES, Er E, Pekuz S, Kadioglu Yilmaz B, Kirmizitas M, Ister MB, Kulu B, Hazan F, Kilic M, Unal Uzun O, Kiykim E, Aydin HI, Erdol S, Aktuglu Zeybek AC, Arslan N. Clinical spectrum and genetic landscape of MTHFR deficiency: a cohort study including novel variants. Neurol Sci. 2026; 47(10).

Methylene tetrahydrofolate reductase (MTHFR) deficiency is a rare inherited metabolic disorder that impairs myelination and brain development, leading to primary clinical manifestations, particularly neurological deficits. The aim of this study was to comprehensively describe the clinical presentation, biochemical profile, and molecular spectrum of patients with MTHFR deficiency. This multicenter, retrospective, descriptive study evaluated the medical records of 19 patients diagnosed with MTHFR deficiency at ten metabolic disease centers. A total of 13 patients had early-onset disease. In the early-onset group, all patients had varying degrees of neurodevelopmental delay. The median diagnostic delay from the first symptom to diagnosis was 3 months in early-onset cases and 118 months in late-onset cases. MTHFR deficiency exhibits a broad clinical spectrum, with acute deterioration possible even in chronically affected patients. MTHFR deficiency should be considered among treatable neurometabolic disorders in patients with otherwise unexplained neurodevelopmental or neuropsychiatric manifestations, particularly when accompanied by additional neurological findings.

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7. Koziel N. ADHD, autism or complex trauma? The complicated nature of the question. Br J Psychiatry. 2026: 1-2.

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8. Li Y, Wu J, Liu N, Li X, Zhou T, Kang J. Disentangling oscillatory and aperiodic neural activity in autism: A spectral parameterization analysis of neurofeedback intervention. Behav Brain Res. 2026; 514: 116396.

BACKGROUND: Autism Spectrum Disorder (ASD) is characterized by atypical neural oscillations and heterogeneous alterations in excitation/inhibition (E/I) balance, the directionality of which varies across individuals, neural circuits, and developmental stages. While Alpha-band neurofeedback (NFB) is a promising intervention, its underlying neurophysiological mechanisms remain unclear, partly due to the conflation of periodic and aperiodic signals in traditional EEG analysis. METHODS: This randomized controlled trial recruited 40 children with ASD, assigned to either an experimental group (Alpha-training NFB) or a no-feedback group. Resting-state EEG and behavioral assessments (SRS, ABC) were collected pre- and post-intervention. We employed spectral parameterization to decompose neural activity into aperiodic (1/f slope, offset) and periodic (periodic alpha power, center frequency) components. RESULTS: NFB training yielded significant behavioral improvements in social cognition and relating skills. Physiologically, the experimental group exhibited a significant steepening of the aperiodic slope (increased exponent), reflecting a reduction in neural noise and potential optimization of inhibitory modulation. Furthermore, we observed enhanced periodic alpha power and an acceleration of the alpha center frequency (ACF), indicative of improved neural efficiency and maturation. These physiological shifts in frontal and occipital regions were significantly correlated with improvements in behavioral scores. CONCLUSION: Alpha-training NFB was associated with improvements in caregiver-rated behavioral scores and modulated spectral features of resting-state EEG in children with ASD. These findings validate the utility of spectral parameterization markers in evaluating neuromodulatory interventions.

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9. Liu D, Gao X, Yu S, Chen C, Zhang F, Liu J, Wang R. Oral health status among preschool children with autism in Changchun, Jilin Province, China: A cross-sectional study. Medicine (Baltimore). 2026; 105(40): e51012.

This study aimed to compare oral health status, oral-related behaviors, and caregivers’ oral-health knowledge and beliefs between preschool children with autism spectrum disorder (ASD) and typically developing (TD) children. A cross-sectional study was conducted among 694 preschoolers (344 ASD, 350 TD) aged 3-6 in Changchun. Clinical oral examinations were conducted to record caries prevalence and the number of decayed, missing (due to caries), and filled teeth (dmft) index. Caregivers completed a three-part questionnaire covering demographic information, children’s oral hygiene practices and dental history, as well as caregivers’ oral-health knowledge and beliefs. Statistical analyses included univariate comparisons, multivariable logistic and negative binomial regression models adjusted for age, sex and residence. ASD children exhibited significantly lower caries prevalence and dmft values than TD children Significant intergroup differences were also observed in oral hygiene habits, snacking frequency, and dental attendance behaviors. Caregivers of TD children showed better oral health knowledge and attitudes, while parental educational level and household income were significantly lower in the ASD group. After adjusting for baseline confounding factors, ASD status remained independently associated with lower caries risk and lower dmft indices. Further sensitivity analyses excluding hospital-recruited TD children and adopting age- and sex-matched sampling confirmed the robustness of these findings. Although ASD children presented lower caries experience, they demonstrated suboptimal oral-hygiene-related behaviors and less frequent dental visits than TD children. Caregivers’ knowledge and attitudes were critical factors associated with children’s oral health. Considering baseline socioeconomic imbalances and partial hospital-based recruitment of TD controls in this cross-sectional study, these findings should be interpreted cautiously, and definitive causal relationships cannot be established.

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10. Onyango C, Wanjiku LN, Oketch C. Machine Learning Models for Early Autism Spectrum Disorder Detection: Systematic Review With a Pediatric and Engineering Perspective. Cureus. 2026; 18(9): e115572.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition for which earlier identification may facilitate timely developmental assessment and intervention. This systematic review synthesized evidence on machine learning (ML)/ deep learning (DL) approaches for early ASD detection, with particular attention to pediatric populations, data modalities, model architectures, reported diagnostic performance, validation strategies, methodological quality, and clinical and engineering translation. PubMed and IEEE Xplore were searched through July 29, 2026. The supplied database exports contained 794 PubMed records and 455 IEEE Xplore records. After removal of seven records published after the prespecified cutoff, 1242 records remained. Thirty-eight duplicates were removed, leaving 1204 unique records for screening. Following title/abstract screening, 314 reports underwent detailed record-level eligibility assessment, of which 237 were classified as record-level eligible on the basis of available titles, abstracts, bibliographic metadata, and accessible record information. The identified evidence encompassed questionnaire and clinical-information models, home-video and behavioral analysis, speech and acoustic features, eye tracking, EEG/MEG, MRI/fMRI, wearable sensors, and multimodal approaches. Reported diagnostic performance varied substantially across populations, modalities, algorithms, and validation settings. Some retrospective studies reported very high classification accuracy, whereas independent and cross-cultural validation generally produced more variable performance. For example, an ML model based on medical and background information reported an AUROC of 0.895 during development and 0.790 in independent validation, while a real-world evaluation of an AI-based diagnostic system reported sensitivity of 99.1% and specificity of 81.6% among determinate outputs. Because the available evidence was heterogeneous and full-text reports were not available for all candidate studies, quantitative pooling and definitive study-level risk-of-bias assessment were not considered defensible. PROBAST+AI domains were therefore assessed only when sufficient methodological information was available, with otherwise unclear or not-assessable judgments. Across the evidence base, external validation, prevention of data leakage, dataset shift, explainability, privacy, fairness, reproducibility, computational requirements, and clinical workflow integration remain major barriers to translation. ML and DL approaches show promise as adjunctive screening and risk-classification technologies, but the available evidence does not support replacing specialist ASD assessment with autonomous AI diagnosis. Future research should prioritize prospective multicenter validation, transparent reporting, independent testing, clinically meaningful explainability, privacy-preserving methods, health-equity evaluation, calibration, and deployment within real-world pediatric clinical workflows.

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11. Osborn EJ, Young RL, Weber N, Fuss B. Autism and Homelessness: Risk and Protective Factors in a Bayesian Analysis of Autistic Characteristics. Autism. 2026: 13623613261486061.

Autistic individuals are overrepresented in homeless populations, yet mechanisms driving this association remain unclear, particularly in the context of diagnostic access and structural inequities. We surveyed 333 adults in Australia, assessing autistic traits (Autism-Spectrum Quotient [AQ]-50), homelessness risk factors, adaptive functioning and social support. Bayesian hierarchical logistic regression models were used to estimate associations. Associations between autistic traits and homelessness probability differed according to diagnostic status. Among participants without a formal diagnosis, higher AQ-50 scores were associated with greater estimated homelessness probability, although uncertainty remained; this association was attenuated among formally diagnosed participants. Structural risk factors were credibly associated with higher homelessness probability, whereas mental health-related risk factors showed a positive posterior estimate with greater uncertainty. Interaction effects involving socioeconomic status and social support suggested that associations between autistic traits and homelessness varied according to broader social context. Bayesian mediation analyses explored adaptive functioning as a potential pathway linking autistic traits and homelessness; however, evidence for an indirect effect remained uncertain. Findings highlight the importance of diagnostic accessibility, social support and structural accommodation for individuals experiencing elevated autistic traits and housing instability. Improving access to diagnosis, autism-informed services and structural supports may represent promising targets for future interventions aimed at reducing housing instability, pending longitudinal evidence.Lay AbstractAutistic people are more likely to experience homelessness than the general population, but the reasons for this are not fully understood. Many adults with autistic traits may never receive a formal diagnosis, which can limit their access to support and services. This study examined how autistic traits, everyday functioning skills and social support relate to the risk of homelessness. We surveyed 333 adults in Australia about autistic traits, factors associated with homelessness, daily living skills and social support. Statistical analyses were used to estimate how these factors were associated with the probability of homelessness. Adults reporting higher autistic traits without a formal autism diagnosis showed a higher estimated probability of homelessness within this sample. Associations between autistic traits and homelessness differed according to diagnostic status, and stronger social support was associated with lower estimated homelessness probability. These results suggest that earlier identification of autism and improved access to support services may be associated with improved housing stability and service accessibility for autistic adults. Strengthening social support networks may also be important. These findings may help inform screening practices within homelessness services and policies that improve access to autism assessment and support.

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12. Shaw KA, Russell LA, DiRienzo M, Patrick ME, Valencia-Prado M, Bilder D, Esler A, Pierce K, Vanegas SB, Washington A, Alford A, Anbar J, Mehta R, Maenner MJ. Profound Autism Prevalence and Assessment Practices in a Population-Based Study. J Am Acad Child Adolesc Psychiatry. 2026.

OBJECTIVE: Profound autism is a proposed subcategory of autism including individuals unable to meet basic daily adaptive needs and requiring 24-hour care. This report describes documented assessment practices and profound autism prevalence derived using different definitions. METHOD: Among 7,084 8-year-old children with autism from a population-based surveillance program in 13 US sites in 2022, we calculated the availability of IQ, verbal status, and adaptive data. We imputed missing data to calculate profound autism prevalence using the « updated » (2025) definition, which includes having 1) either an intellectual quotient (IQ) score <50 OR being nonverbal/minimally verbal AND 2) adaptive functioning significantly below age level (≤70). We compared prevalence using the updated definition to using the "prior" (2021) profound autism definition that did not incorporate adaptive scores, the updated definition with higher IQ score cutoff (≤70), and using only nonverbal/minimally verbal status and adaptive ≤70. RESULTS: Among children with autism, 61.1% had IQ data, 74.0% had verbal status data, and 55.9% had adaptive data available. Cognitive data (IQ or verbal status) and adaptive data were available to determine profound autism status for 54.4%. After imputation, updated definition profound autism prevalence was 5.4 (95% CI: 5.1-5.8) and nonprofound autism prevalence was 27.5 (95% CI: 26.8-28.1) per 1,000 8-year-olds in 2022. Of children with autism, 16.5% met the updated profound autism definition and 32.1% met one element of the definition but did not meet full criteria. Prevalence was 33.5% higher using the prior definition, 70.9% higher using the updated definition with higher IQ score cutoff, and 33.3% lower using only nonverbal/minimally verbal status and adaptive ≤70. CONCLUSION: Prevalence of nonprofound autism was higher than profound autism. Adaptive or other assessments underlying the profound autism definition were missing almost half the time. Community evaluation practices and the profound autism definition can evolve to better understand and meet needs of individuals with autism.

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13. Wierenga LM, Zabihi M, van Drunen L, van der Meulen M, Achterberg M, Rutherford S, Marquand A, Crone EA. Understanding vulnerability through variability: A longitudinal twin study linking sex differences in neurodiversity, neurodevelopment and X-linked genetic mechanisms. Behav Brain Res. 2026; 514: 116350.

There are marked sex differences in the prevalence and expression of neurodiverse conditions, including autism and ADHD. Yet, it is unclear how sex-related mechanisms may affect differences in the expression of symptoms. In the present study, we examine whether previously reported greater male than female variability in neuroanatomy extends to brain development and if X-linked mechanisms are involved, using twin modeling. We further tested whether these observed patterns may be behavioral relevant and linked to symptoms of autism and ADHD. The present study included data from the L-CID study with up to three longitudinal imaging scans from 990 same-sex twin pairs (56% monozygotic, 7-14 yo, 49% females). We replicated greater male than female variability in both cross-sectional and longitudinal data, which was most pronounced for cortical surface area. In line with our hypothesis, twin modeling results support a significant role of X-chromosome expression related to sex differences in neuroanatomical variation. In addition, normative modeling analysis showed that, as predicted, deviations in brain anatomy at both upper and lower ends of the distribution were associated with symptoms of neurodiversity. These findings support a significant and understudied role of the X-chromosome in sex differences in the brain and the need to move beyond binary models to better understand sex-linked vulnerability and protective mechanisms in mental health.

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