1. Banker SM, Schafer M, Barkley S, Trayvick J, Chen A, Peters AW, Thinakaran AA, Gu X, Foss-Feig JH, Schiller D. Neural Tracking of Social Navigation in Autism Spectrum Disorder. Biol Psychiatry. 2026; 100(4): 389-98.

BACKGROUND: As we navigate changing social landscapes, maintaining maps of interpersonal dynamics can help guide our choices. Autism spectrum disorder (ASD) is associated with social challenges that may affect the accumulation or application of social information. However, little is known about social cognitive mapping in autistic adults. METHODS: Herein, we investigated differences in social navigation among 122 adults with ASD, typical development (TD), and misophonia (MIS) (included as a clinical comparison group) using a social interaction task during functional magnetic resonance imaging. RESULTS: Compared with other groups, adults with ASD behaved socially distant from task characters. Nevertheless, the groups displayed comparable neural tracking of social distances in regions previously identified in nonclinical samples, including the posterior cingulate cortex (PCC), as well as the parahippocampal place area, where tracking uniquely related to cross-diagnostic social avoidance symptoms. In contrast, the ASD group showed distinctive hypoactivity in the temporal pole (TP) during social decisions, associated with smaller real-world social networks and reduced insight into their external symptoms. Additionally, while the TD and MIS groups showed functional decoupling between the TP and PCC during social decisions, this was not detected in ASD. CONCLUSIONS: Adults with ASD showed distinct behaviors and neural activity during deliberation in social interactions. However, brain systems supporting social mapping appear preserved across groups, consistent with previous findings, which have now been extended to a clinically diverse sample. These results highlight both shared and ASD-specific neural mechanisms of social navigation, thereby offering insight into potential neural differences in how social evidence guides choices in ASD.

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2. Baranes Y, Segal O. Investigating Morphosyntactic Patterns and Phonological Memory in Children With ASD and DLD. Autism Dev Lang Impair. 2026; 11: 23969415261418495.

PURPOSE: This study explored the morphosyntactic characteristics of Hebrew-speaking children with autism spectrum disorder (ASD) and developmental language disorder (DLD) as well as the associations between morphosyntactic skills and phonological memory. METHODS: Eighty Hebrew-speaking preschool children aged 5-to-6 years were assessed. All participants had nonverbal intelligence within average range IQ > 85. Language abilities were assessed using the Hebrew Goralnik test, which evaluates naming, articulation, comprehension, sentence imitation, expressive language, and storytelling skills. Morphosyntactic skills were assessed using the Katzenberger Hebrew Language Assessment test (KHLA). This test assessed the following language skills: (a) inflecting verbs for the past and future tense; (b) using the same verb in two verb-derivation patterns; (c) inflecting plural forms with changing stems and/or irregular suffixes; (d) deriving singular forms from nouns with irregular plural suffixes; (e) inflecting the plural adjectives of irregular inflected nouns; and (f) deriving consequential adjectives from a verb. In addition, phonological memory for nonsense words and syllables was assessed using the Shatil test. RESULTS: Typically developing (TD) children achieved the highest KHLA and Shatil scores. One-way ANCOVAs controlling for nonverbal intelligence revealed significant group differences. Pairwise comparisons demonstrated that TD outperformed ASD-Language- normal (ASD-LN), ASD-Language-Impaired (ASD-LI), and DLD on the KHLA and phonological memory tasks. Associations were found between KHLA and Shatil scores in ASD-LI and DLD, suggesting that morphosyntactic skills are linked to phonological memory in these groups. Hierarchical regression analysis across all groups (n = 80) revealed that phonological memory contributes an additional 11% to the variance of morphosyntactic skills (KHLA) beyond the effects of language abilities. CONCLUSION: These results highlight differences in morphosyntactic skills among subgroups of children with TD, ASD-LN, ASD-LI, and DLD that are associated with difficulties in phonological memory, especially in children with ASD-LI and DLD.

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3. Dedeoğlu ZN, Uzun N, Kılınç İ, Kılıç AO. Serum Biomarkers of Neuroaxonal and Astroglial Damage in Autism Spectrum Disorder: Relationship With Symptom Severity, Behavioural Dimensions and Age. Int J Dev Neurosci. 2026; 86(5): e70173.

PURPOSE: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by persistent deficits in social communication and the presence of restrictive, repetitive patterns of behaviour. Although its exact aetiology remains multifaceted and partially understood, recent clinical interest has shifted towards neurobiological substrates, specifically neuroaxonal and astroglial integrity. This study aims to compare serum levels of Neurofilament Light Chain (NfL), Glial Fibrillary Acidic Protein (GFAP), Tau and S100B between children with ASD and healthy controls, while investigating the influence of these biochemical variables on autism severity and behavioural manifestations. METHODS: The study cohort consisted of 44 children (aged 24-72 months) diagnosed with ASD according to DSM-5-TR criteria and 40 age-matched healthy controls. Clinical assessments were conducted using the Childhood Autism Rating Scale (CARS), the Aberrant Behaviour Checklist (ABC) and the Autism Behaviour Checklist. Serum concentrations of the targeted biomarkers were measured using the ELISA method from venous blood samples. RESULTS: Serum NfL, Tau, GFAP and S100B concentrations did not differ significantly between children with ASD and healthy controls. Exploratory analyses suggested possible associations between selected biomarkers and clinical characteristics; however, these associations did not remain statistically significant after age adjustment and correction for multiple comparisons. Further studies using larger cohorts and ultrasensitive analytical platforms are needed to validate these preliminary findings. CONCLUSION: These findings indicate that serum NfL, Tau, GFAP and S100B did not differentiate children with ASD from healthy controls. Exploratory biomarker-clinical associations did not remain statistically significant after age adjustment and correction for multiple comparisons. Larger longitudinal studies using ultrasensitive analytical platforms are warranted.

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4. Fuhr BP, Perl YS, Severino I, Kringelbach ML, Deco G, Ruhé HG, Lombardo MV. Dynamic Functional Synchronization Profiles in Autism Differ by Spatial Scale and Along Hierarchical Cortical Gradients. Biol Psychiatry Glob Open Sci. 2026; 6(5): 100787.

BACKGROUND: Prevailing theories propose that autism is characterized by local cortical overconnectivity and long-range underconnectivity, but empirical evidence remains mixed. METHODS: Here, we applied the turbulence dynamics framework to the ABIDE (Autism Brain Imaging Data Exchange) dataset (N = 1009) to examine how functional synchronization profiles dynamically change over time and across the cortex over different spatial scales. RESULTS: Autistic individuals showed increased short-range and reduced long-range functional synchronization variability over time, as well as reduced synchronization strength across all spatial scales. Synchronization also decayed more rapidly with distance and exerted weaker influence across scales in autism. These distance-specific alterations suggest that local hyperconnectivity may be associated with turbulent synchronization dynamics that fail to propagate coherently across the cortex, resulting in an overly rigid brain organization at longer distances. Mapping these effects onto the sensorimotor-association cortical gradient revealed increased variability in sensorimotor regions and decreased variability in the association cortex. CONCLUSIONS: Together, we found evidence of disturbances in functional synchronization dynamics at different spatial scales and along hierarchical brain gradients in autistic individuals. These results consolidate ideas about dynamic functional connectomic organization in autism and situate these alterations along hierarchical brain gradients that are closely linked to neurodevelopmental processes. Autism is characterized by atypical neuronal connectivity. Fuhr et al. investigated how different brain regions synchronize their activity over space and time in autism, using resting-state brain scans from more than 1000 individuals. The autistic brain shows altered synchronization patterns in terms of variability, strength, and how synchronization spreads across the brain. Furthermore, these differences between autistic and typically developed individuals follow a hierarchical organization of the brain, running from sensory to higher cognitive regions. eng.

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5. Galbraith C, Gray T, Sturges M. Walking together: exploring australian parent experiences of walking in nature with their preschool children with autism using a bioecological lens. Int J Qual Stud Health Well-being. 2026; 21(1): 2719072.

BACKGROUND: Walking regularly in natural settings is a common healthy activity for Australian parents and their preschool children. Extant research conveys that walking in nature provides cognitive, emotional and physical benefits for both parents and children. Children with autism are less likely to participate in physical activity or access nature regularly, and autistic adults are less likely to hold pro-environmental views. While research exploring the experiences of typical parents and preschoolers exists, the perceptions of parents of preschool children with autism on walking is yet to be studied. METHODS: This paper examines the experiences of parents of children with autism using Bronfenbrenner’s bioecological model as a theoretical framework. We recruited 20 parents of preschoolers with autism and a comparison group of 12 parents of preschoolers without autism and invited them to complete an online qualitative survey about walking together in nature. RESULTS: Five themes were generated from their responses, including calm, connection, coordination and communication, with an additional theme of priority. Parents identified that walking in nature provided opportunities for developmental growth for their children such as increased communication, connection and calm, but also highlighted challenges such as differences in moving together in coordination. CONCLUSION: We advocate that supporting family priorities includes supporting their ability to access everyday activities such as walking in nature, a common activity that provides rich opportunities for parents and their preschoolers with autism.

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6. K CR, Patel NR, Thurmon A, Kantor BL, Lorino MG, Tiemroth AS, Morrison V, Akumuo M, Shenoy A, Blanco-Fernandez X, Baccini V, Siddika MA, Gu C, Meadows SM, Galazo MJ. Loss of Zmiz1 in Mice Leads to Impaired Cortical Development and Autistic-Like Behaviors. Biol Psychiatry. 2026; 100(4): 447-61.

BACKGROUND: De novo mutations in transcriptional regulators are emerging as key risk factors contributing to the etiology of neurodevelopmental disorders. Human genetic studies have recently identified ZMIZ1 and its de novo mutations as a cause of a neurodevelopmental syndrome strongly associated with intellectual disability, autism, attention-deficit/hyperactivity disorder, microcephaly, and other developmental anomalies. However, the role of ZMIZ1 in brain development or how ZMIZ1 mutations cause neurological phenotypes is unknown. METHODS: We generated forebrain-specific Zmiz1 mutant mice (Zmiz1-knockout) to assess ZMIZ1 function in cortical development. Neural progenitors, excitatory neurons, and glia were assessed using immunolabeling. Neuron-specific reconstruction was applied to callosal projection neurons to analyze dendritic arborization and projection through the corpus callosum. Behavioral tests assessed motor activity, anxiety, communication, and social interactions. RNA sequencing at multiple developmental stages and chromatin immunoprecipitation sequencing (ChIP-seq) revealed molecular pathways and targets regulated by ZMIZ1. RESULTS: Loss of ZMIZ1 led to cortical microcephaly, corpus callosum dysgenesis, and abnormal differentiation of upper-layer cortical neurons. Zmiz1-knockout mice showed alterations in motor activity, anxiety, communication, and social interactions with strong sex differences, resembling phenotypes associated with autism. Zmiz1 mutation led to transcriptomic changes disrupting neurogenesis, neuron differentiation programs, and synaptic signaling. We identified Zmiz1-mediated downstream regulation of key neurodevelopmental genes, including Lhx2, Auts2, and EfnB2. Importantly, reactivation of the ephrin-B2 pathway rescued the dendritic outgrowth deficits in Zmiz1 mutant cortical neurons. CONCLUSIONS: Our in vivo findings provide insight into Zmiz1 function in cortical development and reveal mechanistic underpinnings of ZMIZ1 syndrome, thereby providing valuable information for future studies on this neurodevelopmental disorder.

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7. Maltsev D. BEYOND THE GENETIC CODE: SYSTEMIC REGULATORY MELTDOWN AS A FRAMEWORK FOR PRIMARY EPIGENETIC DISEASES. Georgian Med News. 2026; (375): 21-42.

BACKGROUND: Modern medicine is increasingly encountering multisystemic disorders that do not fit into traditional genetic or organ-specific nosologies. This study proposes and validates the concept of « primary epigenetic disease » (PED) as a distinct clinical entity. OBJECTIVE: to define the molecular mechanisms, genetic foundations, and clinical manifestations of PED, and to establish a standardized diagnostic framework using autism spectrum disorder (ASD) and chronic fatigue syndrome (CFS) as primary models. MATERIALS AND METHODS: A narrative review was conducted using PubMed and Scopus databases (predominantly 2021-2026). Evidence was synthesized from meta-analyses, randomized controlled trials, and large-scale genomic studies focusing on DNA methylation, histone modifications, and microRNA networks. RESULTS: We identified that PED emerges from a synergistic failure of the epigenetic machinery, often driven by a high load of common single nucleotide polymorphisms (SNPs) in genes such as MTHFR, DNMTs, and HDACs. We established a 30-point diagnostic matrix comprising 15 clinical criteria (e.g., developmental delays, connective tissue dysplasia, and paradoxical drug sensitivity) and 15 laboratory criteria (e.g., SAM/SAH imbalance, mitochondrial dysfunction markers, and global DNA hypomethylation). The application of this matrix to ASD and CFS demonstrates a shared pathogenic core: systemic biological desynchronization, barrier failure, and persistent low-grade inflammation. CONCLUSION: Primary Epigenetic Disease represents a « regulatory meltdown » of the genome. Effective management requires a paradigm shift from symptomatic treatment to « epigenetic rehabilitation, » which combines systemic genomic modulation with precision biochemical correction of identified bottlenecks. This framework provides a robust toolkit for the identification and management of multisystemic patients in the era of systems medicine.

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8. N A, Patil CM. Data-Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets. Int J Dev Neurosci. 2026; 86(5): e70172.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition with a broad spectrum of symptoms, which makes timely and accurate diagnosis challenging. The development of machine learning (ML) and deep learning (DL) has created opportunities for automated ASD screening and detection. This systematic review focuses on the analyses of 59 peer-reviewed studies on unimodal and multimodal approaches to ASD detection that were published between 2019 and 2025. The results demonstrated that classical ML algorithms (such as logistic regression [LR], support vector machines [SVM] and random forests [RF]) and DL models (convolutional neural networks [CNN], recurrent neural networks (RNN) and transformers) were used to assess the accuracy of the diagnosis for a variety of data modalities ranging from behavioural measures to neuroimaging, electroencephalography (EEG), eye tracking and speech, with accuracy from 68% to 99%. A careful examination of these studies, however, shows that they share certain common flaws, including small sample size, demographic bias, overfitting and absence of external validation. Hybrid multimodal frameworks have been shown to yield consistent performance improvements over unimodal frameworks, with accuracies of 95%-99% achieved through attention, graph-based learning and hybrid fusion approaches. This review highlights four major points: (1) a critical review of dataset ethics and validity, even for non-clinical facial image datasets; (2) an architectural comparison of multimodal fusion strategies (early fusion, late fusion and hybrid fusion) focusing on computational complexity and clinical applicability; (3) a quantitative summarization of the performance trends by modalities and sample size; and (4) a structured review of indicators of reproducibility and regulatory hurdles for clinical translation. This review suggests the need to develop large, well-balanced datasets, the application of explainable AI (XAI) techniques, standardization (e.g., brain imaging data structure [BIDS]) and regulatory guidelines for facilitating the clinical translation of ASD detection systems.

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9. Peng Y, Sun B, Wang H, Wei Z, Li H, Zhao S. Classification of Autism Spectrum Disorder in Children Using Electroencephalography Power Ratios Obtained During a Naturalistic Mentalizing Task. Biol Psychiatry. 2026; 100(4): 426-33.

BACKGROUND: Autism spectrum disorder (ASD) diagnosis relies on behavioral observation, but a shortage of qualified experts leads to delayed diagnosis, with the average age of diagnosis being 4.8 years. In this study, we aimed to assess the ability of resting-state electroencephalography (EEG) power spectral features and EEG features during naturalistic theory of mind (ToM) tasks to distinguish children with ASD from typically developing (TD) children and to evaluate early screening potential. METHODS: A cross-sectional diagnostic study was conducted among 183 Chinese children ages 3 to 11 years (83 with physician-diagnosed ASD and 100 TD children). After quality control, the EEG data of 163 participants were analyzed. Participants wore EEG devices while they watched Disney’s « Partly Cloudy » as a naturalistic social task and completed the resting-state recordings. The primary outcome was XGBoost-based ASD classification performance using resting-state EEG power spectral features and EEG features, evaluated by accuracy, area under the curve (AUC), sensitivity, and precision. RESULTS: A total of 163 participants (73 ASD, 90 TD) were analyzed. The groups differed significantly in sex (male proportion: 89.15% vs. 67.00%, p < .001) and IQ (92.85 vs. 112.43, p = .035). The mental-control power ratio model performed best, with an accuracy of 0.925 (95% CI, 0.909-0.940) and an AUC value of 0.980 (95% CI, 0.972-0.986). The performance of the resting-state models was poor (accuracies: 0.549 and 0.515). Cross-age prediction remained robust, with accuracies of ∼90% to 92% and AUCs >97%, showing only slightly reduced precision in the youngest group. CONCLUSIONS: Unlike resting-state EEG features, EEG power ratios during naturalistic ToM tasks distinguish children with ASD from TD children with high accuracy.

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10. Rao N, Veeranki YR. Automated autism spectrum disorder detection using EEG signals and time-frequency visibility graphs. Neuroscience. 2026.

Early and objective screening of Autism Spectrum Disorder (ASD) remains challenging because conventional diagnosis primarily relies on behavioural assessment and clinical observation. To address this limitation, this study proposes a dual-domain computational framework for automated EEG-based ASD classification by integrating complementary time-frequency analysis with Horizontal Visibility Graph (HVG)-based network modelling. Four time-frequency decomposition techniques, namely the Short-Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT), Wigner-Ville Distribution (WVD), and Superlet Transform (SLT), were employed to characterise the non-stationary dynamics of resting-state EEG signals. The resulting time-frequency representations were transformed into HVG networks, from which 17 graph-theoretic descriptors were extracted and evaluated using conventional machine learning classifiers, including a Soft Voting Ensemble. Among the investigated methods, the DWT-HVG framework combined with the Soft Voting Ensemble achieved the best performance, yielding an accuracy of 93.54%, sensitivity of 94.32%, specificity of 92.76%, F1-score of 93.62%, balanced accuracy of 93.54%, and an Area Under the Curve (AUC) of 98.17% using stratified 10-fold cross-validation. Statistical analysis using the Wilcoxon signed-rank test further confirmed the superiority of the DWT-based representation over the STFT, WVD, and SLT-based approaches. These findings demonstrate that integrating multiresolution time-frequency analysis with HVG-based graph-theoretic feature extraction provides an accurate, interpretable, and computationally efficient framework for objective EEG-based ASD screening.

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11. Rudhra O, Singh SK. Systems-level multi-omics dissection of syndromic and idiopathic autism reveals distinct regulatory architectures, candidate molecular signatures, and potential therapeutic targets. Comput Biol Med. 2026; 213: 111822.

Biological systems operate as self-organizing information networks in which genetic, epigenetic, and regulatory interactions collectively determine functional outcomes. Autism encompasses a heterogeneous set of neurodevelopmental conditions, including syndromic and idiopathic subtypes. Despite extensive gene discovery efforts, how these autism subtypes differ in their underlying organization of biological information remains poorly understood. Here, we apply an integrative systems-level, multi-omics framework to compare syndromic and idiopathic autism as distinct regulatory systems. High-confidence autism risk genes were curated from the SFARI and AutismKB databases, and analyzed using functional enrichment, protein-protein interaction network modeling, graph-theoretic hub identification, brain-region, cell-type-specific transcriptomic validation, experimentally supported miRNA regulatory network reconstruction, and deep learning-based drugtarget interaction analysis. Our analyses reveal clear differences in network organization between autism subtypes. Idiopathic autism is predominantly associated with synaptic signaling, ion channel activity, and transcriptional modulation, with hub genes KAT2B and AR enriched in basal ganglia-associated regions and astrocytes. In contrast, syndromic autism shows enrichment for transcriptional regulation, chromatin remodeling, and dense miRNA-mediated control, with hub genes CHD3 and CSNK2A1 preferentially expressed in cerebellar and cortical regions, as well as inhibitory neurons. Notably, master regulatory miRNAs differ completely between subtypes, indicating distinct post-transcriptional regulatory strategies. Deep learning-based screening further identifies subtype-specific therapeutic candidates with predicted central nervous system accessibility. Together, these findings demonstrate that syndromic and idiopathic autism differ in how regulatory information is structured and propagated across molecular networks, providing a systems-level perspective on autism heterogeneity and a general framework for analyzing biological information organization in complex systems.

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12. Schnitzler T, Perla R, Korn CW. Whole-body synchronization in autistic adults. Front Hum Neurosci. 2026; 20: 1866968.

INTRODUCTION: A characteristic feature of autistic individuals is deviations in nonverbal behavior during social interactions, evident in altered synchronization. In non-autistic individuals, increased synchronization adversely affects the capacity for emotional self-regulation. Yet, synchronization in autistic individuals has primarily been examined in conversational contexts. METHODS: In this study, 33 autistic and 33 non-autistic adults performed a dyadic movement task. Participants were asked to « have a conversation without words » using only improvised movements. Using an inertial sensor-based motion capture system, we investigated interpersonal synchronization. RESULTS: Our findings indicated greater synchronization in autistic dyads than in non-autistic dyads. Mixed dyads did not differ from either autistic or non-autistic dyad. Across all three dyad types, the task was not associated with reliable observed changes in emotional self-regulation, positive affect, or negative affect, and baseline-adjusted post-task outcomes were not reliably associated with interpersonal synchronization. However, it is important to note our small sample size, which limits the robustness and generalizability of the results. DISCUSSION: We discuss our findings in relation to the task demands. Whole-body synchronization played a crucial role in our study, unlike in previous studies of conversational settings. Consequently, our task relied less on language, familiarity with social interaction tasks, and eye contact, all of which are aspects that autistic individuals often find challenging. Our findings contribute to the ongoing debate on interpersonal synchronization in autism spectrum condition by extending previous studies on conversational contexts to whole-body movement. The data can be combined with future datasets to create larger samples and provide a more nuanced understanding about synchronization in autism spectrum condition.

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13. Schuurmans IK, Smajlagic D, Baltramonaityte V, Malmberg ALK, Neumann A, Creasey N, Felix JF, Tiemeier H, Pingault JB, Czamara D, Raïkkönen K, Page CM, Lyle R, Havdahl A, Lahti J, Walton E, Bekkhus M, Cecil CAM. Genetic Susceptibility to Neurodevelopmental Conditions Is Associated With Neonatal DNA Methylation Patterns in the General Population: An Individual Participant Data Meta-Analysis. Biol Psychiatry. 2026; 100(4): 414-25.

BACKGROUND: Autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and schizophrenia (SCZ) are highly heritable and linked to disruptions in fetal neurodevelopment. Epigenetic processes, such as DNA methylation (DNAm), are considered a key pathway of interest. However, it is unclear whether 1) genetic susceptibility to neurodevelopmental conditions (NDCs) is associated with DNAm patterns already at birth, 2) DNAm patterns are unique or shared across conditions, and 3) neonatal DNAm patterns can be leveraged to enhance genetic prediction of neurodevelopmental outcomes. METHODS: We conducted epigenome-wide meta-analyses of genetic susceptibility to ASD, ADHD, and SCZ (measured with polygenic scores [PGSs]) and cord blood DNAm in 4 European population-based cohorts (n(pooled) = 5802; 50.2% female). We estimated DNAm pattern overlap between PGSs using heterogeneity statistics. Furthermore, we built methylation profile scores for each PGS to test incremental variance explained over genetic data alone in 130 developmental outcomes from birth to 14 years. RESULTS: In probe-level analyses, the SCZ PGS was associated with neonatal DNAm at 246 loci (p < 9 × 10(-8)), predominantly in the major histocompatibility complex, supporting an early-origins perspective on SCZ. Functional characterization confirmed strong genetic effects, blood-brain concordance, and enrichment for immune-related pathways. Eight loci were identified for the ASD PGS (mapping to FDFT1 and MFHAS1) and none for the ADHD PGS. Differentially methylated regions were detected across PGSs (130-166 regions). Overall, DNAm signals were largely distinct between conditions. Incorporating neonatal DNAm data in genetic prediction models nominally increased the explained variance for several cognitive and motor outcomes. CONCLUSIONS: Genetic susceptibility to NDCs, particularly SCZ, is detectable in cord blood DNAm in the general population.

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14. Shamay-Tsoory SG. From Social Maps to Social Interactions in Autism. Biol Psychiatry. 2026; 100(4): 351-2.

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15. van Erp ML, Yuwattana W, Poolcharoen C, Saeliw T, Roytrakul S, Vanwong N, Hu VW, Trairatvorakul P, Chonchaiya W, Sarachana T. Peripheral blood mononuclear cell proteomic profiling reveals cytoskeletal- and chromatin-associated protein signatures in autism spectrum disorder. Brain Behav Immun Health. 2026; 56: 101319.

PURPOSE: We aimed to identify protein patterns in peripheral blood mononuclear cells (PBMCs) associated with autism spectrum disorder (ASD) diagnosis and clinical heterogeneity and to explore their relevance to biological processes implicated in ASD. METHODS: PBMC proteomic profiles were examined in a Thai cross-sectional cohort of 191 children with ASD and 106 typically developing (TD) controls. Mass spectrometry (LC-MS/MS) was performed to identify differentially expressed proteins (DEPs). Exploratory classification performance was assessed by ROC analysis across clinical subgroups. Associations between DEPs and behavioral and cognitive symptoms were evaluated using mixOmics multivariate integration. DEPs were further assessed for overlap with ASD-associated genes and previously reported ASD blood and brain proteomic datasets, and functionally annotated using protein-protein interaction and enrichment analyses. RESULTS: Thirty-nine annotated proteins were differentially expressed between children with ASD and TD controls, while a subset was associated with heterogeneity within ASD. Several DEPs were encoded by known ASD-associated genes and overlapped with proteins previously reported in ASD blood and brain studies. Functional analyses identified enrichment of cytoskeletal and chromatin-associated pathways, including an actin-centered protein interaction network. Multivariate integration analyses revealed associations between these proteins and behavioral, social, emotional, sensory, and cognitive phenotypes, while subgroup analyses indicated subtle molecular heterogeneity within ASD. CONCLUSION: PBMC proteomic profiles identified molecular signatures associated with ASD diagnosis and clinical heterogeneity in this discovery cohort. Exploratory network analyses highlighted cytoskeletal- and chromatin-associated proteins, supporting further evaluation of peripheral proteomics as a tool for investigating ASD biology and biologically informed stratification.

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16. Xue Y, Jia FY. [Focusing on profound autism: validity of clinical taxonomy and management of treatment-resistant symptoms]. Zhongguo Dang Dai Er Ke Za Zhi. 2026; 28(8): 909-15.

The broad-spectrum diagnostic framework for autism spectrum disorder (ASD) in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) tends to obscure the specific medical and support needs of individuals with severe impairments. In response, the Lancet Commission on the future of care and clinical research in autism proposed the concept of « profound autism » in 2021, referring to individuals with ASD who are aged 8 years or older, have severe intellectual disabilities (IQ < 50) and/or minimal or completely absent verbal communication abilities, and require round-the-clock (24-hour) care. Patients with profound autism often present with occult somatic comorbidities and treatment-resistant psychiatric abnormalities, including life-threatening self-injurious behavior and catatonia, which frequently render traditional stepped-care interventions ineffective. This review systematically summarizes the conceptual definition and clinical phenotypes of profound autism and emphasizes the need to shift the intervention focus toward managing serious comorbidities and establishing augmentative and alternative communication systems. Additionally, the potential application of modified electroconvulsive therapy as a salvage treatment for refractory and life-threatening symptoms is discussed. The aim is to provide evidence-based guidance for multidisciplinary management and the construction of comprehensive lifespan support systems for individuals with profound autism.

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17. Yang K, Geng Y, Zhou W, Zhang L, Xu Z, Wang J, Li F, Qiu Z. Closing the Gap in Autism Genetics: Population-Specific Variants and the Imperative for Global Inclusion. Biol Psychiatry. 2026; 100(4): 358-68.

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental condition with an exceptionally complex and heterogeneous genetic architecture, encompassing both polygenic common variants and rare, high-impact variants. Over the past decade, large-scale sequencing studies in Europe and North America have identified hundreds of ASD risk genes and substantially advanced biological insight. However, the global distribution of ASD genomic research remains profoundly imbalanced, with most non-European ancestry populations severely underrepresented. This Eurocentric bias constrains variant discovery, limits fine-mapping resolution, and reduces the generalizability of genetic findings, with direct implications for biological interpretation, diagnosis, and therapeutic development. In this review, we synthesize current evidence on the global landscape of ASD genomics, emphasizing the striking contrast between high-depth, well-powered Euro-American cohorts and the persistent undersequencing of populations in Asia and Africa. We highlight emerging data demonstrating pronounced ancestry-specific risk genes, indicating that the genetic architecture of ASD is not uniform worldwide. We further discuss the downstream biological inference and translational applications, including artificial intelligence-assisted diagnostics and gene-based therapies. We argue that achieving a comprehensive and biologically meaningful understanding of risk genes of ASD requires large-scale trans-ethnic sequencing, integrative multi-omic approaches, and coordinated global collaboration.

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