Molecular Autism最新文献

Background: Noonan syndrome (NS) and Noonan syndrome with multiple lentigines (NSML) are neurodevelopmental conditions caused by genetic variants leading to upregulated signaling in the RAS-MAPK pathway. While previous research has focused on genetic variability in cognitive and cardiac phenotypes, behavioral phenotypes, and their correlations across genetic variants and within the PTPN11 gene remain poorly characterized.

Methods: This study included 121 individuals with NS (PTPN11: 88, SOS1: 18, RAF1: 6, KRAS: 2, RIT1: 3, NRAS: 2, LZTR1: 2, SOS2: 1) and seven individuals with NSML (PTPN11), compared to age- and sex-matched typically developing (TD) (N = 71). Behavioral questionnaires assessed social responsiveness and ASD-related traits (using SRS-2), and emotional problems (using CBCL) to identify genetic variant-specific behavioral profiles. Biochemical profiling of SHP2 activity in PTPN11-associated NS variants examined genotype-phenotype relationships.

Results: Compared to TD individuals, those with PTPN11-associated NS, NSML, and SOS1-associated NS exhibited clinically elevated scores, indicating increased ASD-related behaviors, poorer social functioning, and heightened emotional problems. Genetic variant comparisons revealed that individuals with PTPN11-associated NS and NSML exhibited greater ASD-related challenges than those with RAF1. Individuals with NSML exhibit elevated attention problems compared to all other genetic groups. Logistic regression results suggested each one-unit increase in SHP2 fold activation for PTPN11-associated NS corresponded to a 64% higher likelihood of markedly elevated restricted and repetitive behaviors, suggesting genotype-phenotype links.

Limitations: Small sample sizes for rarer variants, leading to unequal group sizes across subgroups, with PTPN11 variants comprising most of the NS group. Future research should address these sampling constraints and conduct functional studies to clarify variant impacts. Longitudinal assessments could elucidate behavioral phenotype trajectories.

Conclusions: This study underscores the importance of genetic variant-specific research to understand unique behavioral phenotypes in NS and NSML. Our findings indicate a higher risk for ASD-related symptoms in PTPN11-associated NS and NSML compared to other variants. Additionally, individuals with PTPN11-associated NS and higher SHP2 fold activation exhibited greater impairments in restricted and repetitive behaviors, suggesting SHP2 activation variations may contribute to phenotypic variability. By linking ASD-related symptoms to biochemical predictors in PTPN11-associated NS, this study may inform future targeted treatment approaches.

Background: Difficulties with non-verbal communication, including atypical use of facial expressions, are a core feature of autism. Quantifying atypical use of facial expressions during naturalistic social interactions in a reliable, objective, and direct manner is difficult, but potentially possible with facial analysis computer vision algorithms that identify facial expressions in video recordings.

Methods: We analyzed > 5 million video frames from 100 verbal children, 2-7 years-old (72 with autism and 28 controls), who were recorded during a ~ 45-minute ADOS-2 assessment using modules 2 or 3, where they interacted with a clinician. Three different facial analysis algorithms (iMotions, FaceReader, and Py-Feat) were used to identify the presence of six facial expressions (anger, fear, sadness, surprise, disgust, and happiness) in each video frame. We then compared results across algorithms and across autism and control groups using robust non-parametric statistical tests.

Results: There were significant differences in the performance of the three facial analysis algorithms including differences in the proportion of frames identified as containing a face and frames classified as containing each of the six examined facial expressions. Nevertheless, analyses across all three algorithms demonstrated that there were no significant differences in the quantity of any facial expression produced by children with autism and controls. Furthermore, the quantity of facial expressions did not correlate with autism symptom severity as measured by ADOS-2 CSS scores.

Limitations: The current findings are limited to verbal children with autism who completed ADOS-2 assessments using modules 2 and 3 and were able to sit in a stable manner while facing a wall-mounted camera. Furthermore, the analyses focused on comparing the quantity of facial expressions across groups rather than their quality, timing, or social context.

Conclusions: Commonly used automated facial analysis algorithms exhibit large variability in their output when identifying facial expressions of young children during naturalistic social interactions. Nonetheless, all three algorithms did not identify differences in the quantity of facial expressions across groups, suggesting that atypical production of facial expressions in verbal children with autism is likely related to their quality, timing, and social context rather than their quantity during natural social interaction.

Background: Fragile X syndrome (FXS) is characterized by cortical hyperexcitability, a core neurophysiological feature that contributes to sensory hypersensitivity, cognitive dysfunction, and other disabling symptoms. This disruption in excitatory-inhibitory balance is a key pharmacological target, yet reliable biomarkers to quantify it noninvasively remain limited. Spectral slope, derived from the aperiodic component of the EEG power spectrum, has emerged as a potential index of cortical excitability. Here, we evaluated spectral slope and theta-alpha peak frequency in individuals with FXS to assess their utility as candidate neurophysiological biomarkers.

Methods: Five minutes of resting state EEG data were collected from 70 subjects with FXS (mean age 20.5 ± 10 years; 32 females) and 71 age-matched controls (mean age 22.2 ± 10.7 years; 30 females). The Spectral Parameterization toolbox (SpecParam) was used to separate periodic and aperiodic components of the source localized power spectra and characterize aperiodic slope and theta-alpha peak frequency.

Results: Statistical modeling of aperiodic slope revealed a significant two-way interaction between sex and diagnostic group, but no interaction with brain lobe. Among males, the aperiodic slope was significantly decreased in FXS, indicating greater cortical excitability, compared to typically developing controls (TDC), whereas no difference was observed between FXS and TDC females. For peak alpha frequency, statistical modeling identified significant two-way interactions between sex and diagnostic group, and between brain lobe and diagnostic group, but no significant three-way interaction.

Limitations: This study is limited by the absence of non-invasive measures of cortical fragile X mental retardation protein (FMRP). Additionally, participants were not stratified by mosaic status and FMRP levels were not quantified, which could affect variability and interpretation.

Conclusion: Compared to traditional band-limited power measures, aperiodic slope provides a more direct and validated index of excitation-inhibition balance. Our findings of reduced aperiodic slope in male subjects with FXS align with preclinical circuit-level evidence of increased excitability in FXS and are consistent with previous findings of reduced individual alpha peak frequency, supporting with thalamocortical dysrhythmia models of FXS. Importantly, aperiodic slope measurements can be applied directly to various modalities of local field potential data, enabling more robust cross-species and translational comparisons.

Background: Autistic individuals generally exhibit real-world executive function (EF) difficulties and perform poorly on EF tasks. However, while autistic traits are distributed continuously throughout the general population, the relationships between autistic traits and EF among nonclinical individuals remain unclear. Here, we conducted complementary meta-analyses to clarify the relationships between autistic traits and various aspects of EF in the general population.

Methods: A literature search was conducted using PubMed, PsycINFO, and Web of Science on July 11, 2025. After screening by two independent reviewers, 39 articles that reported 40 studies were included. These studies either compared EF between groups with high and low autistic traits, based on a cutoff, or investigated the relationships between continuous measures of autistic traits and EF.

Results: We found significant negative associations between autistic traits and EF among nonclinical individuals across EF processes. Notably, these relationships were observed only when EFs were measured using questionnaires rather than behavioral tasks. Specifically, random-effects and robust Bayesian meta-analyses revealed significant, strong correlations between higher autistic traits and poorer ratings on EF questionnaires, with primarily substantial evidence supporting the presence than absence of relationships. In contrast, the meta-analyses indicated nonsignificant, very weak correlations between higher autistic traits and poorer performances on EF tasks, with primarily substantial evidence supporting the absence than presence of relationships.

Limitations: These findings were mainly based on self-reported measures of autistic traits in adults and derived from single studies without follow up or replication.

Conclusions: Autistic traits are associated with lower perceived real-world EF behavior rather than poorer EF task performance in the general population. These findings underscore the importance of paying closer attention to addressing the concerns of individuals with high autistic traits and their parents regarding their own and their children's EF behavior. Based on the available evidence, we construct a picture of the relationships between autistic traits and EF across the trait's continuum.

Registration: This study was preregistered at https://osf.io/zncv3 .

Background: Structural alterations in subcortical brain regions-including the amygdala, hippocampus, basal ganglia, and cerebral ventricles-have been linked to various clinical features of autism spectrum disorder (ASD). However, volumetric features among these regions in autistic individuals across the lifespan remain poorly understood. This cross-sectional study aimed to investigate age-associated volumetric deviations in these clinically implicated subcortical regions of autistic individuals and neurotypical controls, and to examine the structural interrelationships within each group.

Methods: We examined multi-site T1-weighted MRI data from 119 autistic and 122 neurotypical participants aged 7-73 years. Volumetric data for the amygdala, hippocampus, basal ganglia, and cerebral ventricles were harmonized across sites using the ComBat algorithm. Following this, volumetric composite indices (principal component scores) were extracted for each region using principal component analysis. These scores represent dominant volumetric patterns of each subcortical region, with higher values reflecting greater volume. These composite scores were then compared between groups and with increasing age.

Results: Autistic participants exhibited greater amygdala volume in early life, followed by more pronounced age-associated reductions in adulthood compared to neurotypical controls. A similar trend was observed for the hippocampus, with early volumetric enlargement giving way to steeper declines in later years. In contrast, the autistic group consistently trended towards larger basal ganglia across the lifespan. Additionally, autistic participants showed accelerated enlargement in the cerebral ventricles with increasing age. Both groups exhibited patterns of inverse volumetric associations between the cerebral ventricles and surrounding subcortical regions in later adulthood; however, these relationships were more pronounced and widely distributed in the autistic group.

Limitations: The cross-sectional design of this study limited us from capturing intra-individual differences at baseline and quantifying the lifespan trajectories of each participant. Site-related sampling differences may have introduced cohort bias in the results.

Conclusions: Autistic participants and neurotypical controls exhibit distinct, age-related volumetric patterns across key subcortical brain regions. Enlargement of the cerebral ventricles and their inverse structural relationships with neighboring structures in later life may indicate atrophic processes beginning in middle adulthood in ASD. These findings highlight the need to further investigate mechanisms of atypical brain aging in ASD and consider these subcortical brain regions as potential biomarkers of neurodegeneration and intervention targets across the adult lifespan.

Background: Angelman Syndrome (AS) is a severe neurodevelopmental disorder with only symptomatic treatment currently available. The primary cause of AS is loss of functional UBE3A protein. This can be caused by deletions in the maternal 15q11-q13 region, maternal AS-imprinting center defects (mICD), paternal uniparental disomy of chromosome 15 (UPD) or mutations within the UBE3A gene. Current mouse models are Ube3a-centric and do not address expression changes of other genes in the 15q11-q13 locus on the pathophysiology of AS. This limits the ability to discern differences in therapeutic responses to current UBE3A-targeting strategies and hampers the identification of novel therapeutics/co-therapeutics.

Methods: Using a mouse line that harbors a maternally inherited mutation affecting the AS-PWS imprinting center ('mICD mice'), we studied the impact of the mICD or UPD AS subtype on behavior, seizure susceptibility and proteome. Additionally, by using mice overexpressing two copies of Ube3a or antisense oligonucleotide (ASO) targeting Ube3a-ATS, we analyzed the impact of bi-allelic Ube3a activation on behavior and proteome.

Results: mICD mice showed 80% reduction in UBE3A protein, bi-allelic expression of Ube3a-ATS and Mkrn3-Snord115 gene cluster, leading to robust AS behavioral deficits and proteome alterations similar to Ube3a^m-/p+ mice. Genetic UBE3A overexpression in mICD mice, mimicking therapeutic strategies that effectively activate the biallelic silenced Ube3a gene, resulted in a complete rescue of all behavioral phenotypes, seizure susceptibility and proteome alterations. Subsequently, treatment with an antisense oligonucleotide (ASO) to directly activate the biallelic silenced Ube3a gene in mICD mice also resulted in efficient reinstatement of UBE3A, 30% higher relative to WT, alongside a partial rescue of behavioral phenotypes.

Limitations: Despite using a highly robust AS-specific behavioral battery, we did not investigate readouts such as neuronal activity and sleep, for which impairments in Ube3a^m-/p+ mice were described.

Conclusions: Taken together, these findings demonstrate that the loss of UBE3A protein is the primary factor underlying AS phenotypes in this mICD/UPD mouse model of AS, while the biallelic expressed genes in this locus play either a marginal or yet unidentified role. These findings also corroborate UBE3A reinstatement as an attractive therapeutic strategy for AS individuals carrying an mICD or UPD mutation.

Background: Difficulty in social inferences is a core feature in autism spectrum disorders (ASD). On the behavioral level, it remains unclear whether reasoning about others' mental states (Theory of Mind, ToM) and empathic responses to others' physical states may be similarly or differentially affected in autism. On the neural level, these inferences typically engage distinct brain networks (ToM versus Pain networks), but their functional specialization remains not well understood in autism. This study aimed to investigate the functional specialization, heterogeneity, and brain-behavior relationships of the ToM and Pain networks in autistic compared to neurotypical (NT) participants. We hypothesized differential functional network specialization (i.e., functional connectivity), increased heterogeneity, and less typical network responses specifically in the ToM network, with relatively similar responses in the Pain network in ASD.

Methods: Using functional magnetic resonance imaging (fMRI), we investigated neural responses in 107 adults (autistic: 34 (female = 11), NT: 73 (female = 23); matched for age, intellectual functioning, sex, motion) while they passively watched a short, animated movie including events that evoke reasoning about characters' mental states and bodily sensations. Preregistered analyses included regression models to assess inter-region correlation of within- and across-network connectivity, inter-subject correlation to quantify similarity to the average neurotypical, as well as to within- and across-group timecourse responses, and brain-behavior relationships relevant for social inferences.

Results: Functional specialization of ToM and Pain networks were overall intact, with distinct network responses in both groups. The autistic group showed differential ToM network responses and reduced similarity to the average typical response for both networks. Network responses were more idiosyncratic and heterogenous in the autistic group. Brain-behavior relationships differed between groups for ToM behavior only.

Limitations: Effects between groups were overall small. Samples were acquired across two sites, yet the sample size restricts subgroup analyses that may further inform autistic heterogeneity and limits generalizability.

Conclusions: We found weak evidence for greater differential responses in brain networks underlying ToM inferences than those involved in empathic responses in autism, consistent with a prior empathy imbalance hypothesis. We outline suggestions for replicating, generalizing and extending these results in future research.