Autism Spectrum Disorder Research

The paper identifies a convergent molecular subtype in ASD by integrating epigenomic and transcriptomic data, revealing shared dysregulation and links to immune processes and genetic risk variants.

This study identifies 532 ASD-related lncRNAs, validates 86.7% with CNV data, and explores their functions and pathways in autism.

The paper assesses reduced language lateralization in autism and the broader autism phenotype using individual-subject analyses and finds it is a robust and spatially selective neural marker.

This study uses machine learning to predict autism risk genes by integrating gene expression, constraint metrics, and variation features, identifying known and novel candidates.

The paper examines the association between neonatal acute phase protein levels and autism spectrum disorder risk using a case-control study within a cohort.

The study finds no difference in neural prediction error responses between individuals with ASD and neurotypical controls to auditory rhythms of varying complexity.

The paper shows that individuals with higher autistic traits rely more on fine image details for natural image recognition.

The paper identifies atypical brain asymmetry in autism as a potential neurophenotype for stratification, showing individualized patterns linked to symptom severity and language delay.

The paper uses clustering and genome-wide association studies to identify subgroups in autism spectrum disorder and finds significant loci associated with these subgroups.

The paper shows that the Hurst exponent from fMRI BOLD signals indexes synaptic excitation-inhibition ratio, revealing sex-specific E:I imbalance in autistic mice medial prefrontal cortex.

The paper investigates how typical and autistic adolescents represent and update social knowledge during learning, finding that autistic adolescents rely only on their own preferences while typical adolescents use population preferences and prediction error updating.

This study evaluates the impact of genetic testing on an inpatient child and adolescent psychiatry service, finding increased testing rates and higher representation of underrepresented minorities.

This study replicates and extends findings of reduced 40Hz auditory steady state responses in ASD adolescents compared to typically developing controls.

This study compares global processing and intra-subject variability in ADHD, ASD, and ASD+ groups using a copying task, finding longer processing times in ASD and distinct patterns between ASD and ASD+ groups.

This work develops a single volume image generator and deep learning classifiers to improve ASD classification using multi-site brain imaging data.

The paper shows reduced neural suppression in ASD using fMRI and behavioral tasks, but finds no difference in GABA levels, suggesting top-down processing differences rather than inhibitory neurotransmitter deficits.

This study investigates the role of STEP in a VPA-induced ASD mouse model and finds that STEP inhibition reverses behavioral deficits.

The paper shows BTBR mouse genetic variants lead to altered chromatin accessibility and hyper-responsive innate immunity in macrophages, linking genetic differences to ASD-related immune abnormalities.

The paper introduces a 5-minute test called LEPS to measure prefrontal synthesis in children and shows it predicts high- versus low-functioning autism classification better than IQ tests.

This study investigates visual navigation in ASD using a computational task, finding increased variability but intact integration, with reduced variance through feedback.

The paper assesses the test-retest reliability of EEG PSD profiles in children with ASD and TD controls using two analytical approaches, showing stable individual signatures across sessions.

This study finds that individuals with ASD show atypical pupillary responses to distractor stimuli, suggesting altered locus coeruleus activity during attentional tasks.

This paper introduces forecASD, a machine learning method that identifies potential autism risk genes using genome-scale data, showing improved prediction of gene-disrupting mutations and differential expression.

This paper reviews and meta-analyzes biological motion perception in autism, finding moderate deficits with high heterogeneity, suggesting spatio-temporal factors and secondary processing roles are key.

The paper identifies genetic factors in autism that contribute to comorbidity with intellectual disability, showing increased risk for ID with specific variants.

This study investigates neural correlates of cardiac interoception in ASD and typical development, finding no global deficit but age-related changes and associations with social symptoms.

This study uses machine learning to identify biological mechanisms underlying ASD subgroups based on clinical and genetic data, finding distinct processes in mild and severe cases.

This study examines the empirical variability and limitations of the research-grade ADOS in characterizing autism, finding issues with normality, variance, and longitudinal consistency.

This study examines neural synchrony during movie watching in children with and without autism, finding reduced synchrony in ASD-diagnosed children in social cognition-related brain areas.