Autism Spectrum Disorder Research

The paper identifies de novo mutations in ASD, distinguishing between germinal and postzygotic mutations and their biological roles across different developmental stages and brain regions.

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 examines how gene functional networks in individuals with intellectual disability relate to autism spectrum characteristics, finding that chromatin regulation networks are linked to higher Inflexibility.

The paper develops a phenotypic stratification model to predict autism subtypes based on SC-RRB balance and applies it to fMRI data, identifying subtype-specific neural circuitry and gene expression patterns.

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.

The paper uses complex network analysis and machine learning to classify autism spectrum disorder from developmental delay using gene dosage and Gene Ontology features, achieving 85.18% accuracy.

This study identifies shared genetic variants between ASD and ADHD using multivariate genomic analyses and finds 7 novel SNPs and 4 novel genes associated with both disorders.

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

The paper examines amygdala reactivity and functional connectivity to explicit and implicit threatening faces in ASD and controls, finding dissociated patterns linked to anxiety traits.

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 identifies de novo tandem repeat mutations in ASD probands and shows they are more prevalent, larger, and evolutionarily deleterious compared to unaffected siblings.

The paper examines the relationship between striatal intrinsic connectivity and RRB severity in children with ASD using ABIDE data, finding significant cortico-striatal correlations.

The paper identifies ultra-rare inherited mutations that are younger and preferentially transmitted to autism probands, revealing novel candidate genes and functional networks.

This pilot study examines the effect of visual cueing on motor variability in children with ASD during two-legged hopping, finding no significant reduction in variability despite higher baseline impairment in ASD.

This paper improves multi-site autism classification by minimizing site dependence using second-order functional connectivity features, achieving 73% accuracy on the ABIDE dataset.

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.

This study finds altered beta-band oscillations and connectivity in autism spectrum disorder (ASD) during detail-oriented visual processing, suggesting aberrant feedback connectivity in the visual system.

The paper quantifies autism recurrence risk by detecting paternal sperm mosaicism through single molecule genotyping and whole genome sequencing.

The paper identifies noncoding de novo mutations contributing to autism spectrum disorder by altering chromatin interactions and gene expression in neural stem cells.

The paper uses EEG data from infants to predict later ASD symptoms using multivariate pattern analysis, finding specific connectivity differences associated with ASD.

The paper identifies functional brain networks predicting behavioral regulation subdomains in children with and without ASD using connectome predictive modelling.

The paper examines neural dynamics of executive function in kindergarteners with ASD using EEG to predict academic achievement.

This study models multiplex autism using iPSC-derived neurons from a family with varying ASD liability, identifying morphological, physiological, and transcriptomic changes linked to polygenic susceptibility.