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.

The paper evaluates how insufficient eye tracking data affects fixation preference reliability and between-group differences in typically developing and ASD participants.

The paper was withdrawn due to methodological concerns that altered the main conclusion about gene-cholesterol cascades and brain connectivity in autistic patients.

This study finds that mimicry in autism spectrum conditions is linked to procedural learning brain regions rather than social bonding regions, suggesting communication difficulties may stem from this association.

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.

This study investigates whether autism is characterized by faster or slower model updating during early sensory processing by comparing mismatch negativity (MMN) responses in autism and control groups.

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.

This study identifies distinct synaptic and gene expression abnormalities in primate ASD models across developmental stages, suggesting potential targets for early therapeutic intervention.

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 examines the boundary sharpness coefficient (BSC) as a measure of cortical microstructure in ASD, finding age, sex, and regional differences in BSC that correlate with clinical measures.

The paper uses pregnancy data to identify biomarkers and predict autism spectrum disorder (ASD) prognosis at birth with high specificity.

This study investigates reward processing in autism spectrum disorder (ASD) and finds generalized hypoactivity in the ventral striatum during anticipation of both social and monetary rewards.

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

The paper uses single-cell RNA-seq data to develop a machine learning method called A-risk to prioritize autism risk genes, identifying 71 candidates including new ones like CLCN4, PRKAR1B, and NR2F1.

This paper introduces DeepND, a multitask learning algorithm for gene risk prioritization in comorbid neurodevelopmental disorders, identifying key brain regions and developmental windows associated with ASD and ID.

This study shows that inhibiting proBDNF to mBDNF conversion in mice leads to autism-like behaviors, suggesting proBDNF as a potential blood marker and therapeutic target for ASD.

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 paper identifies tissues most affected by SFARI Autism genes removal and finds convergence between autism and neurological disorders in tissue expression patterns.

The paper identifies a functional connectivity endophenotype that significantly increases ASD diagnosis risk and generalizes across independent data.

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.