Christine Ecker

This study uses genome-wide meta-regression to analyze social behavior across different contexts, ages, and reporters, identifying six genetic loci and showing modest heritability.

The paper investigates the relationship between glutamate and GABA gene-sets, autism symptoms, and cortical thickness using data from the EU-AIMS LEAP cohort.

The paper identifies patterns of neural variability in autism using fMRI across tasks, finding greater global connectivity deviations and similar spatial patterns in autistic individuals compared to controls.

The paper identifies a multimodal brain pattern associated with autism by integrating gray and white matter morphology data and linking it to clinical symptoms.

The paper examines hemispheric asymmetry in structural covariance networks in ASD across 43 datasets, finding altered topological features in specific brain regions.

The paper investigates sex differences in autism using resting-state fMRI data, finding significant effects in brain connectivity and replicating some findings in independent samples.

The paper identifies structural covariation alterations in autism using VBM and links these patterns to core autism symptoms through canonical correlation analysis.

fsbrain is an R package for visualizing structural neuroimaging data, offering 3D visualization, customization, and publication-quality outputs.

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 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.

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.

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 identifies altered structural brain asymmetry in autism spectrum disorder using a large-scale meta-analysis of MRI data from 1,774 ASD cases and 1,809 controls.

This study compares structural brain differences across ADHD, ASD, and OCD using MRI data from multiple cohorts, finding disorder-specific alterations in brain volume and cortical thickness.

This study uses normative models to analyze cortical thickness deviations in ASD, finding individualized patterns rather than common case-control differences.

The paper identifies 5 distinct subgroups within autism spectrum conditions using clustering analysis on cognitive datasets, revealing varying levels of mentalizing impairments.