Tony Charman

This study uses longitudinal normative modelling of white matter fractional anisotropy in the EU-AIMS LEAP cohort to show individual deviations in white matter development are linked to clinical variation in autism, with significant cross-sectional and longitudinal brain-behaviour associations.

The paper introduces a multiscale resource integrating genetics, brain imaging, and clinical data from autistic individuals and their relatives to explore genetic and neurobiological patterns in autism.

The paper investigates how cortical excitability during fixations relates to frequency-specific neural activity in children and adults, finding developmental differences in oscillatory patterns.

The paper performs an epigenome-wide analysis of DNA methylation in infants to identify associations with the pupillary light reflex, finding novel methylation signatures linked to PLR latency and amplitude.

The paper examines EEG abnormalities in ASD and controls, finding reduced fE/I in both groups with abnormalities, suggesting inhibition-dominated networks in sensory regions.

EAGER establishes a registry of autistic individuals and those with rare genetic conditions for genomic and phenotypic research, aiming to improve clinical trials and health outcomes through data sharing.

The paper maps functional connectivity patterns in the somatosensory cortex during rest and emotional tasks in ASD and neurotypical individuals, linking them to adaptive functioning skills.

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.

This study identifies factors linked to early autism traits in toddlers using a community cohort, finding associations with parenting style and socio-economic deprivation.

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

This study identifies genetic correlates of phenotypic heterogeneity in autism by analyzing common and de novo variants across core features and co-occurring disabilities.

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.

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

This study identifies reproducible functional connectivity alterations in ASD using rs-fMRI across four large cohorts, linking them to clinical symptoms and brain regions.