Anthony David Edwards

The paper identifies a stable structural connectome fingerprint in preterm neonates that can distinguish individuals more effectively than functional connectomes.

This paper uses machine learning to predict neonatal brain age and clinical risk from structural connectomes, finding thalamocortical connections are key predictors of developmental outcomes.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

The paper uses neonatal cortical connectivity data to predict 18-month developmental outcomes, finding that structural covariance networks predict social-emotional performance.

This study examines how gestational age at birth affects structural brain development in healthy term-born infants and links these changes to neurodevelopmental outcomes at 18 months.

This paper benchmarks geometric and traditional deep learning models for cortical segmentation and neurodevelopmental phenotype prediction using neonatal cortical surface data.

This study examines how preterm birth affects cortical development in infants at term-equivalent age using MRI, finding altered microstructure and morphology with individual variability.

This paper develops a multi-channel 4D atlas of neonatal brain development combining structural and diffusion MRI data, showing microstructural differences between term and preterm infants.

The paper analyzes neonatal functional MRI data to study functional connectivity in term and preterm infants, finding age-related maturation and preterm birth-related disruptions in brain networks.

This paper applies unsupervised domain adaptation techniques to segment neonatal brain MRI data from a preterm population, improving segmentation accuracy and enabling analysis of cortical thickness and language outcomes.

The paper develops normative brain volumetric development curves for term-born infants and compares preterm infants to these curves, identifying individual deviations linked to clinical factors and neurocognitive outcomes.

This paper benchmarks geometric deep learning models for cortical parcellation on the Human Connectome Project data, showing high overlap but variability in specific brain regions.

The paper links cortical morphology at birth to gene expression patterns in the fetal brain and shows preterm birth disrupts this relationship.

This paper introduces an automated pipeline for processing neonatal resting-state fMRI data, improving motion correction and quality assurance in the developing Human Connectome Project.

The paper optimizes multi-shell diffusion MRI acquisition parameters using a data-driven approach, resulting in a specific protocol for neonatal imaging.

The paper develops cortical profiles in neonatal brains using MRI to track morphometric and microstructural changes, revealing how network similarity evolves over time.

This study examines ADHD symptoms in very preterm children, finding higher rates of subthreshold ADHD and associations with cognitive and socio-demographic factors.

The paper investigates microstructural cortex development in neonates with congenital heart disease and finds impaired oxygen delivery correlates with abnormal microstructure.

The paper finds that genetic risk for neuropsychiatric diseases is linked to reduced deep grey matter volume in preterm infants, suggesting a mechanism for how early adversity interacts with genetic vulnerability.

The paper examines how brain network properties at birth predict language skills and shows that syntactic abilities are affected by preterm birth.

The paper introduces a fully automated pipeline for neonatal cortical surface reconstruction from MRI, achieving high accuracy in 85% of cases.

A linearly scaling groupwise method jointly estimates mean brain shape and longitudinal change across 275 neonates (35 to 44 weeks post-menstrual age), yielding a temporally consistent morphological atlas.

This paper introduces a new regularization method for cortical surface alignment in brain imaging, improving the robustness and biological interpretability of multi-modal data alignment.

This paper identifies DLG4 (PSD95) expression in microglia and links genetic variation in DLG4 to white matter structure in preterm infants.