Emer Hughes

The paper describes the release of a large fetal fMRI dataset and presents methods for its preprocessing and analysis to study in utero brain connectivity.

This study examines dynamic functional connectivity in neonatal brains using fMRI and links it to neurodevelopmental outcomes in early childhood.

This study identifies depth-dependent cortical microstructure alterations in children with focal epilepsy using quantitative MRI, suggesting potential pre-symptomatic changes detectable regardless of radiological abnormalities.

This paper introduces a new analytical framework called matnets to study fetal brain connectivity, revealing that high-level associative areas develop functional connections before birth.

This study uses diffusion MRI to track thalamocortical pathway maturation in human fetal brains, identifying key neurobiological changes in the second to third trimester.

The paper examines how functional brain centrality develops in neonates and how preterm birth affects it, finding age-related changes and regional differences but no direct link to 18-month neurodevelopmental outcomes.

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 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 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 study finds elevated Myo-inositol levels in the developing brains of individuals with Down Syndrome, detectable as early as 10 weeks post conception.

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.

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 uses in-vivo MRI to identify early structural brain growth deviations in fetuses and neonates with Down Syndrome compared to controls.

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.