Emma C. Robinson

This study examines structural brain maturation patterns in neonates using MRI data, finding age-related changes and differences between term and preterm-born infants.

This study identifies a consistent cortical signature of very preterm birth across development and links it to neurodevelopmental outcomes.

This paper presents a normative framework for brain asymmetry across the lifespan using a large neuroimaging dataset, revealing distinct spatial patterns and providing tools for clinical and research applications.

This study uses MRI data to compare cortical growth patterns in fetal and preterm brains, revealing distinct developmental trajectories and implications for cortical organisation.

This study identifies common cortical folding patterns across age groups using hierarchical surface registration, revealing hemispheric asymmetry and dissociation between folding and functional organisation.

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.

The paper presents a 3D digital atlas of the human fetal brain at 21 weeks gestation, revealing molecular signatures of cortical expansion linked to neurogenesis timing and evolutionary boundaries.

This study uses the Vogt-Bailey index to analyze cortical microstructure gradients in preterm and term infants, finding spatially distributed alterations in preterm infants.

The paper constructs a sulcal phenotype network from MRI scans to identify two clusters of sulci related to developmental timing and gene expression gradients.

This paper develops a generative model to simulate cortical surface development and assesses its ability to predict cognitive outcomes in preterm infants.

The paper introduces a deep learning pipeline for automated fetal brain segmentation and volumetry, showing reduced need for manual refinement and agreement with manual segmentations in clinical cases.

The paper investigates structural and functional asymmetry in the neonatal cerebral cortex using MRI data from term and preterm neonates and adults.

This paper generalises the HCP multimodal cortical parcellation to UK Biobank data using a geometric deep learning framework, improving detection rates across cortical areas.

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

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.

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.

This paper develops a data-driven framework using non-negative matrix factorisation to map structural connections in the neonatal brain and generates a cortical parcellation from 323 newborns.

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

This paper estimates local cortical growth patterns in preterm infants using aMSM, revealing regional and temporal differences in expansion linked to folding and injury detection.

This study examines how the spatial arrangement of brain regions influences functional connectivity and behavioral predictions.

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.