Logan Z. J. Williams

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 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.

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