Gareth Ball

CranioTrace automates head circumference and cranial shape measurement from neuroimaging, enabling population growth modeling and identifying disease-related growth trajectory shifts.

This paper benchmarks spatial transcriptomics in stem cell-derived organoids using Stereo-seq, optimizing RNA capture and introducing regional analysis methods.

The paper examines how BOLD variability in preterm infants relates to cortical microstructure and gene expression, finding increased variability in specific regions with decreased diffusivity and upregulated gliogenesis genes.

This study examines how thalamocortical innervation timing and spatial distribution influence cortical network formation during gestation, finding that hubs emerge from spatiotemporal constraints rather than preferential targeting.

The paper introduces dGAMLSS, a distributed algorithm for fitting GAMLSS models to construct population reference charts without sharing patient data.

The paper identifies prenatal transcriptomic signatures of network hubs in the neonatal human brain, showing how early gene expression programs shape brain network connectivity.

The paper shows that brain abnormalities after preterm birth are spatially heterogeneous but temporally consistent and plastic in response to social environments.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

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.

The paper uses MRI to show age-related changes in cortical microstructure that align with oligodendrocyte gene expression during development.

This study shows that generative network models fail to capture the spatial embedding of long-range brain connectivity, highlighting issues with current model evaluation methods.

This study maps thalamocortical connectivity in infants using diffusion MRI and finds a conserved anterior/posterior gradient that matures during gestation.

This study uses state-space models to analyze spontaneous infant movements, showing age-related changes and differences in high-risk infants.

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 identifies 928 genes associated with brain weight variation across three datasets and links them to neurodevelopmental processes and brain function.

This study identifies a medial-lateral thalamic axis of genetic and connectomic variation linked to cortical projections and neurodevelopmental disorders.

The paper introduces a longitudinal dataset of school-age children and adolescents in China, detailing its design, sampling, and neuroimaging measurements.

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.

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

The paper investigates how participant dropout affects structural MRI measures in longitudinal studies, finding that it can bias brain metric estimates and group differences.

This study identifies genetic influences on hub connectivity in the human connectome using diffusion-weighted imaging in twins and transcriptomic data.

This study examines individual variation in brain age prediction using machine learning models and finds significant between-subject differences in feature importance despite consistent overall patterns.

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

This study links childhood cortical thinning patterns with gene expression gradients, identifying genes involved in synaptic remodeling and neurodevelopmental disorders.

This paper introduces surface-based versions of neonatal brain atlases and evaluates their parcellation accuracy compared to manual labels and an adult-derived atlas.

The paper extends the M-CRIB neonatal brain atlas to include 54 white matter regions using manual segmentation and co-registration of diffusion tensor imaging data.

This study uses non-negative matrix factorisation to identify cortical regions co-varying with connectivity subnetworks in children and adolescents, finding age-related changes in cortical thickness and connectivity.

This study identifies brain morphometry features that predict individual differences in ASD symptom severity using a subject-level machine learning approach.

This study analyzes longitudinal MRI data from macaques to model postnatal brain development and links subcortical grey matter variation to birth weight.

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.