Joseph V. Hajnal

This study uses 7 Tesla fMRI to examine BOLD responses in newborns and adults, showing depth-dependent changes in cortical activity related to development.

This paper validates a motion correction technique, DISORDER, for pediatric brain morphometry, showing improved reliability for motion-degraded scans compared to conventional MPRAGE.

This study uses in vivo tractography to examine neonatal white matter pathways related to hypothalamic and reward functions, linking early-life factors to later neurodevelopmental and obesity outcomes.

This study links ASD common genetic variants to regional brain volume differences in newborns, finding positive associations with frontal and fronto-temporal volumes and negative associations with parietal and parieto-occipital volumes.

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

The paper examines microstructural changes in fetal brain compartments before cortical folding begins, finding that anisotropy decreases in subplate and cortical plate regions prior to sulcal pit formation.

The paper shows that functional connectivity gradients in resting state networks are present in the human fetal brain from 25 weeks gestation, with spatial consistency increasing in primary sensory and motor networks.

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 MRI to compare brain volumes in neonates with Down syndrome to a control group, finding reduced whole brain volume in DS 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.

This study assesses visual attention in toddlers using eye-tracking data from 350 participants and introduces a shared data resource for research.

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

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 investigates the role of common genetic variation in neonatal subcortical brain development by examining SNP associations and polygenic scores from adult studies.

This paper proposes an automated pipeline for reconstructing fetal thorax MRI from motion-corrupted stacks across 21-36 weeks GA, using 3D CNNs and landmark-guided pose estimation.

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.

The paper creates 3D MRI atlases of normal and abnormal fetal aortic arch anatomy and evaluates deep learning for automated multi-label segmentation.

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.

dStripe corrects slice artefacts in diffusion MRI using a constrained neural network to reduce interslice inconsistencies without biasing 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.

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