Sean P. Fitzgibbon

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

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 introduces an automated pipeline for registering stand-alone histology sections to whole-brain MRI data with sub-voxel accuracy.

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 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 uses functional and diffusion MRI to predict infants' noxious-evoked brain activity from resting-state data and links it to white matter microstructure.

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.

The paper links multimodal brain imaging markers in adolescents with physical activity and health measures using canonical correlation analysis.

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 extends probabilistic functional modes to model subject variability in fMRI data, showing spatial maps capture most cross-subject variability and functional coupling reflects cognitive state.

This paper translates adult fMRI pain signatures to infants, finding similar basic nociceptive encoding but differences in higher-level cognitive modulation of pain.

The paper introduces a fully automated pipeline for neonatal cortical surface reconstruction from MRI, achieving high accuracy in 85% of cases.