Congenital heart disease (CHD) affects almost 1% of UK births, and is the most frequent congenital malformation. Improvements in antenatal diagnosis, cardiac surgery, and perioperative care mean that most infants born with CHD now survive. However, children and adults with CHD who required cardiopulmonary bypass surgery in infancy are at increased risk of adverse neurodevelopmental sequalae. The underlying mechanisms leading to these impairments are not clear but are likely to begin in utero when critical processes of brain development are taking place.
Using quantitative MRI techniques, we have shown altered brain development after birth in infants with CHD. This study will use state-of-the-art fetal neuroimaging combined with machine learning approaches to assess brain development in fetuses with CHD and in healthy fetuses in order to determine whether brain development is impaired in the CHD sample before birth.
Machine learning approaches to analyse neuroimaging data (Dr Robinson), fetal neuroanatomy (Prof Counsell), neuroimaging of brain development in CHD (Prof Counsell), multimodal neuroimaging analysis in the fetal brain (Dr Robinson & Prof Counsell).
Year 1: Undertake a literature review on fetal brain development in congenital heart disease. Develop a spatio-temporal model of brain macro and microstructural whole brain development using combined surface and volume analysis
Year 2: Investigate supervised and normative modelling techniques for comparing features derived from multimodal MRI across a large cohort of healthy and CHD fetuses.
Year 3: Assess differences in healthy and CHD fetal neurodevelopment and use this to develop MRI biomarkers of CHD outcome.