Early fetal and infant neurodevelopment is a critical period, in which the human brain undergoes rapid growth and folding, underpinned by complex processes of cellular maturation and migration. Perturbations to this process, either through genetic or environmental influences can have profound implications. Better modelling of the causal impact of these processes is therefore vital, if we are to predict which infants are more likely to develop long-term neurodevelopmental difficulties.
Classical approaches to human brain analysis lack the precision needed to tailor predictive models to individual brains; however new AI techniques are opening a door towards sophisticated generative models of a range of neurological processes (Bass C NeurIPS 2020; Da Silva M. MLCN 2021; Fawaz A MIUA 2022)
This project will develop AI-driven mechanistic models of neurodevelopment, using MRI imaging data collected from individuals at multiple timepoints from fetus to 3-4 years of age. It will benefit from access to the Brain Imaging in Babies Study (BIBS), affiliated with AIMS2-trials, which recruits individuals at higher likelihood of later neurodevelopmental difficulties, such as autism.
Years 1-2: Train in machine learning and cortical surface processing, for analysing human brain data; harmonise image processing across scan timepoints and build normative models of neurodevelopmental trajectories for different brain regions.
Year 2-3: Extend existing deep generative models of neonatal neurodevelopment (Fawaz A MIUA) with causal networks. This will support tailored simulation of neurodevelopmental trajectories from fetus to 4 years and offer mechanistic insight into which environmental and genetic risk factors most impact brain appearance. In this way it will be possible to suggest new biomarkers predictive of children’s neurodevelopmental outcomes at 4 years.
Year 4 – 6 months will be reserved for write up.