Modelling system-level brain structure organisation

Neurological conditions affect over 3 billion people worldwide, yet their diagnosis and treatment still heavily rely on visual inspection of brain scans. Quantitative magnetic resonance imaging (MRI) measures are underused, and the system-level relationships between different aspects of brain structure remain poorly understood. This project aims to transform our understanding of brain structure by modelling how grey matter shape, measured at multiple spatial scales, relates to white matter connectivity. Recent work shows that multiscale morphometry can reveal patterns invisible to conventional approaches, for example, ageing effects are amplified in large-scale brain structures. I hypothesise that specific types of white matter tracts (e.g. short- vs. long-range) correspond to grey matter changes at distinct scales. Using normative modelling, I will build population-level baselines of grey- and white-matter structure. I will then develop joint, system-level models to capture grey-white-matter interactions and identify individual-level deviations. These methods will be validated in epilepsy surgery, where histopathology and surgical outcomes provide a direct clinical benchmark, and applied to neurodegenerative dementias to understand how their disrupted grey-white-matter relationship relates to underlying disease mechanisms. All models and tools will be released as an open-source software toolbox, enabling broad application across neuroscience and neurology.