This dissertation develops brain-charting methods to support precision psychiatry and neurology by measuring how an individual’s brain differs from population norms. Using normative modelling, it replaces conventional case-control averages with personalised deviation scores across age and disease progression. The research introduces warped normative models for non-Gaussian imaging data and multivariate extreme-value methods for identifying unusual patterns across brain regions. Applications to rare copy-number variants and Parkinson’s disease reveal individual differences obscured by group averages. The defence also examines longitudinal monitoring, environmental influences, resilience, ethics, stigma and clinical implementation, while emphasising that brain deviations alone cannot define pathology or determine treatment.

This research investigates how Amazonian butterflies evolve their visual systems to match the light conditions of different rainforest niches. By comparing eye and brain structures across many species, it reveals that evolution repeatedly finds the same sensory solutions, showing that adaptation can be surprisingly predictable and may drive the formation of new species.