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 develops automated tools to identify psychedelic-inspired compounds that restore lost neural connections associated with depression, anxiety, and addiction. Using advanced imaging and custom analysis software, the project screens potential therapeutics that promote neuronal growth, aiming to create treatments that repair brain circuitry rather than simply managing symptoms.

Psychiatric symptoms often precede neurodegenerative diseases, but the biological link remains unclear. This research examines the FMR1 gene using postmortem brain tissue to uncover shared molecular mechanisms, aiming to predict neurodegeneration earlier, improve treatment strategies, and reframe psychiatric symptoms as potential early warning signs.