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 the brain makes decisions under uncertainty by studying mice navigating reward-based mazes. Rather than relying on memorisation, mice continually update mental models through active exploration. These findings improve our understanding of anxiety disorders and may inspire more adaptive artificial intelligence systems.

This research challenges the long-standing assumption that brain regions causing no errors during awake brain surgery are functionally unimportant. By measuring subtle delays in speech rather than errors alone, it introduces causal parametric mapping, offering surgeons a more sensitive way to preserve language function and improve patient outcomes.

This research develops advanced optical imaging technology to observe neurons firing in real time throughout the brain. By combining high-speed microscopy with flexible fibre-optic image relays, the system overcomes the challenge of light scattering, enabling clearer recordings of neural activity and deeper insights into brain function.

This research applies fluid mechanics, numerical simulations, and machine learning to model the brain’s waste-clearance system during sleep. By investigating how fluid moves through brain tissue and how aging or injury affect this process, the work aims to identify strategies for preventing or slowing neurodegenerative diseases such as Alzheimer's.

This research investigates the neurological causes of sleep dysfunction in people with myotonic dystrophy, a common multisystem muscular dystrophy. Using mouse models and brain activity monitoring, the study examines how diseased brains lose the ability to compensate for stress, providing new insights into sleep quality, cognition, and disease progression.

This research uses the Manhattan maze to study rapid learning and memory in mice. The study demonstrates that mice can acquire complex navigation sequences after only a few rewards, retain memories overnight, and generalize learned strategies to new mazes. The findings provide insights into few-shot learning, memory formation, and adaptive intelligence.

This research shows that pauses in information streams alter decision-making. After a break, the brain increases effort, giving greater weight to subsequent information—a “peak-after-break” effect. A computational model explains this as a performance-effort tradeoff. Findings challenge traditional theories and suggest strategic pauses can shape attention, memory, and judgment.