We apply cutting-edge data science approaches to two core scientific goals: 1) to develop a better understanding of the brain across the lifespan, in both health and disease; and 2) to predict development, cognition and mental health, at the individual level.

The methods we use include network science, machine learning and Natural Language Processing. Importantly however, our work is scientifically driven- focusing first on the two scientific goals above and then developing methods tailored to our questions of interest. We work with a range of data modalities, including brain MRI, speech, genetics and genomics and behavioural and cognitive data.

Key recent contributions include work relating brain connectivity markers of schizophrenia to transcriptomic data, to shed fresh light on the biological mechanisms underlying the condition; and a new tool for mapping semantic speech graphs which showed speech graphs from first episode psychosis patients were more fragmented than healthy control subjects. The latter was developed as part of a project at The Alan Turing Institute, exploring the potential of speech data to predict outcome for patients with psychotic disorders.


Below are a few key publications. For a full list of publications, please see here.