Identifying complex spatio-temporal biomarkers of brain diseases

Bayesian models today are providing the tools to explore the complexity of brain architecture. Therefore, there is a crucial need for leading researchers with an in-depth comprehension of the current challenges in neuroscience and the quantitative skills to develop cutting-edge solutions. 

The aim of this project is the development of a modelling framework for the identification of new, complex spatio-temporal brain patterns which can improve our understanding of the functional activity of the brain, our ability to identify early signs of brain diseases and the prediction of their prognosis. In addition, further timely neuroscientific challenges such as the identification of inter-individual variations in brain responses and the inclusion of multiple covariates (e.g. laboratory and clinical) in the identification of complex biomarkers of brain diseases will be researched during the project. 

 The methodologies developed in the project will provide neuroscientists with innovative analytical tools that will contribute to neuroscientific research on a wide range of brain conditions, from developmental to neurodegenrative diseases, which affect millions of people in the UK and worldwide. 

The student will acquire advanced modelling skills in the research areas of Bayesian nonparametrics and functional data analysis which will be essential to develop the innovative modelling framework. New methods will be tested on well-known publicly available neuroscientific datasets and results presented at international conferences in both statistics and neuroscience. The student will be also involved in the development of R packages that will allow immediate access to all methods developed in this project to the wider scientific community.