In recent years there has been a remarkable progress in our understanding of the brain as a multi-scale networked system. These advances result from the combination of new experimental and imaging techniques with advanced mathematical and computational methods. Network modelling, where we use nodes and links (mathematically, a graph) to represent a very large and complex system, is particularly well-suited to the study of the human brain at different scales. New developments in so-called network neuroscience open up exciting new opportunities for the structural and dynamic study of the brain.
The aim of this interdisciplinary project between mathematicians and clinicians is to adapt and develop these recent mathematical and computational advancements in network neuroscience to investigate the structural and functional brain connectivity in newborns. The research focuses initially on a specific condition called hypoxic-ischaemic encephalopathy (HIE), caused by poor oxygen delivery to the brain around the time of birth. However, we expect the work developed in this research project to serve as a model for a range of neurological disorders beyond HIE.
The methodology is based on network modelling but incorporates recent advances beyond classical network science, including extensions to multilayer networks, and higher-order relations (simplicial complexes) to capture more complex topologies of models of the brain at different scales.
The clinical team, working with the Neonatal Unit at Princess Anne Hospital, University Hospital Southampton, have extensive clinical expertise on HIE, and provide high-quality neonatal clinical imaging (MRI) data sets for this project.
The overall objective of the project is to demonstrate the value of network-based and topology-based modelling in neuroimaging, which could be applied to many neurological conditions and diseases. From a clinical point of view for this project, it is to understand the relationship between brain development, by examining MRI data from both neonates and 6-8 year olds, and long-term neurological and neuro-developmental function. The general aim will be identify associations between brain network architecture/topology and cognitive and behavioural function.
The prospective candidate will have a degree in Mathematics, Bioinformatics, Computer Science, Physics or related field, with a background and/or interest in network modelling, topology and discrete mathematics. Some programming experience in a programming or numerical computing environment such as MATLAB, Python or R, and an interest in neuroscience or biomedical imaging, are desirable. An enthusiasm for real-world applications of complex mathematical ideas and a positive attitude towards interdisciplinary research are essential. Dr Vollmer, Dr Darekar and Dr Lennartsson will provide support and training in the clinical and imaging aspects of the project, while Dr Sanchez-Garcia and Professor MacArthur will lead the mathematical and data analytic aspects. At the end of the PhD programme, the student will have gained proficiency in a unique set of clinical, imaging, mathematical, and data analytical skills and valuable experience working with biomedical data in a collaborative and interdisciplinary environment, and therefore increasing her/his employability in very sought-after set of skills, both for future academic and industry jobs.