Using computational analyses to understand cognitive impairment in multiple sclerosis

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Multiple sclerosis (MS) is the most common cause of neurological disability in young adults. Cognitive impairment is common and is strongly associated with adverse outcomes including unemployment. Recent studies across a number of neurodegenerative conditions demonstrate that pathology typically occurs along networks of connected brain regions. These regions may be selectively vulnerable due to specific metabolic and cytoarchitectural characteristics. MS, long considered a multi-focal condition characterised by random pathology in the central nervous system, has recently been re-evaluated, in light of similar, network-based pathology. The present study will examine how changes in the connectivity of brain networks leads to the emergence of cognitive impairment in people with MS using a number of complementary approaches. First, we will apply Machine Learning algorithms to clinical MRI datasets to understand whether there are specific structural features that reliably characterise those with MS (generally) and those with cognitive symptoms (specifically). Second, we will examine whether these regions demonstrate changes in levels of cortical connectivity at early stages in MS. Third, we will examine whether similar changes occur in the specific pathways linking these hub regions. These findings will offer substantial new insight into the mechanisms of pathology in MS and the factors that affect employment, social function and quality of life. Throughout this project, the successful candidate will have the opportunity to work in a thriving academic environment, but also to spend time with our industrial collaborator, AInostics, developing neuroimaging analysis algorithms for the medical imaging services industry.