Investigating early structural and functional changes to cholinergic and noradrenergic brain systems, and their relationship to APOE-e4 and Alzheimer'

Dementia is an age-related, highly debilitating condition. Around 850,000 people in the UK suffer from dementia, at an estimated cost of £26 billion annually. While research has helped us understand some of the biological changes underlying different forms of dementia, there is presently no cure, and its neurodegenerative processes appear to be largely irreversible. As a result, a major focus of current dementia research is on the early changes that occur years or even decades prior to the cognitive deficits that are presently used for diagnosis.

This PhD project will investigate two aspects of early dementia-related phenomena: (1) two neuromodulatory systems - noradrenergic and cholinergic - known to be susceptible to pathology found in Alzheimer's disease (AD) and other less common forms of dementia; and (2) genetic risk factors for AD and other dementias - in particular, carriers of the APOE-e4 allele, which confers a higher risk of developing AD in later life. We will focus on three major datasets: (1) The Human Connectome Project (HCP), which contains cross-sectional structural and functional neuroimaging, task performance, and whole-genome data from ~1200 healthy young participants. (2) The Cambridge Centre for Ageing and Neuroscience (Cam-CAN), which contains rich cognitive and imaging data from ~650 participants uniformly sampled across the adult lifespan. (3) The Alzheimer's Disease Neuroimaging Initiative (ADNI), which includes longitudinal data for ~3500 older adults across several stages of the project, with multimodal neuroimaging data, genetics, and other assessments-with an emphasis on MCI and Alzheimer's patients.

The candidate will analyse these datasets in order to assess differences in the structure, function, and multimodal connectivity between APOE-e4 carriers and non-carriers, in both younger and older cohorts. Analyses will focus on the influence of the two neuromodulatory systems of interest on these large-scale patterns, and in particular whether these influences can be observed at early age ranges. The use of multimodal approaches will help us focus more closely on the biological mechanisms underlying connectivity changes. They will learn how to process data obtained from large, publicly available datasets, containing neuroimaging, neuropsychological, clinical, and genetic information. This will involve the use of Matlab and/or Python programming languages, specialized neuroimaging software including the FSL and SPM packages, and high-performance parallel processing of data using SLURM.