More than 900,000 people in the UK have dementia (Alzheimer’s Research UK, 2024), while worldwide this number is estimated to be greater than 55 million (World Health Organisation, 2023).

A prominent issue in this field and in neuropsychology more broadly (which focuses on understanding the links between brain and behaviour), is that when attempting to understand the impact of neurological disorders on brain function, the tools available for interrogating and interpreting functional neuroimaging data (for example functional magnetic resonance imaging [fMRI]) and electrophysiological recordings [magneto/electroencephalography]) have notable limitations.

Particularly, there is substantial room for advancement and optimisation of processing and analysis pipelines. The shortcomings in evaluating and characterising brain-network dysfunction severely limits our understanding of disease symptomology and its neural basis, of disease progression, and it restricts the evaluation of future interventions that aim to demonstrate changes in brain function. Yet, with ever increasing accessibility to large, high-quality datasets there is an unprecedented opportunity to make progress via the merging of neuropsychology, neuroscience and advanced data science methods to overcome these challenges.

Key problems that arise, for example, are that the neuroimaging scans are often short, in the region of 5 or 6 minutes long. The limited amount of data that is collected and available means that extracting reliable and reproducible brain-network metrics from the recordings can be challenging.

This PhD project aims to address the issues mentioned above and will focus on developing enhanced methods for analysing and assessing neuroimaging data. The project will investigate which combinations of preprocessing choices lead to the most robust brain network estimates for the classification of cognitive decline relating to dementia. The PhD project will also focus on developing new methods to interrogate the networks and to amplify signal of interest and to minimise noise.

Interdisciplinary PhD project

We invite you to apply for this cutting-edge interdisciplinary PhD project, that bridges neuropsychology, neuroimaging, and data-science.

The supervisory team will include Dr McGeown, who is an academic neuropsychologist based within the Department of Psychological Sciences & Health, with expertise in neuropsychology, neuroimaging techniques, and dementia. Dr McGeown leads both the Laboratory for Neuroanalytics and the Dementia Research Network at University of Strathclyde.

Interdisciplinary support is provided via Dr Smith (Department of Computer & Information Sciences), who offers expertise in advanced computational analysis pipelines and graph theory for brain-networks, and Dr Clark (Department of Electronic & Electrical Engineering), who specialises in network-based analysis methods.

The student will also actively participate in collaborative networks such as the Scottish Imaging Network (SINAPSE) and the Scottish Dementia Research Consortium, to further develop their knowledge, skills and networks.