With a rapidly aging human population worldwide, neurodegenerative diseases represent a pressing medical and societal concern. Given the increase in life expectancy, dementia is expected to affect almost 152 million people by 2050.
Currently, there is no cure for many age-related diseases including Alzheimer’s disease (AD), but early intervention can meaningfully delay neurodegeneration. For this reason, developing techniques for early and accurate diagnosis is critical and novel biomarkers can facilitate the development and validation of new therapies.
Current AD biomarkers rely on measures from cerebrospinal fluid (CSF), positron emission tomography (PET) and/or magnetic resonance imaging (MRI). However, these techniques are relatively invasive, expensive, and often difficult to access.
Electroencephalography (EEG) represents a promising low-cost, non-invasive alternative that is already implemented in healthcare systems worldwide and has been shown to reliably dissociate AD patients from healthy controls (Benwell et al., 2020; Flores-Sandoval et al., 2023). However, to date EEG studies have been limited to case-control designs focussing on average differences between patients and healthy participants.
Unfortunately, because AD represents a highly heterogeneous disorder with marked individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, previous EEG studies may have been hindered in their attempt to identify sensitive and specific biomarkers.
Research Opportunity
Here, we propose to adopt a ‘normative modelling’ approach to map trajectories of EEG activity over the lifespan whilst accounting for key variables such as sex, level of education, socioeconomic status and more.
Existing and new resting-state EEG data will be combined to develop the model, and a key outcome of the project will be an open-source Python toolbox which will allow for iterative growth of the model by researchers around the world for application in improved diagnosis and tracking of various neurodegenerative diseases, including AD but also potentially many more.
The PhD candidate will gain extensive knowledge and skills in neuropsychology, neurophysiology, computational modelling and coding in both Matlab and Python. They will collaborate with researchers at universities across the UK (Glasgow, Bath, Cardiff) and Harvard Medical School in the US. Other spin-off projects in line with the interests and skills of the candidate will be possible. The candidate will join the vibrant cognitive neuroscience community in Psychology at the University of Dundee and will be part of a very friendly and supportive team.
Programme Structure
The psychologists work on world-leading research across five key areas:
- Clinical
- Cognitive
- Cognitive neuroscience
- Developmental
- Social
Admission requirements
Academic requirements
GPA: Upper Second Class
English requirements
IELTS: 6.5
Other requirements
General requirements
- Honours degree at 2.1 or above in a relevant discipline.
- In certain circumstances we may ask you to spend a year completing our Master's degree in Psychological Research Methods before you commence your doctoral research. The decision about the suitability of your qualifications is made by the potential supervisor and the schools postgraduate advisor.
- IELTS 6.5
Tuition Fees
Tuition fees are shown in AUD and the most likely applicable fee is shown based on your nationality.
- International: 41,423 AUD / year (≈ 21,940 GBP / year)
- Domestic: 9,451 AUD / year (≈ 5,006 GBP / year)
Living costs
Dundee, United Kingdom: 1,486-2,821 AUD / month. The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
