Normative modelling of resting-state EEG across the lifespan for application in the early diagnosis of neurodegenerative disorders including Alzheimer’s disease

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 (Alzheimer’s Disease International, 2018). 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

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. This will provide a reference model to which individual cases can be compared, allowing for inference about the likely presence or absence of a disease which is informed by richer biological and social information than typical case-control studies allow for. 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.

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