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The Manchester Dementia Prediction Database: Using advanced statistical methods to optimise diagnosis and prognosis for people with degenerative neurological conditions.

   Faculty of Biology, Medicine and Health

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  Dr Jennifer Thompson, Dr A Woollams, Dr Matthew Jones  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Early diagnosis and accurate prognostic information are key priorities to enable people with neurodegenerative dementia to know what to expect and to enable them to plan effectively for their future needs. The challenge we face in providing this information is that there is a great deal of individual variation, both in terms of the cognitive profile within syndromes at presentation, and in the trajectory of decline across and within individuals (e.g., Rogers et al., 2006), making it difficult to provide definitive answers. This project will apply advanced statistical techniques to a unique dataset of neuropsychological data from over 1500 patients with neurodegenerative conditions, such as Alzheimer’s disease, frontotemporal dementia and Lewy Body dementia, in order to better understand individual variation in presentation and progression. The project will draw upon previous work on Alzheimer’s Disease using data reduction techniques to understand variation (Lambon Ralph et al. 2003), and will extend this approach to diagnosis (Halai et al., 2018) and prognosis (Seghier et al., 2016). The first stage will involve analysis of neuropsychological data from a large group of individuals with neurodegenerative conditions, in order to identify underlying cognitive components, and use advanced statistical techniques to predict neurodegenerative syndromes from cognitive profiles. The next stage will quantify variability in progression using longitudinal data, and explore to what extent progression can be predicted by component scores from initial neuropsychological assessment data. The final stage will identify neural correlates of the key cognitive dimensions (Butler et al., 2014), and determine if this approach improves mappings between cognitive and neural decline. This project therefore offers the exciting opportunity to use an extensive neuropsychological dataset to reveal fundamental insights about the nature of cognitive impairment in neurodegenerative conditions and to use these results to optimise diagnosis and prognosis for people with dementia.

Entry Requirements

Candidates are expected to hold a minimum upper second class honours degree (or equivalent) in a relevant subject area, which includes psychology, neuroscience, and speech and language therapy. A Master’s degree in a related subject would be an advantage. Individuals with experience in advanced statistics, neuroimaging analysis, and/or experience with neurological patient populations are encouraged to apply.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website ( Informal enquiries may be made directly to the primary supervisor. On the online application form select PhD Medical / Clinical Science

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit

Funding Notes

Applications are invited from self-funded students. This project has a Band 2 fee. Details of our different fee bands can be found on our website ( For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website


Butler, R.A., Ralph, M.A.L., Woollams, A.M. Capturing multidimensionality in stroke aphasia: Mapping principal behavioural components to neural structures (2014) Brain, 137 (12), pp. 3248-3266.

Halai, A.D., Woollams, A.M., Lambon Ralph, M.A. Predicting the pattern and severity of chronic post-stroke language deficits from functionally-partitioned structural lesions. (2018) NeuroImage: Clinical, 19, pp. 1-13.

Lambon Ralph, M.A., Patterson, K., Graham, N., Dawson, K., Hodges, J.R. Homogeneity and heterogeneity in mild cognitive impairment and Alzheimer's disease: A cross-sectional and longitudinal study of 55 cases
(2003) Brain, 126 (11), pp. 2350-2362.

Rogers, T.T., Ivanoiu, A., Patterson, K., Hodges, J.R. Semantic memory in Alzheimer's disease and the frontotemporal dementias: A longitudinal study of 236 patients (2006) Neuropsychology, 20 (3), pp. 319-335.

Seghier, M.L., Patel, E., Prejawa, S., Ramsden, S., Selmer, A., Lim, L., Browne, R., Rae, J., Haigh, Z., Ezekiel, D., Hope, T.M.H., Leff, A.P., Price, C.J. The PLORAS Database: A data repository for Predicting Language Outcome and Recovery After Stroke (2016) NeuroImage, 124, pp. 1208-1212.
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