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

When people seek help concerning noticeable cognitive decline, rapid diagnosis and accurate prognosis are key priorities to give people with neurodegenerative conditions knowledge about what to expect and 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 database of neuropsychological data from over 1500 patients with neurodegenerative conditions in order to better understand individual variation in presentation and progression. It will draw upon work previous work on Alzheimer’s Disease using data reduction techniques to understand variation (Lambon Ralph et al. 2003), and extend this approach to apply the results to diagnosis (Halai et al., 2018) and prognosis (Seghier et al., 2016), following advances in the field of stroke aphasia.

The first arm of the project will apply principal components analysis to initial neuropsychological screening data across a large group of individuals with a variety of neurodegenerative conditions, which will distil the underlying cognitive components affected. By then delineating the range of individual variation along these key dimensions, we can construct a model to predict syndrome type via regression techniques. The second arm of the project will quantify variability in progression within syndromes using the available longitudinal data, and explore to what extent this can be predicted by component scores derived from initial assessment data. The final arm of the project will identify the neural correlates of the key cognitive dimensions (Butler et al., 2014), and determine if this approach improves mappings between behavioural and neural decline. It will also explore if integrity of these functionally partitioned areas can improve behavioural diagnostic and prognostic models, based on our current work in stroke demonstrating the superiority of this approach over standard neuroanatomic parcellations. 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.

https://www.research.manchester.ac.uk/portal/anna.woollams.html#
http://www.cerebralfunctionunit.co.uk/mattjones.html
http://www.cerebralfunctionunit.co.uk/jenniferthompson.html


Entry requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.