Modelling dementia progression based on machine learning and simulations
Award Number
MR/T004347/1Programme
Research GrantStatus / Stage
ActiveDates
1 April 2019 -31 March 2022
Duration (calculated)
02 years 11 monthsFunder(s)
MRC (UKRI)Funding Amount
£304,844.34Funder/Grant study page
MRC UKRIContracted Centre
Newcastle UniversityPrincipal Investigator
Marcus KaiserPI Contact
Marcus KaiserWHO Catergories
Models of DiseaseTools and methodologies for interventions
Understanding Underlying Disease
Disease Type
Dementia (Unspecified)CPEC Review Info
| Reference ID | 238 |
|---|---|
| Researcher | Reside Team |
| Published | 12/06/2023 |
Data
| Award Number | MR/T004347/1 |
|---|---|
| Status / Stage | Active |
| Start Date | 20190401 |
| End Date | 20220331 |
| Duration (calculated) | 02 years 11 months |
| Funder/Grant study page | MRC UKRI |
| Contracted Centre | Newcastle University |
| Funding Amount | £304,844.34 |
Abstract
Our basic strategy is to 1) develop machine learning and dynamic model with available public datasets (ADNI, DPUK, Newcastle data) and 2) validate them with the against other cohorts (UK Biobank and Korea data). In this project, we will combine neuroimaging dataset including DWI, resting-sate fMRI, and PET data. DEVELOPING MACHINE LEARNING APPROACHES Using structural connectivity as measured with diffusion MRI in prodromal dementia, starting with connectivity in healthy subjects, and using a computer simulation to study the progression over time towards healthy ageing or dementia, we found that pathophysiological alterations associated with dementia become significantly apparent before the onset of symptoms, meeting diagnostic criteria for clinical dementia, indicating a potential biomarker for progression towards dementia. This machine learning approach could be improved further by using deep learning. Indeed, Korea University, in addition to providing further training and test datasets, has already developed a deep learning approach for dementia brain connectivity data. We will extend these approaches to develop a model of disease progression, looking at changes in white matter and gray matter organization, and testing the role of different underlying biological mechanisms through computational modelling. VALIDATING OUR APPROACH IN A CLINICAL SETTING This study utilizes existing datasets for training our approach. We use severable studies to ensure that predicting disease progression is reliable across study sites (UK vs. Korea) and patient cohorts. Note that studies, in addition to neuroimaging data, include the complete set of cognitive and clinical scores (MMSE, CAMCOG, UPDRS-III, NPI-Hall, CAF, Cornell-DS) as well as medication information. In a second step, we will use other datasets (UK Biobank and data from Korea University) as test datasets to see whether our approach can deal with novel datasets.
Aims
Going beyond machine learning subtype classification, our study aims to develop a simulation-based model of disease progression that can become a standard clinical tool to predict future disease progression of individual patients and to facilitate early treatment of the disease leading to improved outcomes for patients and reduced overall healthcare costs.