There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Sara Wade |
| Institution: | University of Warwick |
| Department: | School of Mathematics, University of Edinburgh |
| Country: | |
| Proposed Analysis: | In a clinical trial setting, designed to test the effectiveness of any proposed drugs or therapies for Alzheimer's disease, accurate tools are needed for monitoring disease progression, as inclusion criteria, and for disease-staging. Clinical measures, such as the mini-mental state exam (MMSE) or the Alzheimer's disease assessment scale-cognitive subscale (ADAS-cog), are traditionally used as outcome measures but suffer from high variation, low sensitivity to changes in early disease, and long follow-up times. Neuroimaging or biological biomarkers may be better suited to clinical trials as outcome measures to monitor disease progression, particularly in early stages of the disease when drugs or therapies tend to be most effective. However, in order to validate their use in clinical trials, the clinical significance of short term changes in imaging or fluid biomarkers needs to be understood. The aim of this study is to use AD biomarker data to predict long term cognitive decline. A Bayesian nonparametric longitudinal model is developed to jointly model clinical measures for long term decline and imaging and fluid markers. The advantages of the model include its ability to incorporate hypothetical biomarker dynamics; data driven complexity; and ability to capture the variability in patient specific trajectories due to various unobserved factors, such as enhanced cognitive reserve or lifestyle choices. Due to the high number of biomarkers, latent factor models and priors for sparse precision matrices are considered to reduce dimension of the latent parameter space. We develop a MCMC algorithm for posterior inference and investigate MAP approaches to speed up computations. |
| Additional Investigators |
