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: | Anna Svenningsson |
| Institution: | Lund University |
| Department: | Department of Clinical Sciences Malmo |
| Country: | |
| Proposed Analysis: | As part of my PhD project I study cognitive resilience. Below is a short description of the research plan. Main research question What factors predict cognitive resilience defined using longitudinal cognition and longitudinal change in grey matter in non-demented cases? Datasets/participants From BioFINDER 1, cognitively unimpaired subjects and subjects with SCD/MCI, who have results from at least two MRI scans and at least two cognitive test visits, will be included. This will result in a sample of about 520 subjects. The main results in BioFINDER 1 will be replicated in non-demented cases in ADNI. Variables For defining resilience: MRI: Cortical thickness (whole brain and AD signature cortex). Cognition: Composite score ("Modified PACC5) consisting of MMSE, ADAS delayed recall (weighted twice), Trailmaking test B, and Animal fluency. Possible predictors of resilience: Demographics: Age at baseline, sex, years of education Genetics: APOE, KLOTHO, polygenic risk scores (for education, IQ, AD) CSF biomarkers: AD pathology (amyloid-beta and tau), synaptic markers, inflammatory markers Imaging biomarkers: vascular pathology (WML volume), DTI measures Methods 1. Plotting cortical thickness against time for each individual and calculating the regression coefficient. 2. Plotting cognitive composite score against time for each individual and calculating the regression coefficient. 3. Plotting the regression coefficient for change in cognition against the regression coefficient for change in cortical thickness for all participants (and CU/SCD and MCI separately), running a linear regression analysis on this and saving the residuals. The residuals will be used as measures of cognitive resilience against atrophy, ie better/worse cognitive trajectory than expected given the level of atrophy, either as continuous measures or by dividing the subjects into groups based on their residuals. 4. Compare the prevalence/levels of possible predictors of resilience (listed above) in the different groups or do correlation analyses between predictors and residuals. |
| Additional Investigators |
