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: | Shannon Drouin |
| Institution: | University of Alberta |
| Department: | Psychology |
| Country: | |
| Proposed Analysis: | We are requesting access to ADNI imaging, clinical, and biomarker data for the purposes of conducting an investigation of memory resilience in cognitively normal older adults. As a PhD student at the University of Alberta and a Canadian Consortium on Neurodegeneration in Aging trainee (Team 9: Biomarkers), I have trained in machine learning and related data-driven modeling applications to longitudinal data on brain/cognitive aging and neurodegenerative disease. In addition to contributions from my supervisor (Dr. Roger A Dixon), collaborators on the research team provide complementary expertise in imaging (Drs. Pierre Bellec, Montreal; Simon Duchesne, Laval), other biomarkers (Dr. Mario Masellis, Toronto), and resilience-related clinical risk factors (Dr. Kaarin Anstey, UNSW). Two specific aims guide the planned analyses. First, we will use both multi-wave ADNI imaging (MRI, normalized for head size, sex, scanner type and scanner site) and clinical (cognitive assessments) data to classify memory resilience, operationally defined as maintained memory despite longitudinal evidence of substantial hippocampal atrophy. Specifically, we will apply latent class growth analyses (Mplus 8.2) to longitudinal MRI and cognitive trajectory data to (1) identify a group of older adults with advancing hippocampal (right and left) atrophy, and (2) identify a subgroup of older adults with maintained memory trajectories within the atrophy group. Second, we will use baseline clinical (AD-related risk and protective factors, including demographics, vital signs, screening labs, medications) and biomarker (from plasma and CSF) data available within ADNI to test multi-modal predictions of memory resilience using random forest classification models (sklearn, Python 3.9). Our research plans, data preparation, and analytic procedures are grounded in previous research in our labs. Our overall goal is to investigate ‘memory resilience’ as a potential signal of AD avoidance or pathway to healthier brain aging. The current approach plans to elucidate the (1) complex and interactive nature of asymptomatic brain and cognitive aging and (2) pathways of memory resilience to AD-related adversity. |
| Additional Investigators |
