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: | Louisa Thompson |
| Institution: | Brown University Medical School |
| Department: | Psychiatry and Human Behavior |
| Country: | |
| Proposed Analysis: | Subjective cognitive decline (SCD), generally defined as the perception of worsening cognitive performance in the absence of objectively observed cognitive deficits, may be an important feature of early stage AD (Jessen et al. 2014; Mitchell et al. 2014; Yates et al. 2015). However, the same features that lead many to assert that SCD could be a risk marker for preclinical AD, have also led to challenges for research on this topic. One such complication is that SCD often worsens with stress and commonly co-occurs with anxiety and depression (Yates et al., 2017). While anxiety and depression have been associated with increased risk for AD, they are by no means specific to AD and are known to have independent deleterious effects on cognition and daily functioning for individuals throughout the lifespan (Modrego & Ferrandez, 2004; Green et al., 2003). The utility of SCD and how to measure it is therefore not surprisingly a topic of some debate, and more research is needed (Molinuevo et al., 2017). The goal of the proposed research is to parse the unique and shared variance of SCD (captured by a single GDS item) and self-reported depression symptoms (GDS total score) in the prediction of AD biomarkers, namely amyloid PET SUVr, and reported functional impaired. We predict that depression symptoms will explain a degree of unique variance in amyloid PET SUVr, supporting the argument that it is important to include both depression and SCD measures in AD predictive models. We also predict that depression and SCD will be associated with different patterns of responding on functional impairment measures. |
| Additional Investigators | |
| Investigator's Name: | Leslie Brick |
| Proposed Analysis: | Our NIH funded research project is developing an AD risk algorithm to predict cerebral amyloid status from readily available clinical variables, including family history, medical conditions, and self-reported cognitive and functional status. This project has been using ADNI and other open datasets for training our machine learning model and will continue to access the ADNI data for this purpose throughout the remainder of this year. |
