ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Carlos Gomes |
| Institution: | Cornell University |
| Department: | Human Development |
| Country: | |
| Proposed Analysis: | The data will be used for research purposes. The research will focus on clinical memory instruments that are used in the diagnosis of cognitive impairment and dementia in older adults. A key limitation of those instruments is that they measure only raw recall performance and do not decompose it into specific retrieval processes that control recall. This means that they are inherently noisy measures because only some retrieval processes are tied to impairment and dementia. Decomposition can be accomplished with a mathematical modeling technique, the dual-recall model, that was developed in the Memory & Neuroscience lab at Cornell University, directed by Dr Brainerd. Applying the model to clinical memory instruments using data sets that provide samples of three diagnostic groups (healthy control [HC]; mild cognitive impairment [MCI]; Alzheimer’s dementia [AD]) will allow us to: (1) pinpoint the specific retrieval processes that actually differentiate these diagnostic groups; (2) improve the ability of memory instruments to diagnose MCI and AD and to predict longitudinal transitions to those conditions; (3) compare the levels of diagnostic separation and prediction that are achieved by measuring component retrieval processes to the corresponding levels that are achieved by genetic markers, other biomarkers, and other behavioral markers; (4) develop individualized scoring methods for clinical application (i.e., procedures that use memory instruments to assign scores for component retrieval processes to individual subjects); (5) test theoretical hypotheses about relations among different retrieval processes in HC, MCI, and AD groups. |
| Additional Investigators | |
| Investigator's Name: | Charles Brainerd |
| Proposed Analysis: | The research will focus on clinical memory instruments that are used in the diagnosis of cognitive impairment and dementia in older adults. A key limitation of those instruments is that they measure only raw recall performance and do not decompose it into specific retrieval processes that control recall. This means that they are inherently noisy measures because only some retrieval processes are tied to impairment and dementia. Decomposition can be accomplished with a mathematical modeling technique, the dual-recall model, that was developed in my lab lab. Applying it to clinical memory instruments using data sets that provide samples of three diagnostic groups (healthy control [HC]; mild cognitive impairment [MCI]; Alzheimer’s dementia [AD]) will allow us to: (1) pinpoint the specific retrieval processes that actually differentiate these diagnostic groups; (2) improve the ability of memory instruments to diagnose MCI and AD and to predict longitudinal transitions to those conditions; (3) compare the levels of diagnostic separation and prediction that are achieved by measuring component retrieval processes to the corresponding levels that are achieved by genetic markers, other biomarkers, and other behavioral markers; (4) develop individualized scoring methods for clinical application (i.e., procedures that use memory instruments to assign scores for component retrieval processes to individual subjects); (5) test theoretical hypotheses about relations among different retrieval processes in HC, MCI, and AD groups. |