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: | Hebert Caballero |
Institution: | University of Alberta |
Department: | Neuroscience |
Country: | |
Proposed Analysis: | We will analyze cognitive trajectories across the Alzheimer's disease (AD) spectrum. Specifically, we will examine episodic memory trajectories in non-demented, mild cognitive impairment, and AD participants. Genetic (e.g., APOE), neuroimaging (e.g., amyloid PET), CSF (e.g., total tau), and other available biomarkers in ADNI will be used to predict these trajectories using machine learning. Specifically, we will use random forest analysis to detect important biomarker predictors in each of the stages of the AD spectrum. |
Additional Investigators | |
Investigator's Name: | Roger Dixon |
Proposed Analysis: | We will analyze cognitive trajectories across the Alzheimer's disease (AD) spectrum. Specifically, we will analyze episodic memory trajectories in non-demented, mild cognitive impairment, and AD participants. Genetic (e.g., APOE), neuroimaging (e.g., amyloid PET), CSF (e.g., total tau), and other available biomarkers in ADNI will be used to predict these trajectories using machine learning. Specifically, we will use random forest analysis to detect important biomarker predictors in each of the stages of the AD spectrum. |
Investigator's Name: | Georgia McFall |
Proposed Analysis: | We will analyze cognitive trajectories across the Alzheimer's disease (AD) spectrum. Specifically, we will analyze episodic memory trajectories in non-demented, mild cognitive impairment, and AD participants. Genetic (e.g., APOE), neuroimaging (e.g., amyloid PET), CSF (e.g., total tau), and other available biomarkers in ADNI will be used to predict these trajectories using machine learning. Specifically, we will use random forest analysis to detect important biomarker predictors in each of the stages of the AD spectrum. |