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: | Eduardo Castro |
| Institution: | IBM Research |
| Department: | Digital Health |
| Country: | |
| Proposed Analysis: | We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) and clinical measures using multivariate and multimodal predictive models to a) detect brain-based longitudinal patterns of Alzheimer's disease trajectories, and b) detect clinical heterogeneity within Alzheimer's and brain markers associated to these differences |
| Additional Investigators | |
| Investigator's Name: | Guillermo Cecchi |
| Proposed Analysis: | We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) and clinical measures using multivariate and multimodal predictive models to a) detect brain-based longitudinal patterns of Alzheimer's disease trajectories, and b) detect clinical heterogeneity within Alzheimer's and brain markers associated to these differences |
| Investigator's Name: | Pablo Polosecki |
| Proposed Analysis: | We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) and clinical measures using multivariate and multimodal predictive models to a) detect brain-based longitudinal patterns of Alzheimer's disease trajectories, and b) detect clinical heterogeneity within Alzheimer's and brain markers associated to these differences |
| Investigator's Name: | Jenna Reinen |
| Proposed Analysis: | We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) using multivariate and multimodal predictive models to detect a progression invariant marker of Alzheimer’s disease. We will validate such marker by verifying that it is a good predictor of cognitive impairment, as measured by available clinical assessments such as the Mini-Mental State Examination |
| Investigator's Name: | Amit Dhurandhar |
| Proposed Analysis: | We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) using multivariate and multimodal predictive models to detect a progression invariant marker of Alzheimer’s disease. We will validate such marker by verifying that, when coupled with age, it becomes a good predictor of cognitive impairment as measured by available clinical assessments such as the Mini-Mental State Examination. |
| Investigator's Name: | Hongyang Li |
| Proposed Analysis: | We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) using multivariate and multimodal predictive models to detect a progression invariant marker of Alzheimer’s disease. We will validate such marker by verifying that, when coupled with age, it becomes a good predictor of cognitive impairment as measured by available clinical assessments such as the Mini-Mental State Examination. |
| Investigator's Name: | Anushree Mehta |
| Proposed Analysis: | We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) using multivariate and multimodal predictive models to detect a progression invariant marker of Alzheimer’s disease. We will validate such marker by verifying that, when coupled with age, it becomes a good predictor of cognitive impairment as measured by available clinical assessments such as the Mini-Mental State Examination. |
