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: | Xiong Jiang |
| Institution: | Georgetown University |
| Department: | Neuroscience |
| Country: | |
| Proposed Analysis: | Title: A machine learning approach to assess brain injury using multimodal MRI data Description: Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia in older adults. There is a pressing need for biomarkers that have high sensitivity and specificity to assist the detection, diagnosis, and prognosis of early AD. In this project, we will use machine learning approaches to integrate multimodal MRI data to train classifiers to assess brain injury at an early AD stage. The classifiers will be trained with data from several public databases, then independently tested with the data from ADNI and our own cohorts at Georgetown University. |
| Additional Investigators | |
| Investigator's Name: | Richard Gallagher |
| Proposed Analysis: | Title: A machine learning approach to assess brain injury using multimodal MRI data Description: Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia in older adults. There is a pressing need for biomarkers that have high sensitivity and specificity to assist the detection, diagnosis, and prognosis of early AD. In this project, we will use machine learning approaches to integrate multimodal MRI data to train classifiers to assess brain injury at an early AD stage. The classifiers will be trained with data from several public databases, then independently tested with the data from ADNI and our own cohorts at Georgetown University. |
| Investigator's Name: | Kyle Shattuck |
| Proposed Analysis: | Title: A machine learning approach to assess brain injury using multimodal MRI data Description: Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia in older adults. There is a pressing need for biomarkers that have high sensitivity and specificity to assist the detection, diagnosis, and prognosis of early AD. In this project, we will use machine learning approaches to integrate multimodal MRI data to train classifiers to assess brain injury at an early AD stage. The classifiers will be trained with data from several public databases, then independently tested with the data from ADNI and our own cohorts at Georgetown University. |
