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: | Yuxin Yang |
| Institution: | Binghamton University |
| Department: | Industrial and System Engineering |
| Country: | |
| Proposed Analysis: | We would like to apply the machine learning method to help the early detection of Alzheimer’s Disease (AD) since identification in the early stages is still a difficult task in medical practice. Patients develop mild cognitive impairment (MCI) first and then the patients who have a progressive mild cognitive impairment (pMCI) are associated with an increased risk of AD compared with patients who have a stable mild cognitive impairment (sMCI). The objective of this study is to develop efficient feature extraction and classification techniques in the early detection of AD to help in slowing down its progression. This work has the potential to be published in a journal paper. |
| Additional Investigators | |
| Investigator's Name: | Abdelrahman Farrag |
| Proposed Analysis: | We would like to apply the machine learning method to help the early detection of Alzheimer’s Disease (AD) since identification in the early stages is still a difficult task in medical practice. Patients develop mild cognitive impairment (MCI) first and then the patients who have a progressive mild cognitive impairment (pMCI) are associated with an increased risk of AD compared with patients who have a stable mild cognitive impairment (sMCI). The objective of this study is to develop efficient feature extraction and classification techniques in the early detection of AD to help in slowing down its progression. This work has the potential to be published in a journal paper. |
| Investigator's Name: | Zhenxuan Zhang |
| Proposed Analysis: | We would like to apply the machine learning method to help the early detection of Alzheimer’s Disease (AD) since identification in the early stages is still a difficult task in medical practice. Patients develop mild cognitive impairment (MCI) first and then the patients who have a progressive mild cognitive impairment (pMCI) are associated with an increased risk of AD compared with patients who have a stable mild cognitive impairment (sMCI). The objective of this study is to develop efficient feature extraction and classification techniques in the early detection of AD to help in slowing down its progression. This work has the potential to be published in a journal paper. |
| Investigator's Name: | Zhenxuan Zhang |
| Proposed Analysis: | We would like to apply the machine learning method to help the early detection of Alzheimer’s Disease (AD) since identification in the early stages is still a difficult task in medical practice. Patients develop mild cognitive impairment (MCI) first and then the patients who have a progressive mild cognitive impairment (pMCI) are associated with an increased risk of AD compared with patients who have a stable mild cognitive impairment (sMCI). The objective of this study is to develop efficient feature extraction and classification techniques in the early detection of AD to help in slowing down its progression. This work has the potential to be published in a journal paper. |
