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: | Zhengjia Dai |
| Institution: | Sun Yat-sen University |
| Department: | Psychology |
| Country: | |
| Proposed Analysis: | Alzheimer's disease (AD) is a progressive neurodegenerative disorder that impairs patients’ cognitive function and behaviors. There were many studies about AD in both functional connectivity and structural connectivity, which have found that AD had significant abnormal connections compared with healthy controls (HC). But for the AD diagnosis, there still have no certain answers. We want to use machine learning methods, to do the classification for both functional connectivity and structural connectivity in AD, MCI and HC. We want to use the big data to find whether there is a kind of model could predict AD from HC. Further, we want to test whether the model is also suit for predict MCI from HC. If so, these large weighted features might be the core impact factor in AD. Thus, we apply for ADNI datasets. |
| Additional Investigators | |
| Investigator's Name: | Ying Lin |
| Proposed Analysis: | Alzheimer's disease (AD) is a progressive neurodegenerative disorder that impairs patients’ cognitive function and behaviors. There were many studies about AD in both functional connectivity and structural connectivity, which have found that AD had significant abnormal connections compared with healthy controls (HC). But for the AD diagnosis, there still have no certain answers. We want to use machine learning methods, to do the classification for both functional connectivity and structural connectivity in AD, MCI and HC. We want to use the big data to find whether there is a kind of model could predict AD from HC. Further, we want to test whether the model is also suit for predict MCI from HC. If so, these large weighted features might be the core impact factor in AD. Thus, we apply for ADNI datasets. |
| Investigator's Name: | Yue Gu |
| Proposed Analysis: | Alzheimer's disease (AD) is a progressive neurodegenerative disorder that impairs patients’ cognitive function and behaviors. There were many studies about AD in both functional connectivity and structural connectivity, which have found that AD had significant abnormal connections compared with healthy controls (HC). But for the AD diagnosis, there still have no certain answers. We want to use machine learning methods, to do the classification for both functional connectivity and structural connectivity in AD, MCI and HC. We want to use the big data to find whether there is a kind of model could predict AD from HC. Further, we want to test whether the model is also suit for predict MCI from HC. If so, these large weighted features might be the core impact factor in AD. Thus, we apply for ADNI datasets. |
| Investigator's Name: | Junji Ma |
| Proposed Analysis: | Alzheimer's disease (AD) is a progressive neurodegenerative disorder that impairs patients’ cognitive function and behaviors. There were many studies about AD in both functional connectivity and structural connectivity, which have found that AD had significant abnormal connections compared with healthy controls (HC). But for the AD diagnosis, there still have no certain answers. We want to use machine learning methods, to do the classification for both functional connectivity and structural connectivity in AD, MCI and HC. We want to use the big data to find whether there is a kind of model could predict AD from HC. Further, we want to test whether the model is also suit for predict MCI from HC. If so, these large weighted features might be the core impact factor in AD. Thus, we apply for ADNI datasets. |
| Investigator's Name: | Xitian Chen |
| Proposed Analysis: | Alzheimer's disease (AD) is a progressive neurodegenerative disorder that impairs patients’ cognitive function and behaviors. There were many studies about AD in both functional connectivity and structural connectivity, which have found that AD had significant abnormal connections compared with healthy controls (HC). But for the AD diagnosis, there still have no certain answers. We want to use machine learning methods, to do the classification for both functional connectivity and structural connectivity in AD, MCI and HC. We want to use the big data to find whether there is a kind of model could predict AD from HC. Further, we want to test whether the model is also suit for predict MCI from HC. If so, these large weighted features might be the core impact factor in AD. Thus, we apply for ADNI datasets. |
