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: | Tetsutaro Ono |
| Institution: | Dai Nippon Printing |
| Department: | Information Innovation Operations |
| Country: | |
| Proposed Analysis: | The aim of this study is to find an effective indicator from magnetic resonance (MR) images for early diagnosing Alzheimer’s disease (AD) and predicting of a risk of converting from mild cognitive impairment (MCI) to AD. We are planning to combine outputs of several imaging processing techniques such as VBM (Voxel-base morphometry), TBM (Tensor-based morphometry), volume measuring, network connectivity analysis, and to evaluate which combination is best by using machine learning. As we have conducted another similar research using Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) dataset, this study is also aimed to validate its results and to improve it by increasing a size of subjects. |
| Additional Investigators | |
| Investigator's Name: | Tomoaki Goto |
| Proposed Analysis: | The aim of this study is to find an effective indicator from magnetic resonance (MR) images for early diagnosing Alzheimer’s disease (AD) and predicting of a risk of converting from mild cognitive impairment (MCI) to AD. We are planning to combine outputs of several imaging processing techniques such as VBM (Voxel-base morphometry), TBM (Tensor-based morphometry), volume measuring, network connectivity analysis, and to evaluate which combination is best by using machine learning. As we have conducted another similar research using Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) dataset, this study is also aimed to validate its results and to improve it by increasing a size of subjects. |
| Investigator's Name: | Keiko Shigemori |
| Proposed Analysis: | The aim of this study is to find an effective indicator from magnetic resonance (MR) images for early diagnosing Alzheimer’s disease (AD) and predicting of a risk of converting from mild cognitive impairment (MCI) to AD. We are planning to combine outputs of several imaging processing techniques such as VBM (Voxel-base morphometry), TBM (Tensor-based morphometry), volume measuring, network connectivity analysis, and to evaluate which combination is best by using machine learning. As we have conducted another similar research using Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) dataset, this study is also aimed to validate its results and to improve it by increasing a size of subjects. |
| Investigator's Name: | Kyoichi Jinbo |
| Proposed Analysis: | The aim of this study is to find an effective indicator from magnetic resonance (MR) images for early diagnosing Alzheimer’s disease (AD) and predicting of a risk of converting from mild cognitive impairment (MCI) to AD. We are planning to combine outputs of several imaging processing techniques such as VBM (Voxel-base morphometry), TBM (Tensor-based morphometry), volume measuring, network connectivity analysis, and to evaluate which combination is best by using machine learning. As we have conducted another similar research using Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) dataset, this study is also aimed to validate its results and to improve it by increasing a size of subjects. |
| Investigator's Name: | Takahiro Tanaka |
| Proposed Analysis: | The aim of this study is to find an effective indicator from magnetic resonance (MR) images for early diagnosing Alzheimer’s disease (AD) and predicting of a risk of converting from mild cognitive impairment (MCI) to AD. We are planning to combine outputs of several imaging processing techniques such as VBM (Voxel-base morphometry), TBM (Tensor-based morphometry), volume measuring, network connectivity analysis, and to evaluate which combination is best by using machine learning. As we have conducted another similar research using Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) dataset, this study is also aimed to validate its results and to improve it by increasing a size of subjects. UPDATED (2019-11-18) :The aim of this study is to find an effective indicator from magnetic resonance (MR) images for early diagnosing Alzheimer’s disease (AD) and predicting of a risk of converting from mild cognitive impairment (MCI) to AD. We are planning to combine outputs of several imaging processing techniques such as VBM (Voxel-base morphometry), TBM (Tensor-based morphometry), volume measuring, network connectivity analysis, and to evaluate which combination is best by using machine learning. As we have conducted another similar research using Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) dataset, this study is also aimed to validate its results and to improve it by increasing a size of subjects. |
