ADNI data is made available to researchers around the world. As such, there are many active research projects accessing and applying the shared ADNI data. To further encourage Alzheimer’s disease research collaboration, and to help prevent duplicate efforts, the list below shows the specific research focus of the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Namita Aggarwal |
| Institution: | Jawaharlal Nehru University |
| Department: | School of Computer and System Science |
| Country: | |
| Proposed Analysis: | The main aim of our study is to discriminate healthy people from those with Alzheimer’s disease (AD) based on T1 weighted whole brain Magnetic Resonance images (MRI). The brain degeneration is difficult to diagnose visually, which makes the task of manual interpretation difficult. Hence, computer aided analysis, diagnosis and classification of MRI brain images may prove useful for better diagnosis of Alzheimer’s disease. However, MR images as such cannot directly be used for diagnosis. We need to extract relevant features from the data. Various feature extraction methods have been proposed in different domains of real time decision systems. We want to improve diagnosis of AD using appropriate feature extraction techniques. This will allow carrying out computer-aided-diagnosis in faster and accurate way in comparison to manual diagnosis. To evaluate our decision model, there is a requirement of MRI images of normal and abnormal subjects. For this, we require T1-3D MRI data from ADNI. We will be thankful to you for your kind support, which we would like to acknowledge in all our future publications with your permission. |
| Additional Investigators | |
| Investigator's Name: | Bharti Rana |
| Proposed Analysis: | The main aim of our study is to discriminate healthy people from those with Alzheimer’s disease (AD) based on T1 weighted whole brain Magnetic Resonance images (MRI). The brain degeneration is difficult to diagnose visually, which makes the task of manual interpretation difficult. Hence, computer aided analysis, diagnosis and classification of MRI brain images may prove useful for better diagnosis of Alzheimer’s disease. However, MR images as such cannot directly be used for diagnosis. We need to extract relevant features from the data. Various feature extraction methods have been proposed in different domains of real time decision systems. We want to improve diagnosis of AD using appropriate feature extraction techniques. This will allow carrying out computer-aided-diagnosis in faster and accurate way in comparison to manual diagnosis. To evaluate our decision model, there is a requirement of MRI images of normal and abnormal subjects. For this, we require T1-3D MRI data from ADNI. We will be thankful to you for your kind support, which we would like to acknowledge in all our future publications with your permission. |
| Investigator's Name: | R. K. Agrawal |
| Proposed Analysis: | The main aim of our study is to discriminate healthy people from those with Alzheimer’s disease (AD) based on T1 weighted whole brain Magnetic Resonance images (MRI). The brain degeneration is difficult to diagnose visually, which makes the task of manual interpretation difficult. Hence, computer aided analysis, diagnosis and classification of MRI brain images may prove useful for better diagnosis of Alzheimer’s disease. However, MR images as such cannot directly be used for diagnosis. We need to extract relevant features from the data. Various feature extraction methods have been proposed in different domains of real time decision systems. We want to improve diagnosis of AD using appropriate feature extraction techniques. This will allow carrying out computer-aided-diagnosis in faster and accurate way in comparison to manual diagnosis. To evaluate our decision model, there is a requirement of MRI images of normal and abnormal subjects. For this, we require T1-3D MRI data from ADNI. We will be thankful to you for your kind support, which we would like to acknowledge in all our future publications with your permission. |