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: | Emily Pogue |
| Institution: | Columbia University |
| Department: | Biomedical Engineering |
| Country: | |
| Proposed Analysis: | In this project, we will be training a neural network to classify T1 weighted functional MRI images of brains into one of three categories: healthy, schizophrenic, or suffering from Alzheimer’s. Although much work has been done in classifying structural MRI scans, the lack of large datasets of fMRI images has made it difficult to train fMRI classifying models. We will use the program DeepContrast to translate structural MRI scans into pseudo-fMRI scans and use these to train our model. The Alzheimer-affected images will come from ADNI and the Schizophrenic images from SchizConnect. This is a final term project for the course Deep Learning in Biomedical Image Processing in the Biomedical Engineering department of Columbia University. |
| Additional Investigators | |
| Investigator's Name: | Joyce Liu |
| Proposed Analysis: | In this project we will be training a neural network to classify T1 weighted functional MRI images of brains into one of three categories: healthy, schizophrenic, or suffering from Alzheimer’s. Although much work has been done in classifying structural MRI scans, the lack of large datasets of fMRI images has made it difficult to train fMRI classifying models. We will use the program DeepContrast to translate structural MRI scans into pseudo-fMRI scans and use these to train our model. We will be using ADNI for the Alzheimer-affected images. This project is a term project for the course Deep Learning in Biomedical Image Analysis at Columbia University. |
| Investigator's Name: | Hannah Ballard |
| Proposed Analysis: | In this project, we will be training a neural network to classify T1 weighted functional MRI images of brains into one of three categories: healthy, schizophrenic, or suffering from Alzheimer’s. Although much work has been done in classifying structural MRI scans, the lack of large datasets of fMRI images has made it difficult to train fMRI classifying models. We will use the program DeepContrast to translate structural MRI scans into pseudo-fMRI scans and use these to train our model. We will be using ADNI for the Alzheimer-affected images. This project is a term project for the course Deep Learning in Biomedical Image Analysis at Columbia University. |
