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: | Gidon Levakov |
| Institution: | Ben Gurion University of the Negev |
| Department: | Brain and Cognitive Sciences |
| Country: | |
| Proposed Analysis: | Developing a Deep Learning framework for the prediction of chronological age from structural MRI scans. Previous findings associate an overestimation of brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Thus, my aim is to develop interpretable deep learning models to study normal aging and aging in the presence of neurodegenerative diseases |
| Additional Investigators | |
| Investigator's Name: | Galia Avidan |
| Proposed Analysis: | The overarching goal of the present proposal is to characterize structural brain changes in healthy individuals in a wide age range. This will be done by utilizing a machine learning model, with an automated minimal preprocessing pipeline for predicting age from structural brain images using a large dataset, thus allowing to estimate the gap between subjects biological and predicted age. We want to further examine how this gap is related to other health-related risk or resilience factors such as body mass. Moreover, we aim to provide interpretable results using a novel approach which we have adopted for the current settings. |
| Investigator's Name: | Tammy Riklin Raviv |
| Proposed Analysis: | The overarching goal of the present proposal is to characterize structural brain changes in healthy individuals in a wide age range. This will be done by utilizing a machine learning model, with an automated minimal preprocessing pipeline for predicting age from structural brain images using a large dataset, thus allowing to estimate the gap between subjects biological and predicted age. We want to further examine how this gap is related to other health-related risk or resilience factors such as body mass. Moreover, we aim to provide interpretable results using a novel approach which we have adopted for the current settings. |
| Investigator's Name: | Ilan Shelef |
| Proposed Analysis: | The overarching goal of the present proposal is to characterize structural brain changes in healthy individuals in a wide age range. This will be done by utilizing a machine learning model, with an automated minimal preprocessing pipeline for predicting age from structural brain images using a large dataset, thus allowing to estimate the gap between subjects biological and predicted age. We want to further examine how this gap is related to other health-related risk or resilience factors such as body mass. Moreover, we aim to provide interpretable results using a novel approach which we have adopted for the current settings. |
