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: | Xavier Pennec |
| Institution: | Inria |
| Department: | Epione team |
| Country: | |
| Proposed Analysis: | The goal of the study is to develop and test nes methodological techniques to describe the the variability of the barin anatomy at the scale of a population of subjects and to model its links with neurogenerative disease, particularly Alzheimer's diseases. Applications include the spatial comparison of healthy and diseased subjects in neuroscience and atlas to patient registration to map generic knowledge to patient-specific data in medical imaging. In computational anatomy, many methods compute the mean shape (called template or atlas) and a few variations encoded through tangent PCA on the atlas to subject deformations. However, despite important successes, anatomical data tend to exhibit an extensive variability than cannot be modeled with such a unimodal Gaussian model, hampering the prediction power. Thus, the field has moved in practice towards multiple atlases. The goal of our study on the ADNI data is to try new methodological techniques based on the recently proposed barycentric subspace analysis [1], which could be the right theoretical tools to rationalize and sustain multi-atlas methods. MRI imaging data will be used in a cross-sectional setting and correlation analyses between the new barycentric parameters and the clinical variables (age, disease status, etc) will be perfomed to demonstrate the potential of the method. [1] Xavier Pennec. Barycentric Subspace Analysis on Manifolds. Annals of Statistics, 46(6A):2711-2746, July 2018 |
| Additional Investigators |
