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: | Kun Meng |
| Institution: | Brown University |
| Department: | Division of Applied Mathematics |
| Country: | |
| Proposed Analysis: | I plan to use the ADNI data in the following projects: 1. Applications of topological data analysis (TDA) to fMRI functional connectivity analysis. This project transforms the functional data form of fMRI into topological form and applies TDA to analyze the functional connectivity structures. This project is potentially relevant to the following papers: 'Population-level Task-evoked Functional Connectivity via Fourier Analysis' and 'Discriminative persistent homology of brain networks.' 2. Applications of TDA to grayscale images. Grayscale images essentially contain geometric (topological) information. This project transforms the topological structures in the images into functional data and applies functional data analysis to the transformed data. This project is a generalization of the following two papers: 'Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis' and 'Randomness and Statistical Inference of Shapes via the Smooth Euler Characteristic Transform.' |
| Additional Investigators | |
| Investigator's Name: | Jinyu Wang |
| Proposed Analysis: | I plan to use the ADNI data in the following projects: 1. Applications of topological data analysis (TDA) to fMRI functional connectivity analysis. This project transforms the functional data form of fMRI into topological form and applies TDA to analyze the functional connectivity structures. This project is potentially relevant to the following papers: 'Population-level Task-evoked Functional Connectivity via Fourier Analysis' and 'Discriminative persistent homology of brain networks.' 2. Applications of TDA to grayscale images. Grayscale images essentially contain geometric (topological) information. This project transforms the topological structures in the images into functional data and applies functional data analysis to the transformed data. This project is a generalization of the following two papers: 'Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis' and 'Randomness and Statistical Inference of Shapes via the Smooth Euler Characteristic Transform.' |
