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: | Christopher Leatherday |
| Institution: | Curtin University |
| Department: | Applied Physics |
| Country: | |
| Proposed Analysis: | The proposed work is the same for image data from either source, hence the Proposed Analysis for the AIBL application is the same as that for the ADNI application. Our group has created two different optimised hippocampal masks by manually marking T1 volumetric MRIs of a study group of Alzheimer's (AD), Mild Cognitive Impairment (MCI), and Healthy Control (HC) individuals, and warping the MRIs and ROIs to a standard space using Statistical Parametric Mapping (SPM) (Wellcome Department of Neuroimaging, London UK). One of the masks contains only the pes, and the other includes the pes and body. We seek to apply our hippocampal masks to FDG-PET images of subjects with a diagnosis of AD or MCI (established by longitudinal follow-up) and HCs to replicate the findings of Mosconi et al. (2005), who found that an optimised hippocampal mask could effectively differentiate between AD, HC and MCI brain FDG-PET images. Also, we will attempt to quantify any differences between the mask containing only the pes, and the mask containing the whole hippocampus. We then plan on investigating whether there is similar utility in application of the mask to brain SPECT perfusion imaging. SPM’s latest spatial normalisation method technique is Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL). This involves segmentation of a structural brain image into separate grey and white matter components before any normalisation occurs, then a series of linear and non-linear warps are calculated to normalise the image to a standard space. The same deformations can then be applied to a co-registered functional image. SPM will be used to spatially normalise the T1 MRIs and thus the co-registered FDG-PET images from the ADNI group so that each of the hippocampal masks can be used to attempt to quantify differences in hippocampal metabolism in AD, MCI and HC individuals. Reference Mosconi, L., W. H Tsui, S. De Santi, J. Li, H. Rusinek, A. Convit, Y. Li, M. Boppana, and M. J. De Leon. 2005. Reduced hippocampal metabolism in MCI and AD: automated FDG-PET image analysis. Neurology 64, no. 11: 1860-7. |
| Additional Investigators |