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: | Stuart Grieve |
| Institution: | Sydney University |
| Department: | Imaging and Phenotyping Laboratory |
| Country: | |
| Proposed Analysis: | Aims: (1) To investigate common pathways of brain structural and connectivity change in early dementia, depression and concussion compared to changes with “normal ageing” and risk factors shared between these disorders. (2) To link our large, well-characterised samples of people with dementia, depression and concussion with high-quality MRI and clinical data from ADNI. (3) To perform robust replication tests for neuroimaging findings across independent datasets. Research questions: (1) What are the specific and transdiagnostic white matter connectivity and microstructural abnormalities in dementia, depression and concussion? (2) Are such findings replicable across disparate datasets? (3) Are there shared risk factors across these conditions? Our group hosts a standardised framework for automated acquisition, storage and analysis of neuroimaging data on a massive scale. Using this framework, we are combining multiple large independent neuroimaging datasets. We will apply our existing pipelines for image analysis to generate measures of brain structure and test how these relate to demographic, clinical and cognitive outcomes, specifically in dementia, depression and concussion. The quantities of interest are tissue microstructural properties based on water diffusion in specific relevant white matter tracts (fractional anisotropy describing the extent of directionality, axial and radial diffusivity describing the magnitude of diffusion along and across the principal axis of diffusion, and mean diffusivity along all axes), connectomic measures in the whole brain and in specific relevant cognitive networks or for specific targets (network topology of cognitive control networks). Many of the tools for these analyses (FreeSurfer, MRTrix, FSL) are standard freely-available packages and some (for analysis of non-Gaussian diffusion) are developed in-house. To generate our main outcome variables, the following is an example of an analysis pipeline. (1) Segmentation of the T1-weighted image using FreeSurfer version 6. (2) Pre-processing the diffusion data including denoising, eddy-current correction, motion correction, estimation of fibre orientation distributions by spherical deconvolution (FSL and MRTrix tools). (3) Spatial alignment of T1-weighted and diffusion data. (4) Probabilistic fibre tracking for pairs of cortical regions at the grey-white interface using corrections for fibre density. (5) Estimation of diffusion kurtosis and tensor metrics. (6) Measurement of tensor and kurtosis metrics in tracts of interest. (7) Graph analysis of brain connectomes (in-house tools). |
| Additional Investigators |
