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: | Kaitlin McOwen |
| Institution: | Texas A&M University |
| Department: | Psychological and Brain Sciences |
| Country: | |
| Proposed Analysis: | H1: Differences in whole brain-WM integrity (DTI parameters), in mild AD and aMCI due to AD in relation to controls. H2: Possible correlations between CSF measures of Aβ42, p-Tau and t-Tau and DTI parameters in aMCI due to AD and mild AD. Participants will be selected from the ADNI 3 dataset and divided into three groups based on previous diagnoses (mild Alzheimer’s Disease [mild AD], amnestic Mild Cognitive Impairment [aMCI], and Health Control (HC)). Assess the WM microstructural abnormalities using MRICloud (www.MRICloud.org) a recently developed web-based tool to perform automated segmentation and quantification of multiple MRI modalities. Connectivity matrices will be obtained through the ExploreDTI network analysis tool [LEEMANS, AJBSJJDK, et al., 2009]. The AAL (Automated Anatomical Labeling) atlas [TZOURIO-MAZOYER, Nathalie, et al., 2002] will be used as a form of anatomical segmentation and the diffusion images processed according to the pre-processing described above will be used as a basis for estimating the possible between the regions of interest and the subsequent quantification of diffusion parameters (FA, AD, RD and MD). With the connectivity matrices, we will use the GraphVar software [KRUSCHWITZ, J. D., et al., 2015] to extract and characterize each group of subjects with the objectified graph parameters. |
| Additional Investigators |
