Structural imaging provides a means to visualize change in anatomy associated with cognitive decline (e.g., Project 3 """"""""Attention Profiles in Healthy Aging and Early Stage DAT"""""""") and also candidate surrogate markers for detection of early-stage DAT in combination with other biomarkers (e.g., Project 2 """"""""Antecedent biomarkers of AD in CSF""""""""). The goal of the Core E: Imaging is to collect, store, and disseminate imaging data for the use of the present program project investigations and also to facilitate the development of infrastructure to support future imaging projects. The following Specific Aims will be pursued: 1. Structural imaging data on demented and nondemented participants will be collected, in close coordination with Core B: Clinical, at two-year longitudinal intervals. The structural imaging battery will include (i) multiple acquisitions of high contrast MP-RAGE images, (ii)3D T2 images for assessment of white matter. These images will be used for measurement of cortical and subcortical atrophy and assessment of white matter integrity, (iii)diffusion tensor imaging to assess white matter microstructural integrity, and (iv)T2-SWI images. In addition, functional imaging data on demented and nondemented participants will be collected. The functional imaging data will be BOLD images during rest to assess functional connectivity. 2. Research neuroradiological assessment will be made by board-certified neuroradiologists on all structural image data sets. 3. Structural data sets will be archived in conjunction with Core C: Biostatistics and made available via a web-based interface to investigators to pursue research projects. 4. Quantitative structural assessment will be provided for correlating imaging data with project-specific data including (i) automated estimates of whole- brain atrophy, (ii) manual estimates of hippocampal, entorhinal, frontal, and other cortical volumes, (iii) automated estimates of cortical and subcortical volumes derived from Freesurfer software (Fischl et al., 2002;Fischl et al., 2004;Desikan et al., 2006), and (iv) automated assessment of white matter hyperintensities. Quantitative functional assessment will also be provided for correlating imaging data with project-specific data and will include estimates of the functional connectivity between seed regions such as the hippocampus and the precuneus. 5. Working closely with Core C: Biostatistics and Core A: Administration, data will be managed to integrate the Core's function with the scientific goals of the program project.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Research Program Projects (P01)
Project #
Application #
Study Section
Special Emphasis Panel (ZAG1-ZIJ-4)
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Washington University
Saint Louis
United States
Zip Code
Lucey, Brendan P; Mawuenyega, Kwasi G; Patterson, Bruce W et al. (2016) Associations Between β-Amyloid Kinetics and the β-Amyloid Diurnal Pattern in the Central Nervous System. JAMA Neurol :
Esparza, Thomas J; Wildburger, Norelle C; Jiang, Hao et al. (2016) Soluble Amyloid-beta Aggregates from Human Alzheimer's Disease Brains. Sci Rep 6:38187
McKee, Ann C; Cairns, Nigel J; Dickson, Dennis W et al. (2016) The first NINDS/NIBIB consensus meeting to define neuropathological criteria for the diagnosis of chronic traumatic encephalopathy. Acta Neuropathol 131:75-86
Reiman, Eric M; Langbaum, Jessica B; Tariot, Pierre N et al. (2016) CAP--advancing the evaluation of preclinical Alzheimer disease treatments. Nat Rev Neurol 12:56-61
Jin, Sheng Chih; Benitez, Bruno A; Deming, Yuetiva et al. (2016) Pooled-DNA Sequencing for Elucidating New Genomic Risk Factors, Rare Variants Underlying Alzheimer's Disease. Methods Mol Biol 1303:299-314
Hohman, Timothy J; Cooke-Bailey, Jessica N; Reitz, Christiane et al. (2016) Global and local ancestry in African-Americans: Implications for Alzheimer's disease risk. Alzheimers Dement 12:233-43
Van Schependom, Jeroen; Jain, Saurabh; Cambron, Melissa et al. (2016) Reliability of measuring regional callosal atrophy in neurodegenerative diseases. Neuroimage Clin 12:825-831
Hohman, Timothy J; Bush, William S; Jiang, Lan et al. (2016) Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium. Neurobiol Aging 38:141-50
Su, Yi; Rubin, Brian B; McConathy, Jonathan et al. (2016) Impact of MR-Based Attenuation Correction on Neurologic PET Studies. J Nucl Med 57:913-7
Ebbert, Mark T W; Boehme, Kevin L; Wadsworth, Mark E et al. (2016) Interaction between variants in CLU and MS4A4E modulates Alzheimer's disease risk. Alzheimers Dement 12:121-9

Showing the most recent 10 out of 756 publications