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.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Research Program Projects (P01)
Project #
5P01AG003991-30
Application #
8425013
Study Section
Special Emphasis Panel (ZAG1-ZIJ-4)
Project Start
Project End
2014-12-31
Budget Start
2013-01-01
Budget End
2013-12-31
Support Year
30
Fiscal Year
2013
Total Cost
$182,007
Indirect Cost
$62,266
Name
Washington University
Department
Type
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Bonham, Luke W; Karch, Celeste M; Fan, Chun C et al. (2018) CXCR4 involvement in neurodegenerative diseases. Transl Psychiatry 8:73
Mishra, Shruti; Blazey, Tyler M; Holtzman, David M et al. (2018) Longitudinal brain imaging in preclinical Alzheimer disease: impact of APOE ?4 genotype. Brain 141:1828-1839
Wildburger, Norelle C; Gyngard, Frank; Guillermier, Christelle et al. (2018) Amyloid-? Plaques in Clinical Alzheimer's Disease Brain Incorporate Stable Isotope Tracer In Vivo and Exhibit Nanoscale Heterogeneity. Front Neurol 9:169
Schindler, Suzanne E; Gray, Julia D; Gordon, Brian A et al. (2018) Cerebrospinal fluid biomarkers measured by Elecsys assays compared to amyloid imaging. Alzheimers Dement 14:1460-1469
Pehlivanova, Marieta; Wolf, Daniel H; Sotiras, Aristeidis et al. (2018) Diminished Cortical Thickness is Associated with Impulsive Choice in Adolescence. J Neurosci :
Weintraub, Sandra; Besser, Lilah; Dodge, Hiroko H et al. (2018) Version 3 of the Alzheimer Disease Centers' Neuropsychological Test Battery in the Uniform Data Set (UDS). Alzheimer Dis Assoc Disord 32:10-17
Babulal, Ganesh M; Chen, Suzie; Williams, Monique M et al. (2018) Depression and Alzheimer's Disease Biomarkers Predict Driving Decline. J Alzheimers Dis 66:1213-1221
Allison, Samantha; Babulal, Ganesh M; Stout, Sarah H et al. (2018) Alzheimer Disease Biomarkers and Driving in Clinically Normal Older Adults: Role of Spatial Navigation Abilities. Alzheimer Dis Assoc Disord 32:101-106
Gyurkovics, Mate; Balota, David A; Jackson, Jonathan D (2018) Mind-wandering in healthy aging and early stage Alzheimer's disease. Neuropsychology 32:89-101
Gangishetti, Umesh; Christina Howell, J; Perrin, Richard J et al. (2018) Non-beta-amyloid/tau cerebrospinal fluid markers inform staging and progression in Alzheimer's disease. Alzheimers Res Ther 10:98

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