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 (e.g., Project 4 """"""""Project 4 """"""""Predicting Cognitive Decline in Healthy Elders""""""""). The goal of the Core F: Neuroimaging 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 A: Clinical, at two-year longitudinal intervals. The structural imaging battery will include (i) high-resolution T1-weighted FLASH images for measurement of the hippocampus, (ii) multiple acquisitions of high contrast MP-RAGE images for measurement of cortical atrophy, (iii) FLAIR images for assessment of white-matter, and (iv) diffusion tensor imaging also to assess white-matter integrity. (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 D: 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, and (iii) diffusion tensor measures of tissue integrity. (5) Working closely with Core D: Biostatistics and Core E: Administration, data will be managed to integrate the Core's function with the scientific goals of the program project.
| Pottier, Cyril; Zhou, Xiaolai; Perkerson 3rd, Ralph B et al. (2018) Potential genetic modifiers of disease risk and age at onset in patients with frontotemporal lobar degeneration and GRN mutations: a genome-wide association study. Lancet Neurol 17:548-558 |
| Joseph-Mathurin, Nelly; Su, Yi; Blazey, Tyler M et al. (2018) Utility of perfusion PET measures to assess neuronal injury in Alzheimer's disease. Alzheimers Dement (Amst) 10:669-677 |
| Del-Aguila, Jorge L; Fernández, Maria Victoria; Schindler, Suzanne et al. (2018) Assessment of the Genetic Architecture of Alzheimer's Disease Risk in Rate of Memory Decline. J Alzheimers Dis 62:745-756 |
| Oxtoby, Neil P; Young, Alexandra L; Cash, David M et al. (2018) Data-driven models of dominantly-inherited Alzheimer's disease progression. Brain 141:1529-1544 |
| 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 |
| Bonham, Luke W; Karch, Celeste M; Fan, Chun C et al. (2018) CXCR4 involvement in neurodegenerative diseases. Transl Psychiatry 8:73 |
| 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 |
| 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 |
| 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 |
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