The overarching aim of this application is to determine the indicators that characterize the progression from cognitive normality to the earliest stages of cognitive impairment caused by Alzheimer disease (AD). The AD field is shifting toward the goal of prevention strategies, but these efforts depend not only on therapeutic development, but also on a detailed understanding of which individuals are at high risk for symptomatic AD in order to target them for clinical trials, disease-modifying therapies, and to monitor therapy success. The overall Specific Aims in this renewal application are to: 1. Follow current participants in HASD and add new enrollees to maintain the sample size at ~250. 2. Obtain longitudinal data from the HASD participants on an annual basis for clinical and psychometric measures and at 3 year intervals with the following measures: a. Novel measures of attentional control and neural network integrity (Project 1) b. Amyloid imaging with [18F] florbetapir (Imaging Core) c. Assays of CSF analytes (Project 2) d. Structural MRI and resting state functional connectivity MRI (Imaging Core) e. Measures of sleep efficiency (Project 2) f. SNPs that predict rate of progression (Project 3) g. At autopsy, correlate measures of A? and tau burden, synaptic integrity, and neuronal loss with variables from Projects 1 and 2 and from the Imaging Core 3. Characterize the cognitive, imaging, molecular biomarker, and genetic factors that distinguish cognitively normal older adults, with and without preclinical AD, and individuals with symptomatic AD. 4. Analyze associations among rates of change of all disease markers from all Cores and Projects (Biostatistics Core).
Alzheimer disease (AD) is preceded by at least a decade of clinically silent brain changes (termed preclinical AD) that ultimately result in declines in memory and thinking. The current application proposes to determine the indicators that identify individuals with preclinical AD at the cusp of developing clinical symptoms so that these individuals can best be targeted for eventual preventative therapies.
|Adel, Tameem; Cohen, Taco; Caan, Matthan et al. (2017) 3D scattering transforms for disease classification in neuroimaging. Neuroimage Clin 14:506-517|
|Schindler, Suzanne E; Jasielec, Mateusz S; Weng, Hua et al. (2017) Neuropsychological measures that detect early impairment and decline in preclinical Alzheimer disease. Neurobiol Aging 56:25-32|
|Zhao, Yue; Raichle, Marcus E; Wen, Jie et al. (2017) In vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imaging. Neuroimage 148:296-304|
|Su, Yi; Vlassenko, Andrei G; Couture, Lars E et al. (2017) Quantitative hemodynamic PET imaging using image-derived arterial input function and a PET/MR hybrid scanner. J Cereb Blood Flow Metab 37:1435-1446|
|Deming, Yuetiva; Li, Zeran; Kapoor, Manav et al. (2017) Genome-wide association study identifies four novel loci associated with Alzheimer's endophenotypes and disease modifiers. Acta Neuropathol 133:839-856|
|Day, Gregory S; Lim, Tae Sung; Hassenstab, Jason et al. (2017) Differentiating cognitive impairment due to corticobasal degeneration and Alzheimer disease. Neurology 88:1273-1281|
|Monsell, Sarah E; Mock, Charles; Fardo, David W et al. (2017) Genetic Comparison of Symptomatic and Asymptomatic Persons With Alzheimer Disease Neuropathology. Alzheimer Dis Assoc Disord 31:232-238|
|Mez, Jesse; Chung, Jaeyoon; Jun, Gyungah et al. (2017) Two novel loci, COBL and SLC10A2, for Alzheimer's disease in African Americans. Alzheimers Dement 13:119-129|
|Khajehnejad, Moein; Saatlou, Forough Habibollahi; Mohammadzade, Hoda (2017) Alzheimer's Disease Early Diagnosis Using Manifold-Based Semi-Supervised Learning. Brain Sci 7:|
|Holth, Jerrah; Patel, Tirth; Holtzman, David M (2017) Sleep in Alzheimer's Disease - Beyond Amyloid. Neurobiol Sleep Circadian Rhythms 2:4-14|
Showing the most recent 10 out of 831 publications