Background: We investigated regional amyloid staging characteristics in C-PiB-PET data from middle-aged to older participants at elevated risk for AD enrolled in the Wisconsin Registry for Alzheimer's Prevention.
Methods: We analyzed partial volume effect-corrected C-PiB-PET distribution volume ratio maps from 220 participants (mean age = 61.4 years, range 46.
The clinical presentation of Alzheimer's disease (AD) varies widely across individuals but the neurobiological mechanisms underlying this heterogeneity are largely unknown. Here, we compared regional gray matter (GM) volumes and associated gene expression profiles between cognitively-defined subgroups of amyloid-β positive individuals clinically diagnosed with AD dementia (age: 66 ± 7, 47% male, MMSE: 21 ± 5). All participants underwent neuropsychological assessment with tests covering memory, executive-functioning, language and visuospatial-functioning domains.
View Article and Find Full Text PDFPositron emission tomography (PET)-based staging of regional amyloid deposition has recently emerged as a promising tool for sensitive detection and stratification of pathology progression in Alzheimer's Disease (AD). Here we present an updated methodological framework for PET-based amyloid staging using region-specific amyloid-positivity thresholds and assess its longitudinal validity using serial PET acquisitions. We defined region-specific thresholds of amyloid-positivity based on Florbetapir-PET data of 13 young healthy individuals (age ≤ 45y), applied these thresholds to Florbetapir-PET data of 179 cognitively normal older individuals to estimate a regional amyloid staging model, and tested this model in a larger sample of patients with mild cognitive impairment (N = 403) and AD dementia (N = 85).
View Article and Find Full Text PDFWe tested the usefulness of a regional amyloid staging based on amyloid sensitive positron emission tomography to predict conversion to cognitive impairment and dementia in preclinical and prodromal Alzheimer's disease (AD). We analyzed 884 cases, including normal controls, and people with subjective cognitive decline or mild cognitive impairment (MCI), from the Alzheimer's Disease Neuroimaging Initiative with a maximum follow-up of 6 years and 318 cases with subjective memory complaints with a maximum follow-up time of three years from the INveStIGation of AlzHeimer's PredicTors cohort (INSIGHT-preAD study). Cox regression showed a significant association of regional amyloid stages with time to conversion from a cognitively normal to an MCI, and from an MCI to a dementia status.
View Article and Find Full Text PDFBackground: Current methods of amyloid PET interpretation based on the binary classification of global amyloid signal fail to identify early phases of amyloid deposition. A recent analysis of 18F-florbetapir PET data from the Alzheimer's disease Neuroimaging Initiative cohort suggested a hierarchical four-stage model of regional amyloid deposition that resembles neuropathologic estimates and can be used to stage an individual's amyloid burden in vivo. Here, we evaluated the validity of this in vivo amyloid staging model in an independent cohort of older people with subjective memory complaints (SMC).
View Article and Find Full Text PDFAmyloid deposition and neurofibrillary degeneration in Alzheimer's disease specifically affect discrete neuronal systems, but the underlying mechanisms that render some brain regions more vulnerable to Alzheimer's disease pathology than others remain largely unknown. Here we studied molecular properties underlying these distinct regional vulnerabilities by analysing Alzheimer's disease-typical neuroimaging patterns of amyloid deposition and neurodegeneration in relation to regional gene expression profiles of the human brain. Graded patterns of brain-wide vulnerability to amyloid deposition and neurodegeneration in Alzheimer's disease were estimated by contrasting multimodal amyloid-sensitive PET and structural MRI data between patients with Alzheimer's disease dementia (n = 76) and healthy controls (n = 126) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI).
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