Mild cognitive impairment: Can FDG-PET predict who is to rapidly convert to Alzheimer's disease?
ABSTRACT Patients with mild cognitive impairment (MCI) were assessed, and a metabolic profile associated with conversion to AD at 18-month follow-up was sought. As compared with nonconverters (n = 10), converters (n = 7) had lower fluorodeoxyglucose uptake in the right temporoparietal cortex (p = 0.02, corrected for cluster size), without individual overlap. Awaiting replication in an independent sample, these findings suggest that among patients with MCI, fluorodeoxyglucose PET may accurately identify rapid converters.
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ABSTRACT: We compared the predictive ability of the various neuroimaging tools and determined the most cost-effective, non-invasive Alzheimer's disease (AD) prediction model in mild cognitive impairment (MCI) individuals. Thirty-two MCI subjects were evaluated at baseline with [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), MRI, diffusion tensor imaging (DTI), and neuropsychological tests, and then followed up for 2 yr. After a follow up period, 12 MCI subjects converted to AD (MCIc) and 20 did not (MCInc). Of the voxel-based statistical comparisons of baseline neuroimaging data, the MCIc showed reduced cerebral glucose metabolism (CMgl) in the temporo-parietal, posterior cingulate, precuneus, and frontal regions, and gray matter (GM) density in multiple cortical areas including the frontal, temporal and parietal regions compared to the MCInc, whereas regional fractional anisotropy derived from DTI were not significantly different between the two groups. The MCIc also had lower Mini-Mental State Examination (MMSE) score than the MCInc. Through a series of model selection steps, the MMSE combined with CMgl model was selected as a final model (classification accuracy 93.8%). In conclusion, the combination of MMSE with regional CMgl measurement based on FDG-PET is probably the most efficient, non-invasive method to predict AD in MCI individuals after a two-year follow-up period. Graphical AbstractJournal of Korean Medical Science 05/2015; 30(6):779-787. DOI:10.3346/jkms.2015.30.6.779 · 1.25 Impact Factor
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ABSTRACT: Aging is associated with microstructural changes in brain tissue that can be visualized using diffusion tensor imaging (DTI). While previous studies have established age-related changes in white matter (WM) diffusion using DTI, the impact of age on gray matter (GM) diffusion remains unclear. The present study utilized DTI metrics of mean diffusivity (MD) to identify age differences in GM/WM microstructure in a sample of healthy older adults (N = 60). A secondary aim was to determine the functional significance of whole-brain GM/WM MD on global cognitive function using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Participants were divided into three age brackets (ages 50-59, 60-69, and 70+) to examine differences in MD and cognition by decade. MD was examined bilaterally in the frontal, temporal, parietal, and occipital lobes for the primary analyses and an aggregate measure of whole-brain MD was used to test relationships with cognition. Significantly higher MD was observed in bilateral GM of the temporal and parietal lobes, and in right hemisphere WM of the frontal and temporal lobes of older individuals. The most robust differences in MD were between the 50-59 and 70+ age groups. Higher whole-brain GM MD was associated with poorer RBANS performance in the 60-69 age group. Results suggest that aging has a significant and differential impact on GM/WM diffusion in healthy older adults, which may explain a modest degree of cognitive variability at specific time points during older adulthood.Brain Imaging and Behavior 04/2015; DOI:10.1007/s11682-015-9383-7 · 3.39 Impact Factor
The Open Nuclear Medicine Journal 11/2010; 2(1):46-52. DOI:10.2174/1876388X01002010046