Abstract
Biomarkers have been required for diagnosing early Alzheimer disease. We assessed the utility of hippocampal diffusion parameters for diagnosing Alzheimer disease pathology in mild cognitive impairment. Sixty-nine patients with mild cognitive impairment underwent both CSF measurement and multi-shell diffusion imaging at 3T. Based on the CSF biomarker level, patients were classified according to the presence (Alzheimer disease group, n = 35) or absence (non-Alzheimer disease group, n = 34) of Alzheimer disease pathology. Neurite orientation dispersion and density imaging and diffusion tensor imaging parametric maps were generated. Two observers independently created the hippocampal region of interest for calculating histogram features. Interobserver correlations were calculated. The statistical significance of intergroup differences was tested by using the Mann-Whitney U test. Logistic regression analyses, using both the clinical scale and the image data, were used to predict intergroup differences, after which group discriminations were performed. Most intraclass correlation coefficient values were between 0.59 and 0.91. In the regions of interest of both observers, there were statistically significant intergroup differences for the left-side neurite orientation dispersion and density imaging-derived intracellular volume fraction, right-side diffusion tensor imaging-derived mean diffusivity, left-side diffusion tensor imaging-derived mean diffusivity, axial diffusivity, and radial diffusivity (P < .05). Logistic regression models revealed that diffusion parameters contributed the most to discriminating between the groups. The areas under the receiver operating characteristic curve for the regions of interest of observers A/B were 0.69/0.68, 0.69/0.68, 0.73/0.68, 0.71/0.68, and 0.68/0.68 for the left-side intracellular volume fraction (mean), right-side mean diffusivity (mean), left-side mean diffusivity (10th percentile), axial diffusivity (10th percentile), and radial diffusivity (mean). Hippocampal diffusion parameters might be useful for the early diagnosis of Alzheimer disease.
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