Alterations in Cortical Thickness and White Matter Integrity in Mild Cognitive Impairment Measured by Whole-Brain Cortical Thickness Mapping and Diffusion Tensor Imaging

Authors:
  • FGF.C. Goldstein
    F.C. Goldstein
  • EVE. Veledar
    E. Veledar
  • ALA.I. Levey
    A.I. Levey
  • JLJ.J. Lah
    J.J. Lah
  • CMC.C. Meltzer
    C.C. Meltzer
  • CHC.A. Holder
    C.A. Holder
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Abstract
BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) is a risk factor for Alzheimer disease and can be difficult to diagnose because of the subtlety of symptoms. This study attempted to examine gray matter (GM) and white matter (WM) changes with cortical thickness analysis and diffusion tensor imaging (DTI) in patients with MCI and demographically matched comparison subjects to test these measurements as possible imaging markers for diagnosis. MATERIALS AND METHODS: Subjects with amnestic MCI (n = 10; age, 72.2 ± 7.1 years) and normal cognition (n = 10; age, 70.1 ± 7.7 years) underwent DTI and T1-weighted MR imaging at 3T. Fractional anisotropy (FA), apparent diffusion coefficient (ADC), and cortical thickness were measured and compared between the MCI and control groups. We evaluated the diagnostic accuracy of 2 methods, either in combination or separately, using binary logistic regression and nonparametric statistical analyses for sensitivity, specificity, and accuracy. RESULTS: Decreased FA and increased ADC in WM regions of the frontal and temporal lobes and corpus callosum (CC) were observed in patients with MCI. Cortical thickness was decreased in GM regions of the frontal, temporal, and parietal lobes in patients with MCI. Changes in WM and cortical thickness seemed to be more pronounced in the left hemisphere compared with the right hemisphere. Furthermore, the combination of cortical thickness and DTI measurements in the left temporal areas improved the accuracy of differentiating MCI patients from control subjects compared with either measure alone. CONCLUSIONS: DTI and cortical thickness analyses may both serve as imaging markers to differentiate MCI from normal aging. Combined use of these 2 methods may improve the accuracy of MCI diagnosis.

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