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O1–05–05: Predicting cognitive decline in non‐demented elderly with MRI, DTI, and longitudinal single‐voxel spectroscopic imaging
Author(s) -
Chao Linda L.,
Schuff Norbert,
Buckley Shan T.,
Zhang Yu,
Mungas Daniel,
Kramer Joel H.,
Yaffe Kristine,
Miller Bruce L.,
Weiner Michael W.
Publication year - 2007
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2007.04.072
Subject(s) - california verbal learning test , cognitive decline , diffusion mri , white matter , verbal learning , psychology , medicine , voxel , cognition , boston naming test , magnetic resonance imaging , audiology , nuclear medicine , dementia , neuropsychology , psychiatry , disease , radiology
Background: It is important to predict cognitive decline in elderly individuals who may be at risk for Alzheimer’s disease (AD). Objective: To determine the regional patterns of gray and white matter change and metabolic changes in the brain that best predict cognitive decline. Methods: Sixty-two non-demented subjects (74.0 8.5 years old) were assessed with the Mini-mental status exam (MMSE) and the California Verbal Learning Test (CVLT) at baseline and follow-up 1.1 0.2 years later. Of the 62 subjects, 48 scored below (impaired) and 14 scored at or above (controls) the ageand education-adjusted norms on the CVLT. Predictors were structural and diffusion-tensor imaging (DTI) and single voxel H MRS imaging at baseline and 6 months later. The outcome measure was the 1-year change in a memory composite score derived from immediate recall trials 1-4 of the CVLT. Results: Stepwise linear regression models were used to determine the relationship between baseline MMSE scores and demographic information with subsequent cognitive decline. These models revealed group status (i.e., control vs. impaired; r 0.11, p 0.008) and educational level (r 0.11, p 0.02) to be significant predictors of decline. Imaging variables were then added to the models to determine the predictive “added value” of volumetric MRI, DTI and MRS imaging to cognitive testing. In one model, left temporal white matter (WM) volume (r 0.13, p 0.007), group status (r 0.22, p 0.002), and baseline MMSE (r 0.27, p 0.049) were significant predictors of cognitive decline. Another model revealed fractional anisotopry (FA) of the right posterior cingulum (r 0.21, p 0.007) and sex (r 0.39, p 0.02) to be significant predictors decline. A third model showed change in N-acetylaspartate (NAA)/creatine (Cr) ratio (r 0.27, p 0.0001) and educational level (r 0.40, p 0. 02) to be significant predictors of decline. Conclusions: Together these results suggest that temporal WM volume, posterior cingulum FA, and change in the posterior cingulate NAA/Cr ratio may add predictive value to neuropsychological testing for predicting cognitive decline.