Objective
To establish a fully automated, robust imaging marker for cerebral small vessel disease (SVD) and related cognitive impairment that is easy to implement, reflects disease burden, and is strongly associated with processing speed, the predominantly affected cognitive domain in SVD.
Methods
We developed a novel magnetic resonance imaging marker based on diffusion tensor imaging, skeletonization of white matter tracts, and histogram analysis. The marker (peak width of skeletonized mean diffusivity [PSMD]) was assessed along with conventional SVD imaging markers. We first evaluated associations with processing speed in patients with genetically defined SVD (n = 113). Next, we validated our findings in independent samples of inherited SVD (n = 57), sporadic SVD (n = 444), and memory clinic patients with SVD (n = 105). The new marker was further applied to healthy controls (n = 241) and to patients with Alzheimer's disease (n = 153). We further conducted a longitudinal analysis and interscanner reproducibility study.
Methods
We developed a novel magnetic resonance imaging marker based on diffusion tensor imaging, skeletonization of white matter tracts, and histogram analysis. The marker (peak width of skeletonized mean diffusivity [PSMD]) was assessed along with conventional SVD imaging markers. We first evaluated associations with processing speed in patients with genetically defined SVD (n = 113). Next, we validated our findings in independent samples of inherited SVD (n = 57), sporadic SVD (n = 444), and memory clinic patients with SVD (n = 105). The new marker was further applied to healthy controls (n = 241) and to patients with Alzheimer's disease (n = 153). We further conducted a longitudinal analysis and interscanner reproducibility study.
Results
PSMD was associated with processing speed in all study samples with SVD (p-values between 2.8 × 10−3 and 1.8 × 10−10). PSMD explained most of the variance in processing speed (R2 ranging from 8.8% to 46%) and consistently outperformed conventional imaging markers (white matter hyperintensity volume, lacune volume, and brain volume) in multiple regression analyses. Increases in PSMD were linked to vascular but not to neurodegenerative disease. In longitudinal analysis, PSMD captured SVD progression better than other imaging markers.
PSMD was associated with processing speed in all study samples with SVD (p-values between 2.8 × 10−3 and 1.8 × 10−10). PSMD explained most of the variance in processing speed (R2 ranging from 8.8% to 46%) and consistently outperformed conventional imaging markers (white matter hyperintensity volume, lacune volume, and brain volume) in multiple regression analyses. Increases in PSMD were linked to vascular but not to neurodegenerative disease. In longitudinal analysis, PSMD captured SVD progression better than other imaging markers.
Interpretation
PSMD is a new, fully automated, and robust imaging marker for SVD. PSMD can easily be applied to large samples and may be of great utility for both research studies and clinical use.
Ann Neurol 2016.
PSMD is a new, fully automated, and robust imaging marker for SVD. PSMD can easily be applied to large samples and may be of great utility for both research studies and clinical use.
Ann Neurol 2016.
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