Objective: The Alzheimer's continuum is biologically defined by beta-amyloid deposition, which at the earliest stages is superimposed upon white matter degeneration in aging. However, the extent to which these co-occurring changes is characterized is relatively underexplored. The goal of this study was to use diffusional kurtosis imaging (DKI) and biophysical modeling to detect and describe amyloid-related white matter changes in preclinical Alzheimer disease. Methods: Cognitively unimpaired participants ages 45 to 85 years completed brain magnetic resonance imaging, amyloid positron emission tomography (florbetapir), neuropsychological testing, and other clinical measures at baseline in a cohort study. We tested whether beta-amyloid-negative (ABÀ) and -positive (AB+) participants differed on DKIbased conventional (ie, fractional anisotropy [FA], mean diffusivity [MD], mean kurtosis) and modeling (ie, axonal water fraction [AWF], extra-axonal radial diffusivity [D e,⊥ ]) metrics, and whether these metrics were associated with other biomarkers. Results: We found significantly greater diffusion restriction (higher FA/AWF, lower MD/D e,⊥ ) in white matter in AB+ than ABÀ (partial η 2 =0.08-0.19), more notably in the extra-axonal space within primarily late myelinating tracts. Diffusion metrics predicted amyloid status incrementally over age (area under the curve = 0.84) with modest yet selective associations, where AWF (a marker of axonal density) correlated with speed/executive functions and neurodegeneration, whereas D e,⊥ (a marker of gliosis/myelin repair) correlated with amyloid deposition and white matter hyperintensity volume. Interpretation: These results support prior evidence of a nonmonotonic change in diffusion behavior, where an early increase in diffusion restriction is hypothesized to reflect inflammation and myelin repair prior to an ensuing decrease in diffusion restriction, indicating glial and neuronal degeneration.
PyDesigner is an open-source and containerized Python software package, adapted from the DESIGNER pipeline, for diffusion weighted magnetic resonance imaging preprocessing and tensor estimation. PyDesigner combines tools from FSL and MRtrix3 to reduce the effects of signal noise and imaging artifacts on multiple diffusion measures that can be derived from the diffusion and kurtosis tensors. This publication describes the main features of PyDesigner and highlights its ease of use across platforms, while examining its accuracy and robustness in deriving commonly used diffusion and kurtosis metrics.
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