Abstract

AbstractBackgroundDamage to white matter (WM) within the medial temporal lobe (MTL) may underlie memory decline in aging and Alzheimer’s disease (AD). We measured MTL WM tract integrity through diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging (NODDI). We tested WM integrity associations with memory performance and amyloid‐ß pathology in cognitively normal older adults.Method105 cognitively normal older adults (70.8 years old, 65% female) with diffusion MRI were processed with MRtrix3. Standard diffusion metrics of fractional anisotropy (FA) and mean diffusivity (MD) were calculated. NODDI MDT was used to generate the neurite density index (NDI), representing number of voxels with unhindered diffusion, and the orientation dispersion index (ODI), representing the variability of neurite orientation. We calculated these measures for MTL tracts from the JHU WM Atlas: cingulum‐hippocampus, fornix column/body, and fornix/stria terminalis. Participants received 18F‐florbetapir (FBP) to measure Aß pathology (n = 94), and global FBP SUVR was calculated. Memory was measured with the Rey Auditory Verbal Learning Test delayed recall score (RAVLT, n = 81). Analyses covaried for age and/or education.ResultBetter memory was associated with lower ODI in the fornix column/body (r = ‐0.27, p = 0.018), with trends for higher FA (r = 0.021, p = 0.06) and lower NDI (r = ‐0.19, p = 0.09). Better memory was also associated with higher MD in the cingulum‐hippocampus (right: r = 0.23, p = 0.04; left: r = 0.25, p = 0.03), with trends for lower ODI (right: r = ‐.20, p = 0.08; left: r = ‐0.20, p = 0.09). Higher FBP SUVR was associated with increased FA in the fornix column/body (r = 0.31, p = 0.003) and a trend in the left cingulum hippocampus (r = 0.20, p = 0.054). Memory or FBP SUVR was not significantly related to integrity of the fornix/stria terminalis.ConclusionIntegrity of fornix column/body and cingulum‐hippocampus were related to both memory performance and Aß pathology. Our results suggest that using a combination of NODDI as well as diffusion tensor metrics provided a more robust understanding of memory impacts in aging.

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