Background and Objective:The dynamics of white matter (WM) changes are understudied in Alzheimer’s disease (AD). Our goal was to study the association between flortaucipir PET and WM health using NODDI and evaluate its association with cognitive performance. Specifically, we focused on NODDI’s Neurite Density Index (NDI), which aids in capturing axonal degeneration in WM and has greater specificity than single-shell diffusion MRI methodsMethod:We estimated regional flortaucipir PET SUVRs from three regions corresponding to Braak stage I, III/IV, and V/VI to capture the spatial distribution pattern of the 3R/4R tau in AD. Then, we evaluated the associations between these measurements and NDIs in 29 candidate WM tracts using Pearson correlation and multiple regression modelsResults:Based on 223 participants who were amyloid positive (mean age of 78 y/o and 57.0% male, 119 cognitively unimpaired, 56 MCI, 48 dementia), the results showed that WM tracts NDI decreased with increasing regional Braak tau SUVRs. Of all the significant WM tracts, the uncinate fasciculus (r=-0.274 for Braak I, -0.311 for Braak III/IV, and -0.292 for Braak V/VI,p<0.05) and cingulum adjoining hippocampus (r=-0.274, -0.288, -0.233,p<0.05), both tracts anatomically connected to areas of early tau deposition, were consistently found to be within the top five distinguishing WM tracts associated with flortaucipir SUVRs. The increase in tau deposition measurable outside the medial temporal lobes in Braak III-VI was associated with a decrease in NDI in the middle and inferior temporal WM tracts. For cognitive performance, WM NDI had similar coefficients of determination (r2=31%) as regional Braak flortaucipir SUVRs (29%), and together WM NDI and regional Braak flortaucipir SUVRs explained 46% of the variance in cognitive performanceDiscussion:We found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages, suggesting a spatial pattern in WM damage. NDI, a specific marker of axonal density, provides complementary information about disease staging and progression in addition to tau deposition. Measurements of WM changes are important for the mechanistic understanding of multifactorial pathways through which AD causes cognitive dysfunction.

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BackgroundThe dynamics of white matter (WM) changes are understudied in Alzheimer’s disease (AD). Leveraging tau deposition measured from Tau‐PET, our goal was to study the association between tau and white matter health using NODDI from multi‐shell diffusion MRI (dMRI). Specifically, we focused on the Neurite Density Index (NDI) from NODDI, which in WM captures the signal from axons.MethodWe identified 251 participants (126 cognitively normal, 58 MCI, 67 dementia) from the Mayo ADRC and Mayo Clinic Study of Aging with Aβ‐PET, Tau‐PET, and multi‐shell dMRI who were Aβ‐positive (mean age of 77 years and 55% male). We limited to Aβ‐positive participants because Aβ deposition accelerates tau deposition. For Tau‐PET, we estimated SUVRs from three regions corresponding to Braak I/II, III/IV, and V/VI (Figure 1.a). To understand the associations between Tau‐PET SUVRs at three Braak regions and NDIs at 30 candidate WM tracts, we implemented three analyses methods ‐ Pearson correlation, linear regression, and multilayer perceptron machine learning regression.ResultHigher tau burden in all three Braak regions was associated with lower neurite density in 10 significant WM tracts (Table 1). Interestingly, uncinate fasciculus (UNC) was found significantly associated with all three Braak regions. All three analysis methods consistently showed differential involvement of WM pathways for Braak staging such as ‐ Cingulum adjoining Hippocampus (CGH) for Braak I/II regions, Medial Temporal WM (MTWM) for Braak III/IV regions, and Inferior Occipital WM (IOWM) for Braak V/VI regions (Figure 1.b). The linear regression and machine learning regression models also suggested that WM measurements reflected 18‐35% of variance due to tau depositionConclusionWe found that CGH and UNC WM were the most impacted ROIs associated with Braak I/II regions as expected. UNC, directly connecting anterior temporal lobes to the orbitofrontal cortex, was a key WM tract identified as important for all Braak I‐VI regions and warrants further investigation in the study of AD progression. This study highlights the significant impact tau deposition may have on WM. This work also demonstrates the feasibility of using NODDI as an informative supplementary imaging modality for monitoring AD progression.

Blood-based biomarkers offer strong potential to revolutionize diagnosis, trial enrollment, and treatment monitoring in Alzheimer’s disease (AD). However, further advances are needed before these biomarkers can achieve wider deployment beyond selective research studies and specialty memory clinics, including the development of frameworks for optimal interpretation of biomarker profiles. We hypothesized that integrating Alzheimer’s disease genetic risk score (AD-GRS) data would enhance the diagnostic value of plasma AD biomarkers by better capturing extant disease heterogeneity. Analyzing 962 individuals from a population-based sample, we observed that an AD-GRS was independently associated with amyloid PET levels (an early marker of AD pathophysiology) over and above APOE ε4 or plasma p-tau181, Aβ42/40, GFAP, or NfL. Among individuals with a high or moderately high plasma p-tau181, integrating AD-GRS data significantly improved classification accuracy of amyloid PET positivity, including the finding that the combination of a high AD-GRS and high plasma p-tau181 outperformed p-tau181 alone in classifying amyloid PET positivity (88% vs. 68%; p = 0.001). A machine learning approach incorporating plasma biomarkers, demographics, and the AD-GRS was highly accurate in predicting amyloid PET levels (90% training set; 89% test set), and Shapley value analyses (an explainer method based in cooperative game theory) indicated that the AD-GRS and plasma biomarkers had differential importance in explaining amyloid deposition across individuals. Polygenic risk for AD dementia appears to account for a unique portion of disease heterogeneity which could noninvasively enhance the interpretation of blood-based AD biomarker profiles in the population.