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Background: The role of white matter (WM) neurodegeneration in Alzheimer's disease (AD) is poorly understood. Fine‐grained sequencing of WM neurodegeneration could provide new avenues for early diagnosis and novel disease‐modifying therapies to slow or halt AD progression. Previous approaches are limited by their coarse granularity and incomplete disease coverage. In this study, we apply event‐based disease progression modelling (DPM) (Fonteijn et al, Neuroimage, 2012), which can provide fine‐grained ordering, to estimate progression of regional WM abnormality in AD. Method: Diffusion tensor imaging (DTI) markers of WM abnormality and Freesurfer volumetric markers of grey matter (GM) atrophy were obtained from ADNI. DTI markers were hemisphere‐averaged and GM volumes were normalised by head size. Eleven WM ROIs with reported AD abnormality and 4 reference GM ROIs were selected for sequencing (abbreviations in Table 1), yielding complete data for 83 cognitively normal subjects, 40 AD patients and 102 MCI subjects. Event‐based DPM (github.com/ucl‐pond/kde_ebm) was used to estimate a data‐driven sequence of neurodegeneration and its uncertainty (positional variance), and individuals were assigned to their most likely stage (Young et al., Brain, 2014). Resampling analysis assessed the robustness of the estimated sequence. Result: Fig. 1 shows the first data‐driven sequence of WM abnormalities in AD, including positional variance, with GM abnormality events for reference. The earliest WM events are increases in mean diffusivity (MD) in CGH and axial diffusivity (AxD) in FX and BCC. Subsequently, a cascade of AxD events in corpus callosum regions precedes abnormality in other medial temporal lobe WM structures. Fig. 2 shows the sequence is relatively robust under resampling, with low positional variance and similar median positions for most ROIs. Subject staging (Fig. 3, 4) shows that the fine‐grained approach splits MCI subjects with hippocampal atrophy into those with hippocampal atrophy and those with additional increased MD in CGH. Conclusion: This study finds WM microstructural abnormality among the earliest events in AD. Early hippocampal WM abnormality and corpus callosum ordering are consistent with early memory deficits in AD and inferences from cruder group‐based comparisons. The order suggests Wallerian degeneration rather than retrogenesis is the primary driver of WM neurodegeneration in AD. [ABSTRACT FROM AUTHOR]

Background: Neurite Orientation Dispersion and Density Imaging (NODDI) provides in‐vivo indices of neurite density (NDI) and orientation dispersion (ODI) within the tissue compartment of each voxel. However, NDI and ODI are treated equally when calculating region of interest (ROI) means, despite tissue fraction (TF) varying within regions undergoing neurodegeneration. Covariation between TF and cortical NODDI measures bias these conventional means and we recommend using tissue‐weighted averages to address this. Method: In this study, we included 22 healthy controls and 33 individuals affected by Young‐Onset Alzheimer's disease (YOAD, see Table 1 for demographics) with suitable diffusion‐weighted and T1‐weighted 3T MR images. Diffusion data were corrected for eddy currents, motion and susceptibility artefacts before fitting the NODDI model to produce NDI, ODI, isotropic volume fraction (ISO) and TF maps (TF=1‐ISO). T1w images were parcellated into cortical ROIs using Geodesic Information Flow (Cardoso et al., IEEE Trans.Med.Im.; 34:1976‐1988, 2015). Five bilateral ROIs expected to undergo neurodegeneration were analysed (Precuneus, Fusiform Gyrus, Superior Parietal, Middle and Inferior Temporal cortex). ROIs were resampled into native diffusion space and two regional measures calculated: 1) conventional, unweighted NDI and ODI averages and 2) tissue‐weighted averages using voxel TF as weights. Within‐participant differences between conventional and tissue‐weighted measures were calculated. Spearman's rank tested correlations and Wilcoxon tests evaluated within‐ and between‐participant differences (Bonferroni adjusted for multiple comparisons). Result: TF positively correlated with GM volume (rs range=0.33‐0.68,p<0.05) in all ROIs except the left fusiform gyrus (rs=0.31,p=0.06) (Figure 1). YOAD individuals had lower TF than healthy controls in all ROIs (Figure 2a), and lower volumes in all ROIs except the right fusiform gyrus (W=475,p=0.27) (Figure 2b). NDI showed small positive/negative biases in six of the ten ROIs (Figure 3a), while ODI showed significant positive biases across all ROIs (Figure 3b). Biases decreased as TF increased towards its maximum of one (Figure 4a‐4b). Conclusion: Lower cortical volumes in YOAD were associated with lower TF and higher bias, suggesting a greater risk for misestimation of cortical region NODDI metrics in studies involving neurodegenerative disease. We recommend tissue‐weighted averages to account for varying intra‐regional TF in NODDI measures. [ABSTRACT FROM AUTHOR]

Background: Mapping axonal loss in young‐onset Alzheimer’s disease (YOAD) can provide a clearer picture of neurodegenerative processes. Axonal loss has been estimated using metrics derived from diffusion MRI (dMRI). However, these measures are also sensitive to myelin, thus cannot unambiguously disentangle axon loss and demyelination. Here, we address this with multi‐modal imaging, combining dMRI with magnetisation transfer saturation (MTsat) MRI, a technique sensitive specifically to myelin. Method: Neurite density index (NDI), a dMRI estimate from NODDI, relates to axon volume fraction (AVF), an axon density estimate unconfounded by myelin: AVF=(1‐MVF)(1‐FWF)NDI (Stikov et al. Neuroimage; 118:397‐405, 2015). MVF is the myelin volume fraction, an estimate of myelin density, and FWF the free water fraction from NODDI. MVF is estimated from MTsat imaging (Mohammadi et al. Frontiers in Neuroscience; 9, 9:441, 2015). Maps of AVF, MVF and NDI were computed for 21 healthy controls and 30 YOAD patients who underwent NODDI‐compatible dMRI and MTsat MRI. Tract‐based spatial statistics (TBSS) was used to map significant changes (p<0.05, FWE‐corrected) along white matter (WM) skeleton (Fig. 2, lower) of the population template. Result: Figure 1 shows the population‐averaged AVF and MVF of each point along the WM skeleton, demonstrating NDI is influenced by both AVF and MVF. While AVF and NDI correlate strongly (R2=0.46, p<5e‐10), suggesting NDI is primarily a marker of axon density, MVF also contributes to the observed NDI variation (R2=0.16, p<5e‐10). Figure 2 shows TBSS group differences in these metrics. MVF reductions in fronto‐occipital regions, indicating demyelination, overlap with NDI reductions but not AVF changes (Fig. 2A). AVF reductions in the splenium, indicating axonal loss, did not overlap with NDI changes (Fig. 2B). This demonstrates sub‐optimal specificity and sensitivity of NDI to axonal loss. Most AVF reductions; in the posterior tracts, fornix and the corpus callosum splenium, areas associated with symptoms of YOAD; were accompanied by decreased NDI. Concomitant MVF reductions occurred in posterior areas (Fig 2C). Conclusion: This study presents a multi‐modal imaging approach to disambiguate markers of axonal loss and demyelination, effects previously entangled in single modality DWI analysis. This technique may provide new insight into in vivo pathological processes in neurodegenerative diseases. [ABSTRACT FROM AUTHOR]