Unique information from common diffusion MRI models about white-matter differences across the human adult lifespan

Abstract

Diffusion Magnetic Resonance Imaging (dMRI) is sensitive to white matter microstructural changes across the human lifespan. Several models have been proposed to provide more sensitive and specific metrics than those provided by the conventional Diffusion Tensor Imaging (DTI) analysis. However, previous results using different metrics have led to contradictory conclusions regarding the effect of age on fibre demyelination and axonal loss in adults. Moreover, it remains unclear whether these metrics provide distinct information about the effects of age, for example, on different white-matter tracts. To address this, we analysed dMRI data from 651 adults approximately uniformly aged from 18 to 88 years in the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) cohort, using six dMRI metrics: Fractional Anisotropy (FA) from standard DTI; Mean Signal Diffusion (MSD) and Mean Signal Kurtosis (MSK) from Diffusional Kurtosis Imaging (DKI) applied to directional averaged diffusion-weighted signals; and Neurite Density Index (NDI), Orientation Dispersion Index (ODI), and isotropic Free water volume fraction (Fiso) estimated from Neurite Orientation Dispersion and Density Imaging (NODDI). Averaging across white-matter regions-of-interest (ROIs), second-order polynomial fits revealed that MSD, MSK, and Fiso showed the strongest effects of age, with significant quadratic components suggesting more rapid and sometimes inverted effects in old age. Analysing the data in different age subgroups revealed that some apparent discrepancies in previous studies may be explained by the use of cohorts with different age ranges. Factor analysis of the six metrics across all ROIs revealed three independent factors that can be associated to 1) tissue microscopic properties (e.g., differences in fibre density/myelin), 2) free-water contamination, and 3) tissue configuration complexity (e.g., crossing, dispersing, fanning fibres). While FA captures a combination of different factors, other dMRI metrics are strongly aligned to specific factors (NDI and MSK with Factor 1, Fiso with Factor 2, and ODI with Factor 3). To assess whether directional diffusion and kurtosis quantities provide additional information about the effects of age, further factor analyses were also performed, which showed that additional information about the effects of age may be present in radial and axial kurtosis estimates (but not standard axial and radial diffusivity). In summary, our study offers an explanation for previous discrepancies reported in dMRI ageing studies and provides further insights on the interpretation of different dMRI metrics in the context of white-matter microstructural properties.

Multimodal MEG and microstructure-MRI investigations of the human hippocampal scene network

“…Density Index (NDI); and Orientation Dispersion (OD). This larger metric space was then reduced through PCA, a technique that has been shown to be effective in capturing biologically informative features of white matter microstructure (Chamberland et al, 2019;Geeraert et al, 2020;Henriques et al, 2023;Read et al, 2023). The results from the PCA (KMO: 0.66, sphericity: p<0.0001; Figure 3) showed that 94% of the microstructure data variance was accounted for by the first two principal components, PC1 and PC2.…”

“…Microstructure data were reduced through PCA using the same methods as those described in Read et al (2023). This approach has been shown to be effective in capturing biologically informative features in previous microstructure datasets (Chamberland et al, 2019;Geeraert et al, 2020;Henriques et al, 2023). In short, the Bartlett test was used to assess the data's appropriateness for PCA, the prcmp function in R (R Core Team, 2019) was then used to apply PCA to centred and scaled data, and the sampling adequacy of the results was tested using the Kaiser-Meyer-Olkin (KMO) test (from the R 'Psych' package; Revelle, 2020).…”