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 (F_iso) 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 F_isoshowed 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, F_isowith 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.

Keywords: MRI; age; diffusion; modelling; white matter.

Fig. 1.

Representative maps of the six diffusion MRI metrics (FA, MSD, MSK, NDI, ODI, F_iso) for two young adults (26 and 25 years old, panelsAandB) and for two elders (79 years old, panelsCandD). Implausible NODDI estimates in regions containing brain ventricles are removed by setting NDI to 0 and ODI to 1 for voxels with F_iso> 0.9 (note that estimating NDI and ODI is degenerate for F_iso≈ 1, c.f.Eq. 2).

Fig. 2.

Mean diffusion metrics extracted from the voxels of all white-matter ROIs as a function of participant’s age, for each dMRI metric:(A)Fractional Anisotropy (FA) from DTI;(B)Mean Signal Diffusion (MSD, in µm²/ms) from DKI;(C)Mean Signal Kurtosis (MSK) from DKI;(D)Neurite Density Index (NDI) from NODDI;(E)Orientation Dispersion Index (ODI) from NODDI; and(F)Volume Fraction of Free isotropic water diffusion (F_iso) from NODDI. The proportion of variance (R²) explained by Linear (L) and Quadratic (Q) components of a second-order polynomial fit of age (with covariates of sex and age-by-sex interactions; see text) is shown at the top of each panel.

Fig. 3.

Proportion of variance explained (R²) by linear and quadratic effects of age on the six diffusion metrics (FA, MSD, MSK, NDI, ODI, and F_isofrom left to right) for each ROI separately in different rows. The ROIs are sorted in a descending manner according to their mean R²values across ROIs. Abbreviations: Ant – anterior; Fasc – Fasciculus; Inf – Inferior, Ped – Peduncle; Post – Posterior; Sup – Superior; Occip – Occipital

Fig. 4.

Linear effects of age for the six diffusion metrics (FA, MSD, MSK, NDI, ODI, and F_isofrom panelsAtoF) within three age subgroups (left to right subpanels), overlaid on the JHU-ICBM FA template—in each panel, results are displayed for a coronal (right) and an axial (left) slice. Correction for multiple comparison is performed using FDR (q < 0.05). Significant negative and positive age effects are colour-coded by blue and red intensities respectively; while non-significant effects are shown in green.

Fig. 5.

Pearson correlation coefficient (R) between each pair of the metrics. The upper right triangle shows raw correlations; the lower left triangle shows correlations after removing linear and quadratic effects of age.

Fig. 6.

Loadings of three factors from factor analysis across the six diffusion metrics (upper three panels) and their profiles against age (lower three panels).

Fig. 7.

Loadings of three factors from factor analysis on each of the 27 ROIs.

Fig. 8.

Effects of age (R²from second-order polynomial fit) for each factor and each of the 27 ROIs.

Fig. 9.

Factor analysis when adding standard diffusion and kurtosis metrics reconstructed from standard DKI. Percentage of total variance explained by each principal component when considering:(A)the six main metrics of this study (FA, MSD, MSK, NDI, ODI, F_iso);(B)the six main metrics of this study plus three diffusion metrics computed from standard DTI: mean, radial, and axial diffusivity (MD, RD, AD);(C)as in(B), plus three diffusion kurtosis metrics: mean, radial, and axial kurtosis (MK, RK, AK).(D)Loadings of four factors from factor analysis when considering 12 diffusion metrics (upper three subpanels) and their profiles against age (lower three subpanels).