<p class="first" id="d15600580e150">The macro- and microstructural architecture of
human brain white matter undergoes
substantial alterations throughout development and ageing. Most of our understanding
of the spatial and temporal characteristics of these lifespan adaptations come from
magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation
and quantification of brain white matter with unprecedented sensitivity and detail.
However, with some notable exceptions, previous studies have relied on cross-sectional
designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional
single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval:
15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI
models to investigate age trajectories in healthy individuals aged 18 to 94 years
(57.12% women). Using linear mixed effect models and machine learning based brain
age prediction, we assessed the age-dependence of diffusion metrics, and compared
the age prediction accuracy of six different diffusion models, including diffusion
tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density
imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment
(SMT-mc), and white matter tract integrity (WMTI). The results showed that the age
slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity
[MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with
previous research, and that the highest performing advanced dMRI models showed comparable
age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's
theorem analysis showed that the 'FA fine' metric of the RSI model and 'orientation
dispersion' (OD) metric of the NODDI model showed the highest sensitivity to age.
The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI)
provide sensitive measures of age-related microstructural changes of white matter
in the brain that complement and extend the contribution of conventional DTI.
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