Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.04.21.053850v1?rss=1 Authors: Beck, D., de Lange, A.-M., Maximov, I. I., Richard, G., Andreassen, O. A., Nordvik, J. E., Westlye, L. T. Abstract: The macro- and microstructural architecture of human brain white matter undergo 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), in particular diffusion MRI (dMRI), which enables visualization 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 (56.98% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the 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], medial diffusivity [MD], radial diffusivity [RD]) were largely consistent with previous research, and that all diffusion models indicated lower white matter integrity with older age. Linear mixed effects models and brain age prediction showed that the FA fine metric of the RSI model and orientation dispersion (OD) metric of the NODDI model showed highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) yield the capability of detecting sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI. Key words: ageing, white matter, multi-shell, longitudinal, diffusion, brain age Copy rights belong to original authors. Visit the link for more info