White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

• Published in: NeuroImage (Orlando, Fla.) Vol. 224; p. 117441
• Main Authors: Beck, Dani, de Lange, Ann-Marie G., Maximov, Ivan I., Richard, Geneviève, Andreassen, Ole A., Nordvik, Jan E., Westlye, Lars T.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.01.2021; Elsevier Limited; Elsevier
• Subjects: Accuracy; Adaptation; Adolescent; Adult; Age; Aged; Aged, 80 and over; Ageing; Aging; Anisotropy; Brain - diagnostic imaging; Brain age; Brain architecture; Cross-Sectional Studies; Datasets; Diffusion; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Female; Humans; Image Processing, Computer-Assisted; Learning algorithms; Life span; Longitudinal; Longitudinal Studies; Machine learning; Magnetic resonance imaging; Male; Medical imaging; Middle Aged; Multi-shell; Neuroimaging; Noise; Predictions; Substantia alba; White matter; White Matter - diagnostic imaging; Young Adult; Multi-shell; Longitudinal; Diffusion; White matter; Ageing; Brain age
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2020.117441

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.