Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants

Abstract

Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions.

Keywords: Aging; Diffusion MRI; Tractography; Volume; White matter.

Fig. 1

We investigated microstructure and macrostructure features of 71 pathways virtually dissected using TractSeg (Wasserthal et al. 2018), visualized and organized into association, limbic, commissural, thalamic, striatal, and projection and cerebellar pathways

Fig. 2

What and where changes occur during aging. The beta coefficient from linear mixed effects modeling is shown as a matrix for all features across all pathways, and also shown as boxplots for both microstructural features (left) and macrostructural features (right). Boxplots are shown separated by pathway types. Results are shown for TractSeg-derived pathways

Fig. 3

Bundle-based visualization of associations with age. Bundles that have significant associations with age are colored based on Beta-association coefficient from linear mixed-effects models, for 5 selected features. Only those with statistically significant change with age are displayed. Results are shown for TractSeg-derived pathways

Fig. 4

Example microstructural and macrostructural associations for a projection white matter tract. A 3D illustration of the anterior thalamic radiation (ThA) is shown (A), as it exhibited significant microstructural (B) and microstructural decline (C). For each microstructural and macrostructural plot, colored datapoints and lines represent individual cohorts

Fig. 5

Example microstructural and macrostructural associations for a commissural white matter tract. A 3D illustration of the forceps minor (CCfmin) is shown (A), as it exhibited significant microstructural (B) and microstructural decline (C). For each microstructural and macrostructural plot, colored datapoints and lines represent individual cohorts

Fig. 6

Along-tract analysis for example association, commissural, thalamic, limbic, projection, and striatal pathways. Streamlines are color coded red-to-blue based on position along pathway, while plots show beta coefficient from linear mixed modelling for FA, MD, AD, and RD. Following the bundle analytics framework (Chandio et al. 2020), positions that show significant age-related effects at a significance level of p < 0.001 are marked with circles