Abstract Brain ageing involves microstructural changes that vary across tissue types and even within regions of those tissues, leading to functional and cognitive alterations. Quantitative MRI offers sensitivity to tissue properties, enabling the identification of differential ageing patterns and distinguishing physiological ageing from pathological changes. In this study, we analysed quantitative MRI data from 293 healthy adults (median age: 52; interquartile range: 36–66; age range: 18–79 years). We applied a multiparametric quantitative MRI approach, including longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*), and Quantitative Susceptibility Mapping, to model normal ageing effects on quantitative MRI metrics across regions using second-order polynomial regression, adjusting for sex, education, and cognition. Peak ages in turning points derived from quadratic fits were extracted to capture region-specific age-related differences across cortical grey matter, superficial white matter, and white matter bundles. According to the results, R1 showed the most robust age modelling, whereas R2* and susceptibility presented greater regional variability. Peak ages varied substantially across regions, reflecting the heterogeneity of age-related microstructural differences. Based on quadratic fits, we identified a spatial gradient in quantitative MRI ageing patterns, with earlier peak ages in white matter bundles, followed by superficial white matter, and culminating in cortical grey matter. This gradient followed a posterior-to-anterior pattern in the cortex and an inferior-to-superior pattern in white matter bundles, consistently observed across all three quantitative MRI metrics. Our study presents exploratory mapping of region- and tissue-specific ageing patterns across brain grey and white matter using multiparametric quantitative MRI, offering insights to support future normative healthy ageing research.
Chen et al. (Mon,) studied this question.