# Mapping heterogeneous region- and tissue-specific brain ageing patterns using quantitative MRI

## TL;DR

This study uses MRI scans to map how different brain regions and tissues change with age, revealing patterns that could help distinguish normal aging from disease.

## Contribution

The study introduces a multiparametric qMRI approach to explore region- and tissue-specific brain aging patterns in healthy adults.

## Key findings

- R1 showed the most robust age modeling, while R2* and susceptibility had greater regional variability.
- Peak ages varied across brain regions, showing a posterior-to-anterior gradient in the cortex and an inferior-to-superior gradient in white matter.
- The study identified a consistent spatial gradient in aging patterns across cortical grey matter, superficial white matter, and white matter bundles.

## 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 (qMRI) offers sensitivity to tissue properties, enabling the identification of differential ageing patterns and distinguishing physiological ageing from pathological changes. In this study, we analysed qMRI data from 293 healthy adults (median age: 52; interquartile range: 36–66; age range: 18–79 years). We applied a multiparametric qMRI approach, including longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*) and Quantitative Susceptibility Mapping, to model normal ageing effects on qMRI 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 (sWM) and white matter (WM) 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 qMRI ageing patterns, with earlier peak ages in WM bundles, followed by sWM and culminating in cortical GM. This gradient followed a posterior-to-anterior pattern in the cortex and an inferior-to-superior pattern in WM bundles, consistently observed across all three qMRI metrics. Our study presents exploratory mapping of region- and tissue-specific ageing patterns across brain grey and WM using multiparametric qMRI, offering insights to support future normative healthy ageing research.

X. Chen et al. reported that quantitative MRI identified distinct patterns of brain ageing. Analysing data from healthy adults, they showed region-specific age variations and gradients in tissue properties. These findings highlight heterogeneity in healthy brain ageing and provide a reference for distinguishing normal from pathological changes.

Graphical Abstract