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Updated: May 1, 2026

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

Xinjie Chen1,2,3,4, Mario Ocampo-Pineda1,2,3, Po-Jui Lu1,2,3

  • 1Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, 4123 Allschwil, Switzerland.

Brain Communications
|February 16, 2026
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Summary
This summary is machine-generated.

Brain aging shows varied microstructural changes across regions. Quantitative MRI (qMRI) reveals a posterior-to-anterior and inferior-to-superior gradient in brain aging patterns, highlighting tissue-specific differences.

Keywords:
age modellingbrain ageinghealthy ageingmultiparametric MRInormative model

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Area of Science:

  • Neuroimaging
  • Brain Aging Research
  • Quantitative MRI

Background:

  • Brain aging involves complex microstructural changes affecting function and cognition.
  • Quantitative MRI (qMRI) is sensitive to tissue properties, aiding in distinguishing normal aging from pathology.
  • Understanding region-specific aging patterns is crucial for healthy aging research.

Purpose of the Study:

  • To model normal aging effects on quantitative MRI metrics across different brain regions and tissue types.
  • To identify region-specific age-related differences using multiparametric qMRI.
  • To explore spatial gradients in brain aging patterns.

Main Methods:

  • Analysis of qMRI data (longitudinal relaxation rate R1, apparent transverse relaxation rate R2*, and Quantitative Susceptibility Mapping) from 293 healthy adults.
  • Application of second-order polynomial regression to model aging effects, adjusting for sex, education, and cognition.
  • Extraction of peak ages from quadratic fits to characterize region-specific aging.

Main Results:

  • Longitudinal relaxation rate (R1) demonstrated robust age modeling, while R2* and susceptibility showed higher regional variability.
  • Significant regional variations in peak ages were observed, indicating heterogeneous microstructural aging.
  • A consistent posterior-to-anterior gradient in cortical aging and an inferior-to-superior gradient in white matter aging were identified across qMRI metrics.

Conclusions:

  • Multiparametric qMRI effectively maps region- and tissue-specific aging patterns in the brain.
  • The identified spatial gradients provide insights into the heterogeneity of healthy brain aging.
  • This research supports the development of normative data for healthy aging studies.