A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex

A. Erramuzpe*, R. Schurr, J. D. Yeatman, I. H. Gotlib, M. D. Sacchet, K. E. Travis, H. M. Feldman, A. A. Mezer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Brain development and aging are complex processes that unfold in multiple brain regions simultaneously. Recently, models of brain age prediction have aroused great interest, as these models can potentially help to understand neurological diseases and elucidate basic neurobiological mechanisms. We test whether quantitative magnetic resonance imaging can contribute to such age prediction models. Using R1, the longitudinal rate of relaxation, we explore lifespan dynamics in cortical gray matter. We compare R1 with cortical thickness, a well-established biomarker of brain development and aging. Using 160 healthy individuals (6-81 years old), we found that R1 and cortical thickness predicted age similarly, but the regions contributing to the prediction differed. Next, we characterized R1 development and aging dynamics. Compared with anterior regions, in posterior regions we found an earlier R1 peak but a steeper postpeak decline. We replicate these findings: Firstly, we tested a subset (N = 10) of the original dataset for whom we had additional scans at a lower resolution; and second, we verified the results on an independent dataset (N = 34). Finally, we compared the age prediction models on a subset of 10 patients with multiple sclerosis. The patients are predicted older than their chronological age using R1 but not with cortical thickness.

Original languageAmerican English
Pages (from-to)1211-1226
Number of pages16
JournalCerebral Cortex
Volume31
Issue number2
DOIs
StatePublished - 1 Feb 2021

Bibliographical note

Publisher Copyright:
© 2020 The Author(s). Published by Oxford University Press. All rights reserved.

Keywords

  • aging
  • cortex
  • multiple sclerosis
  • prediction
  • qMRI

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