ArticleCerebral cortex (New York, N.Y. : 1991)2021
A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex.
Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Mediating effect of cognitive reserve on associations between processing speed and memory in older adults with and without multiple sclerosis.Neuropsychology · 2026Article
- Mapping heterogeneous region- and tissue-specific brain ageing patterns using quantitative MRI.Brain communications · 2026Article
- Quantitative MRI of the hippocampus reveals microstructural trajectories of aging and Alzheimer's disease pathology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Age- and Sex-Specific Patterns in Adult Brain Network Segregation.Human brain mapping · 2025Article
- Longitudinal reproducibility of brain and spinal cord quantitative MRI biomarkers.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Normative trajectories of RImaging neuroscience (Cambridge, Mass.) · 2025Article
- T1 relaxation time is prolonged in healthy aging: a whole brain study.Turkish journal of medical sciences · 2023Article
- How does the brain age in individuals with multiple sclerosis? A systematic review.Frontiers in neurology · 2023Review
- Post mortem brain temperature and its influence on quantitative MRI of the brain.Magma (New York, N.Y.) · 2022Article
- Quantitative measurement of macromolecular tissue properties in white and gray matter in healthy aging and amnestic MCI.NeuroImage · 2021Article
- Local Brain-Age: A U-Net Model.Frontiers in aging neuroscience · 2021Article
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8 authors.
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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.
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