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Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
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Related Experiment Video

Updated: Jul 19, 2025

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
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Considerations on brain age predictions from repeatedly sampled data across time.

Max Korbmacher1,2,3, Meng-Yun Wang3,4, Rune Eikeland3,4

  • 1Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.

Brain and Behavior
|August 17, 2023
PubMed
Summary

Brain age estimation using MRI shows weak correlations with actual age in individuals over time. Magnetic resonance imaging (MRI) acquisition parameters like field strength can introduce biases, impacting clinical applications.

Keywords:
T1-weightedbrain agedensely sampled MRIfield strengthmagnetic resonance imagingscan quality

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Area of Science:

  • Neuroimaging
  • Radiology
  • Biomarkers

Background:

  • Brain age, derived from magnetic resonance imaging (MRI), serves as a general health indicator.
  • Further validation is needed for its clinical application.
  • This study investigates brain age stability and influencing factors.

Purpose of the Study:

  • To assess brain age prediction consistency in individuals over time.
  • To identify the influence of MRI acquisition parameters on brain age.
  • To validate findings using age-matched healthy controls.

Main Methods:

  • Utilized densely sampled T1-weighted MRI data from four individuals.
  • Employed a pretrained deep learning model for brain age prediction.
  • Validated results with two cross-sectional datasets.

Main Results:

  • Observed small within-subject correlations between chronological age and predicted brain age.
  • Identified magnetic resonance imaging (MRI) field strength as a significant influence on brain age.
  • Found inconclusive effects of scan quality on brain age predictions.

Conclusions:

  • Model bias, training data characteristics, and acquisition parameters like field strength may explain weak longitudinal age-brain age relationships.
  • Clinical use of brain age models requires careful consideration of data acquisition variability to avoid biases.