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FreeSurfer version-shuffling can enhance brain age predictions.

Max Korbmacher1,2, Lars T Westlye3,4, Ivan I Maximov1

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

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Summary

FreeSurfer version differences introduce minor variability in brain age predictions. Shuffling data enhances model performance and generalizability for robust brain age estimation.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Machine Learning

Background:

  • Brain age prediction models estimate brain health using neuroimaging data.
  • FreeSurfer is a widely used software for neuroimaging analysis.
  • Variability in software versions can impact reproducibility and model performance.

Purpose of the Study:

  • To investigate the impact of FreeSurfer version differences on brain age prediction models.
  • To assess the influence of training-test splits on model variability.
  • To evaluate methods for improving model generalizability.

Main Methods:

  • Utilized FreeSurfer software with different versions for neuroimaging data processing.
  • Employed various training-test splitting strategies, including repeated random splits.
  • Applied data shuffling techniques to assess their effect on model performance.

Main Results:

  • FreeSurfer version differences introduced small average variability in brain age predictions.
  • Variability was algorithm and individual-difference dependent.
  • Repeated random train-test splitting demonstrated its advantage.
  • Shuffling FreeSurfer version-dependent data improved model performance and generalizability.

Conclusions:

  • While FreeSurfer version differences have a limited impact on average brain age prediction, they can introduce specific variabilities.
  • Repeated random splitting and data shuffling are effective strategies to enhance the robustness and generalizability of brain age prediction models.