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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.
Neuroimage. Reports
|June 26, 2025
Summary
FreeSurfer version differences introduce minor variability in brain age predictions. Shuffling data enhances model performance and generalizability for robust brain age estimation.
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.
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