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<p>Brain age estimation using magnetic resonance imaging is a promising biomarker for detecting accelerated aging and neurodegenerative disorders. However, the development of robust clinical models is severely hampered by the “Effects of Site”, where scanner-specific biases obscure biological signals in multi-center datasets. In this study, we propose a novel harmonization strategy, Inter-Site SMOTE, which generates synthetic training data by interpolating between age- and gender-matched participants from different sites. We hypothesize that these synthetic samples populate the sparse regions between site distributions, effectively bridging domain gaps while preserving biological integrity. We systematically evaluated this approach using four large neuroimaging datasets (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(N=2031\)</EquationSource> </InlineEquation>) in a leave-one-site-out regression task. Our results demonstrate that Inter-Site SMOTE significantly improves generalization to unseen scanners compared to standard data pooling. Crucially, we show that standard statistical harmonization (ComBat) fails to improve predictive performance in this setting due to inference-time assumptions, whereas our data-centric approach enhances robustness. Furthermore, we provide empirical evidence that the improvement is driven by the specific geometry of cross-site interpolation, as intra-site augmentation failed to yield comparable gains. This work presents a simple, effective solution for multi-site harmonization that circumvents the limitations of statistical adjustment methods, paving the way for more generalizable prediction models.</p>

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Data harmonizing via interpolation applied to brain age prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • Nicolás Nieto,
  • Aditi Asati,
  • Kshitij Jadhav,
  • Kaustubh R. Patil

摘要

Brain age estimation using magnetic resonance imaging is a promising biomarker for detecting accelerated aging and neurodegenerative disorders. However, the development of robust clinical models is severely hampered by the “Effects of Site”, where scanner-specific biases obscure biological signals in multi-center datasets. In this study, we propose a novel harmonization strategy, Inter-Site SMOTE, which generates synthetic training data by interpolating between age- and gender-matched participants from different sites. We hypothesize that these synthetic samples populate the sparse regions between site distributions, effectively bridging domain gaps while preserving biological integrity. We systematically evaluated this approach using four large neuroimaging datasets ( \(N=2031\) ) in a leave-one-site-out regression task. Our results demonstrate that Inter-Site SMOTE significantly improves generalization to unseen scanners compared to standard data pooling. Crucially, we show that standard statistical harmonization (ComBat) fails to improve predictive performance in this setting due to inference-time assumptions, whereas our data-centric approach enhances robustness. Furthermore, we provide empirical evidence that the improvement is driven by the specific geometry of cross-site interpolation, as intra-site augmentation failed to yield comparable gains. This work presents a simple, effective solution for multi-site harmonization that circumvents the limitations of statistical adjustment methods, paving the way for more generalizable prediction models.