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@ARTICLE{Antonopoulos:1047431,
      author       = {Antonopoulos, Georgios and More, Shammi and Eickhoff, Simon
                      B. and Raimondo, Federico and Patil, Kaustubh R.},
      title        = {{R}egion-wise stacking ensembles for estimating brain-age
                      using structural {MRI}},
      journal      = {Computers in biology and medicine},
      volume       = {198},
      issn         = {0010-4825},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2025-04294},
      pages        = {111182 -},
      year         = {2025},
      abstract     = {Predictive modeling using structural magnetic resonance
                      imaging (MRI) data is a prominent approach to study
                      brain-aging. Machine learning frameworks have been employed
                      to improve predictions and explore healthy and accelerated
                      aging due to diseases. The high-dimensional MRI data pose
                      challenges to building generalizable and interpretable
                      models as well as for data privacy. Common practices are
                      resampling or averaging voxels within predefined parcels
                      which reduces anatomical specificity and biological
                      interpretability. Effectively, naive fusion by averaging can
                      result in information loss and reduced accuracy. We present
                      a conceptually novel two-level stacking ensemble (SE)
                      approach. The first level comprises regional models for
                      predicting individuals' age based on voxel-wise information,
                      fused by a second-level model yielding final predictions.
                      Eight data fusion scenarios were explored using Gray matter
                      volume (GMV) estimates from four large datasets. Performance
                      measured using mean absolute error (MAE), R2, correlation
                      and prediction bias, showed that SE outperformed the
                      region-wise averages. The best performance was obtained when
                      first-level regional predictions were obtained as
                      out-of-sample predictions on the application site with
                      second-level models trained on independent and site-specific
                      data (MAE = 4.75 vs baseline regional mean GMV MAE = 5.68).
                      Performance improved as more datasets were used for
                      training. First-level predictions showed improved and more
                      robust aging signal providing new biological insights and
                      enhanced data privacy. Overall, the SE improves accuracy
                      compared to the baseline while preserving or enhancing data
                      privacy. Finally, we show the utility of our SE model on a
                      clinical cohort showing accelerated aging in cognitively
                      impaired and Alzheimer's disease patients.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.compbiomed.2025.111182},
      url          = {https://juser.fz-juelich.de/record/1047431},
}