% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Antonopoulos:1022029,
      author       = {Antonopoulos, Georgios and More, Shammi and Raimondo,
                      Federico and Patil, Kaustubh},
      title        = {{S}tacking ensemble for age-prediction improves performance
                      and privacy},
      reportid     = {FZJ-2024-01166},
      year         = {2023},
      abstract     = {Brain-age prediction (BAP) using structural MRI has shown
                      great potential for studying healthy aging anddisease. Two
                      major desirable properties for BAP are high accuracy and
                      data privacy. We propose astacking ensemble model (SEM)
                      which improves both compared to current implementations.Our
                      SEM consists of two levels (L0 and L1). At L0, we used an
                      873-parcel atlas to group gray-mattervolume voxels, and
                      trained one GLMnet model for each parcel. The out-of-sample
                      (OOS, using 3-fold cross-validation) predictions from all L0
                      models were used as features to train a GLMnet model at L1
                      whichprovides the final age-prediction. To make predictions
                      on an independent test-set, L0 models were trainedon the
                      whole dataset (Figure 1).We explored two different ways to
                      train models at L0 and L1, i.e., i) using pooled data from
                      different sites,and ii) treating each site separately and
                      then averaging their outcomes. To compare with
                      currentstandards we also tested models using average GMV in
                      each parcel as inputs of L1. Additionally, to test thecase
                      where enough data is available at the test site, we
                      estimated L0-level OOS predictions on the testdata. These
                      were then used to obtain predictions using L1 models. The
                      former schemes provide differentlevels and types of privacy
                      advantage. The latter provides an advantage for clinical
                      applications, as onlyL0-level predictions need to be shared
                      and not the raw data.We used T1w MRI scans of healthy
                      subjects from 4 open datasets (IXI, eNKI, CamCAN and
                      1000Gehirne)with n>500 each (total N=3103, 18-90 age range).
                      We performed leave-one-site-out analysis and testedthe
                      impact of using one or more datasets for training.The
                      highest test performance was observed for the set-ups with
                      L0-level predictions coming from the testdata, with the best
                      set up using pooled predictions of L0 from three sites to
                      train the L1 model (MAE=4.7)followed by the L1 models
                      trained on 3 sites separately (MAE=4.8). This set-up
                      provides improved dataprivacy as L0 analysis can be
                      performed at the application site and only predictions need
                      to be shared.Set-ups based on mean GMV performed the worst
                      (MAE=6.5-7.3). We also found that L0 models providerobust
                      interpretation of regional aging effects, i.e. the Pearson
                      correlation of real age with predicted-agewas higher than
                      with GMV.},
      month         = {Jun},
      date          = {2023-06-12},
      organization  = {Helmholtz AI, Hamburg (Germany), 12
                       Jun 2023 - 14 Jun 2023},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525) / 5251 - Multilevel Brain
                      Organization and Variability (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253 / G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-01166},
      url          = {https://juser.fz-juelich.de/record/1022029},
}