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@INPROCEEDINGS{Antonopoulos:1034876,
      author       = {Antonopoulos, Georgios and More, Shammi and Raimondo,
                      Federico and Eickhoff, Simon and Patil, Kaustubh},
      title        = {{P}arcel-wise stacking ensemble provides improved age
                      prediction and brain-aging insights},
      reportid     = {FZJ-2024-07622},
      year         = {2024},
      abstract     = {Introduction:Predicting chronological age using structural
                      magnetic resonance imaging (MRI) has shown great potential
                      for studying aging in health and disease. A model trained on
                      MRI scans from healthy individuals can provide biological
                      insights into healthy brain aging and it can also
                      potentially assist in detection of abnormal aging in
                      psychiatric, and neurodegenerative disorders [1, 2]. Such a
                      model may lead to novel monitoring and treatment options.
                      However, both accuracy and explainability of age prediction
                      models need to be improved before they can be applied in the
                      real-world. To this end, we propose parcel-wise stacking
                      ensemble models (SEM) [3]. In SEM the voxels in each region
                      are not weighted equally, as in the standard approaches.
                      Regional models evaluate the contribution of each voxel in
                      the process making this way, better use of voxels'
                      information. Moreover, combining predictions in sequential
                      models leads to reduced overall bias and variance, of the
                      final prediction.Methods:We used T1w MRI scans of healthy
                      subjects from 4 open datasets (IXI, eNKI, CamCAN and
                      1000Gehirne. N>500 each, total N=3103, age range 18-90
                      years). Voxel based morphometry using CAT12.8 [4] was
                      employed to estimate gray matter volume (GMV) for each
                      subject. Performance was estimated in terms of mean absolute
                      error (MAE) assessed in leave-one-dataset-out (LODO) set
                      up.Proposed SEM consists of two levels, denoted as L0 and L1
                      (Figure 1). GLMnet [5] was used for both L0 and L1 models.
                      The L0 uses an 873-parcel atlas and trains a model for each
                      parcel using corresponding voxels-wise GMV. The features for
                      the L1 model are obtained as out-of-sample (OOS) predictions
                      from a 3-fold cross-validation scheme on L0 models. Two
                      types of L0 models were obtained; either by pooling data
                      from different sites (L0p) or by making predictions for each
                      training site separately (L0s). Similarly, in L1 models were
                      obtained by either pooling L0 predictions (L1p) or by
                      training the L1 model for each site separately and averaging
                      the per-site predictions (L1s).Additionally, to examine the
                      case where enough data is available at an application site,
                      we estimate L0 OOS predictions from that site (L0oos). These
                      are then used to obtain predictions using the L1 models. As
                      a baseline we also trained models using the average GMV in
                      each parcel -by averaging predictions of the site-specific
                      models or training models on pooled data across sites-,
                      while ensuring use of consistent training samples across the
                      set ups.Results:The highest test performances were observed
                      for the L0oos setups where L0 predictions came from the test
                      site. The best predictions were for the L0oos-L1p (average
                      MAE=4.8), closely followed by the L0oos-L1s (MAE=4.9).
                      Setups using pooled L0 predictions to train L1,
                      independently of how L0 was trained, L0p-L1p and L0s-L1p,
                      had both MAE=5.1. Models using mean parcel-wise GMV had
                      MAE=5.7 when the model was trained using the train sets
                      together with the 2 folds of the test set. Performance was
                      worse for the other two mean parcel-wise GMV models trained
                      in three datasets, with the L1p setup up being slightly
                      better compared to L1s (MAE=6.2 and MAE=6.7 respectively).L0
                      models provided robust interpretation of regional aging
                      effects, i.e. the Pearson correlation of real age with OOS
                      predicted-age was higher than with GMV (averaged across all
                      datasets used for training, Figure 2). While there is a
                      considerable overlap in the identified regions between the
                      two methodologies, SEM distinctly emphasizes certain areas,
                      notably the subcortex and cerebellum.Conclusions:SEM
                      provides improved age prediction performance compared to
                      using parcel-wise average of GMV as well as novel biological
                      insights regarding healthy aging. Further improvements in
                      the SEM design could be achieved by selecting suitable
                      learning algorithms with appropriate hyperparameter tuning
                      for L0 and L1 models.},
      month         = {Jun},
      date          = {2024-06-23},
      organization  = {OHBM 2024, Seoul (South Korea), 23 Jun
                       2024 - 27 Jun 2024},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2024-07622},
      url          = {https://juser.fz-juelich.de/record/1034876},
}