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@MISC{Knab:280741,
      author       = {Knab, Felix and Koch, Stefan Paul and Major, Sebastian and
                      Farr, Tracy D. and Mueller, Susanne and Euskirchen, Philipp
                      and Eggers, Moritz and Kuffner, Melanie T. C. and Walter,
                      Josefine and Dreier, Jens P. and Endres, Matthias and
                      Dirnagl, Ulrich and Wenger, Nikolaus and Hoffmann, Christian
                      J. and Boehm-Sturm, Philipp and Harms, Christoph},
      title        = {{O}pen data repository, {K}nab et al., {P}rediction of
                      stroke outcome in mice based on non-invasive {MRI} and
                      behavioral testing},
      publisher    = {Zenodo},
      reportid     = {DZNE-2025-00962},
      year         = {2022},
      abstract     = {Open data repository, Knab et al., Prediction of stroke
                      outcome in mice based on non-invasive MRI and behavioral
                      testing Open data repository Knab et al. Prediction of
                      stroke outcome in mice based on non-invasvive MRI and
                      behavioral testing Content: README.txt This information dat
                      Contains MRI data in NIFTI format and secondary data from
                      atlas registration. For documentation of atlas registration
                      files see https://pubmed.ncbi.nlm.nih.gov/28829217/ Files
                      used for the manuscript: t2.nii: t2 weighted image acquired
                      24 h post stroke masklesion.nii: manually delineated lesion
                      $ix_ANO.nii:$ Allen brain atlas in native space (i.e.
                      matching t2.nii) Overlap of regions defined by $ix_ANO.nii$
                      with masklesion.nii were used for calculating percent damage
                      in each atlas region $prediction_models$ Contains separated
                      training and test data as xlsx and csv files with lesion
                      volumes in cubic mm of the Allen brain atlas space, percent
                      damage per atlas region and behavioral data. The training
                      data was used as input for training prediction models in
                      MATLAB, the results were created using the test data. The
                      files have following sturcture: Column 1: animal ID Columns
                      2-537: MRI regions (column title corresponds to the region
                      number as used in the Allen common coordinate framework)
                      Column 538: lesion volume Column 539: initial performance
                      (subacute deficit) = mean performance/deficit on days 2-6
                      Column 540: mean performance/deficit on days 2-6 = initial
                      performance (subacute deficit) - this column equals column
                      539 but has different header which was used to train the
                      residual from initial deficit Column 541: residual
                      performance/deficit Column 542: test or training group
                      Consecutive rows contain data for each animal specified by
                      the animal id The repository also contains all trained
                      models, prediction results for the test data and tables with
                      resulting median absolute error (MedAE) and 5th, 25th, 75th
                      and 95 absolute error quantiles for each model. The model
                      files end with $'_models.mat'$ and contain 50 independently
                      trained models each. Each model version is specified by
                      number 1-50. The result files end with $'_test_results.mat'$
                      or $'_test_results.xlsx',$ files with MedAE and quantiles
                      end with $'_test_errors.xlsx'$ or $'_test_errors.csv.$ The
                      common part of filenames specifies the used paradigm Folder
                      'subacute deficit prediction' contains: -
                      $initial_performance_from_lesion_volume:$ prediction of
                      subacute deficit using lesion volume -
                      $initial_performance_from_segmented_mri:$ prediction of
                      subacute deficit using segmented mri Folder 'long-term
                      outcome prediction' contains: - $lesion_volume:$ prediction
                      of residual deficit using lesion volume - $segmented_mri:$
                      prediction of residual deficit using $segmented_mri$ -
                      $initial_performance:$ prediction of residual deficit using
                      subacute deficit Folder $'mri_inc_oob_imp'$ contains models
                      trained using increasing number of mri segments sorted
                      according to the out-of-bag importance. The number of used
                      segments is given in the file name. The models, results and
                      errors are separated in subfolders. Files with equal file
                      name and different extension always contain the same data},
      keywords     = {stroke (Other) / mice (Other) / outcome prediction (Other)
                      / motor-function (Other) / staircase (Other)},
      cin          = {AG Endres / AG Dirnagl},
      cid          = {I:(DE-2719)1811005 / I:(DE-2719)1810002},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)32},
      doi          = {10.5281/zenodo.6534691},
      url          = {https://pub.dzne.de/record/280741},
}