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@MISC{Knab:279507,
      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
                      and Boehm-Sturm, Philipp and Harms, Christoph},
      title        = {{D}ataset: {O}pen data repository, {K}nab et al.,
                      {P}rediction of stroke outcome in mice based on non-invasive
                      {MRI} and behavioral testing, v3.2},
      publisher    = {Zenodo},
      reportid     = {DZNE-2025-00834},
      year         = {2025},
      abstract     = {Open data repository, Knab et al., Prediction of stroke
                      outcome in mice based on non-invasive MRI and behavioral
                      testing Latest version of files: $repository_v2.0.zip,$
                      Behavior $Data_v2.0.xlsx$ and MRI IDs $Testing\&Replication$
                      Cohort.xlsx (please ignore repository.zip) Open data
                      repository Knab et al. Prediction of stroke outcome in mice
                      based on non-invasvive MRI and behavioral testing Open code
                      and documentation of prediction models available via
                      https://github.com/major-s/mouse-mcao-outcome-predictor
                      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
                      strokemasklesion.nii: manually delineated
                      $lesionx_masklesion.nii:$ lesion in atlas $spaceix_ANO.nii:$
                      Allen brain atlas in native space (i.e. matching
                      t2.nii)Lesion volume was calculated by volume of voxels
                      unequal 0 in $x_masklesion.niiOverlap$ 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 IDColumns 2-537: MRI regions (column title
                      corresponds to the region number as used in the Allen common
                      coordinate framework)Column 538: lesion volumeColumn 539:
                      initial performance (subacute deficit) = mean
                      performance/deficit on days 2-6Column 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 deficitColumn 541: residual
                      performance/deficitColumn 542: test or training
                      groupConsecutive 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 paradigmFolder
                      '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 mriFolder '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 deficitFolder $'mri_inc_oob_imp'$ 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
                      templatesAllen atlas, template, brain mask, hemisphere
                      masks, tissue probability masks in NIFTI format including
                      annotations of region IDs and parameter.m file for use in
                      MATLAB toolbox ANTx2},
      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.14709930},
      url          = {https://pub.dzne.de/record/279507},
}