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@MASTERSTHESIS{Krll:865945,
      author       = {Kröll, Jean-Philippe},
      title        = {{D}ata {R}epresentation and {C}lassification of
                      {A}lzheimer’s {D}isease},
      school       = {Heinrich-Heine Universität Düsseldorf},
      type         = {Masterarbeit},
      reportid     = {FZJ-2019-05211},
      pages        = {53},
      year         = {2019},
      note         = {Masterarbeit, Heinrich-Heine Universität Düsseldorf,
                      2019},
      abstract     = {Application of machine learning algorithms to information
                      of magnetic resonance imaging (MRI) is a widespread approach
                      to differentiate Alzheimer’s disease (AD) patients and
                      healthy controls (HC). Since a variety of brain
                      representations are used by different studies, it is
                      necessary that the influence of the chosen brain atlas on
                      the model performance is investigated. Therefore, the goal
                      is to analyse the effect which is caused by varying
                      granularity of an atlas. In addition, to find acceptance in
                      the medical community, the model must be able to identify
                      biologically relevant regions. Thereby, it can be ensured
                      that the model will reliably identify patients in future
                      applications and is not based on sample-specific
                      characteristics. For this reason, the regions selected for
                      classification by support vector machine (SVM) to
                      differentiate AD vs HC are analysed. Lastly, features that
                      are not selected by a given model are generally disregarded.
                      Since those features could potentially still contain
                      relevant information, they are examined in this study.
                      Different granularities of the Schaefer atlas, with
                      parcellations ranging from 173 to 1273 parcels, were used to
                      extract features from structural images of AD patients and
                      healthy controls. Subsequently, SVM classifiers were trained
                      on the features derived from the different parcellations and
                      their influence was evaluated based on the performance of
                      the resulting model. Biological relevance of the selected
                      features was verified by confirming their role in AD with
                      current literature. Non-selected features were singled out
                      and used to train a non-selected feature model (NFM).
                      Relevance of the non-selected features was evaluated based
                      on performance of the NFM. Evaluation of the obtained
                      accuracies showed that the granularity of the atlas affects
                      the model performance on 1.5 Tesla images of AD patients and
                      HC. Accuracies ranged from $87\%$ for the 173 parcel
                      parcellation, to $83\%$ for the 1273 parcel parcellation.
                      Classification of 3 Tesla images was not significantly
                      affected, with all models achieving accuracies around
                      $91\%.$ Biological relevance of the selected features could
                      be confirmed by literature, although it was evident that not
                      all relevant regions were included in the model. Examination
                      of the NFM revealed that a model based on non-selected
                      features could still classify AD vs HC with an accuracy of
                      $76\%.$ The findings suggest that future atlas-based
                      approaches should pay more attention to the effect of the
                      selected atlas. In addition, the ability of SVM to select
                      biologically relevant regions supports its implementation
                      for diagnosis of AD in the clinic. Lastly, the results
                      indicate that investigation of non-selected features could
                      provide additional insight into the relevance of certain
                      regions for the studied disease.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)19},
      url          = {https://juser.fz-juelich.de/record/865945},
}