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@INPROCEEDINGS{Dyrba:145601,
      author       = {Dyrba, Martin and Wegrzyn, Martin and Kilimann, Ingo and
                      Teipel, Stefan},
      title        = {{C}ombining {DTI} and {MRI} for the automated detection of
                      {A}lzheimer's disease using a large {E}uropean multicenter
                      dataset},
      reportid     = {DZNE-2020-00931},
      year         = {2012},
      abstract     = {Diffusion tensor imaging (DTI) allows assessing neuronal
                      fiber tract integrity in vivo to support the diagnosis of
                      Alzheimer’s disease (AD). It is an open research question
                      to which extent combinations of different neuroimaging
                      techniques increase the detection of AD. In this study we
                      examined different methods to combine DTI data and
                      structural T 1-weighted magnetic resonance imaging (MRI)
                      data. Further, we applied machine learning techniques for
                      automated detection of AD. We used a sample of 137 patients
                      with clinically probable AD (MMSE 20.6 ±5.3) and 143
                      healthy elderly controls, scanned in nine different
                      scanners, obtained from the recently created framework of
                      the European DTI study on Dementia (EDSD). For diagnostic
                      classification we used the DTI derived indices fractional
                      anisotropy (FA) and mean diffusivity (MD) as well as grey
                      matter density (GMD) and white matter density (WMD) maps
                      from anatomical MRI. We performed voxel-based classification
                      using a Support Vector Machine (SVM) classifier with tenfold
                      cross validation. We compared the results from each single
                      modality with those from different approaches to combine the
                      modalities. For our sample, combining modalities did not
                      increase the detection rates of AD. An accuracy of
                      approximately $89\%$ was reached for GMD data alone and for
                      multimodal classification when GMD was included. This high
                      accuracy remained stable across each of the approaches. As
                      our sample consisted of mildly to moderately affected
                      patients, cortical atrophy may be far progressed so that the
                      decline in structural network connectivity derived from DTI
                      may not add additional information relevant for the SVM
                      classification. This may be different for predementia stages
                      of AD. Further research will focus on multimodal detection
                      of AD in predementia stages of AD, e.g. in amnestic mild
                      cognitive impairment (aMCI), and on evaluating the
                      classification performance when adding other modalities,
                      e.g. functional MRI or FDG-PET.},
      month         = {Oct},
      date          = {2012-10-01},
      organization  = {MBIA 2012, Nice (France), 1 Oct 2012 -
                       5 Oct 2012},
      subtyp        = {Other},
      cin          = {Clinical Dementia Research Rostock /Greifswald ; AG Teipel},
      cid          = {I:(DE-2719)1510100},
      pnm          = {344 - Clinical and Health Care Research (POF3-344)},
      pid          = {G:(DE-HGF)POF3-344},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://pub.dzne.de/record/145601},
}