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@ARTICLE{Wang:996722,
      author       = {Wang, Di and Honnorat, Nicolas and Fox, Peter T. and
                      Ritter, Kerstin and Eickhoff, Simon B. and Seshadri, Sudha
                      and Habes, Mohamad},
      title        = {{D}eep neural network heatmaps capture {A}lzheimer’s
                      disease patterns reported in a large meta-analysis of
                      neuroimaging studies},
      journal      = {NeuroImage},
      volume       = {269},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2023-01142},
      pages        = {119929 -},
      year         = {2023},
      abstract     = {Deep neural networks currently provide the most advanced
                      and accurate machine learning models to distinguish between
                      structural MRI scans of subjects with Alzheimer's disease
                      and healthy controls. Unfortunately, the subtle brain
                      alterations captured by these models are difficult to
                      interpret because of the complexity of these multi-layer and
                      non-linear models. Several heatmap methods have been
                      proposed to address this issue and analyze the imaging
                      patterns extracted from the deep neural networks, but no
                      quantitative comparison between these methods has been
                      carried out so far. In this work, we explore these questions
                      by deriving heatmaps from Convolutional Neural Networks
                      (CNN) trained using T1 MRI scans of the ADNI data set and by
                      comparing these heatmaps with brain maps corresponding to
                      Support Vector Machine (SVM) activation patterns. Three
                      prominent heatmap methods are studied: Layer-wise Relevance
                      Propagation (LRP), Integrated Gradients (IG), and Guided
                      Grad-CAM (GGC). Contrary to prior studies where the quality
                      of heatmaps was visually or qualitatively assessed, we
                      obtained precise quantitative measures by computing overlap
                      with a ground-truth map from a large meta-analysis that
                      combined 77 voxel-based morphometry (VBM) studies
                      independently from ADNI. Our results indicate that all three
                      heatmap methods were able to capture brain regions covering
                      the meta-analysis map and achieved better results than SVM
                      activation patte},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36740029},
      UT           = {WOS:000943220200001},
      doi          = {10.1016/j.neuroimage.2023.119929},
      url          = {https://juser.fz-juelich.de/record/996722},
}