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@ARTICLE{Wachinger:280031,
      author       = {Wachinger, Christian and Golland, Polina and Kremen,
                      William and Fischl, Bruce and Reuter, Martin},
      title        = {{B}rain{P}rint: {A} discriminative characterization of
                      brain morphology},
      journal      = {NeuroImage},
      volume       = {109},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {DZNE-2025-00875},
      pages        = {232 - 248},
      year         = {2015},
      abstract     = {We introduce BrainPrint, a compact and discriminative
                      representation of brain morphology. BrainPrint captures
                      shape information of an ensemble of cortical and subcortical
                      structures by solving the eigenvalue problem of the 2D and
                      3D Laplace-Beltrami operator on triangular (boundary) and
                      tetrahedral (volumetric) meshes. This discriminative
                      characterization enables new ways to study the similarity
                      between brains; the focus can either be on a specific brain
                      structure of interest or on the overall brain similarity. We
                      highlight four applications for BrainPrint in this article:
                      (i) subject identification, (ii) age and sex prediction,
                      (iii) brain asymmetry analysis, and (iv) potential genetic
                      influences on brain morphology. The properties of BrainPrint
                      require the derivation of new algorithms to account for the
                      heterogeneous mix of brain structures with varying
                      discriminative power. We conduct experiments on three
                      datasets, including over 3000 MRI scans from the ADNI
                      database, 436 MRI scans from the OASIS dataset, and 236 MRI
                      scans from the VETSA twin study. All processing steps for
                      obtaining the compact representation are fully automated,
                      making this processing framework particularly attractive for
                      handling large datasets.},
      keywords     = {Age Factors / Aged / Brain: anatomy $\&$ histology / Brain
                      Mapping: methods / Female / Humans / Imaging,
                      Three-Dimensional: methods / Magnetic Resonance Imaging:
                      methods / Male / Sex Factors / Signal Processing,
                      Computer-Assisted / Twins: genetics / Brain asymmetry
                      (Other) / Brain shape (Other) / Brain similarity (Other) /
                      Large brain datasets (Other) / Morphological heritability
                      (Other) / Subject identification (Other)},
      ddc          = {610},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1016/j.neuroimage.2015.01.032},
      url          = {https://pub.dzne.de/record/280031},
}