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@ARTICLE{Kiwitz:888898,
      author       = {Kiwitz, Kai and Schiffer, Christian and Spitzer, Hannah and
                      Dickscheid, Timo and Amunts, Katrin},
      title        = {{D}eep learning networks reflect cytoarchitectonic features
                      used in brain mapping},
      journal      = {Scientific Report},
      volume       = {10},
      issn         = {0174-0814},
      address      = {Darmstadt},
      publisher    = {GSI},
      reportid     = {FZJ-2020-05303},
      pages        = {22039},
      year         = {2020},
      abstract     = {The distribution of neurons in the cortex
                      (cytoarchitecture) differs between cortical areas and
                      constitutes the basis for structural maps of the human
                      brain. Deep learning approaches provide a promising
                      alternative to overcome throughput limitations of currently
                      used cytoarchitectonic mapping methods, but typically lack
                      insight as to what extent they follow cytoarchitectonic
                      principles. We therefore investigated in how far the
                      internal structure of deep convolutional neural networks
                      trained for cytoarchitectonic brain mapping reflect
                      traditional cytoarchitectonic features, and compared them to
                      features of the current grey level index (GLI) profile
                      approach. The networks consisted of a 10-block deep
                      convolutional architecture trained to segment the primary
                      and secondary visual cortex. Filter activations of the
                      networks served to analyse resemblances to traditional
                      cytoarchitectonic features and comparisons to the GLI
                      profile approach. Our analysis revealed resemblances to
                      cellular, laminar- as well as cortical area related
                      cytoarchitectonic features. The networks learned filter
                      activations that reflect the distinct cytoarchitecture of
                      the segmented cortical areas with special regard to their
                      laminar organization and compared well to statistical
                      criteria of the GLI profile approach. These results confirm
                      an incorporation of relevant cytoarchitectonic features in
                      the deep convolutional neural networks and mark them as a
                      valid support for high-throughput cytoarchitectonic mapping
                      workflows.},
      cin          = {INM-1 / JARA-HPC},
      ddc          = {530},
      cid          = {I:(DE-Juel1)INM-1-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {571 - Connectivity and Activity (POF3-571) / HBP SGA1 -
                      Human Brain Project Specific Grant Agreement 1 (720270) /
                      HBP SGA2 - Human Brain Project Specific Grant Agreement 2
                      (785907) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539) / Automatic classification of
                      cytoarchitectonic human cortical brain regions using Deep
                      Learning $(jinm16_20200501)$ / SMHB - Supercomputing and
                      Modelling for the Human Brain (HGF-SMHB-2013-2017) /
                      Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62)},
      pid          = {G:(DE-HGF)POF3-571 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539 /
                      $G:(DE-Juel1)jinm16_20200501$ /
                      G:(DE-Juel1)HGF-SMHB-2013-2017 / G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33328511},
      UT           = {WOS:000603657800005},
      doi          = {10.1038/s41598-020-78638-y},
      url          = {https://juser.fz-juelich.de/record/888898},
}