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@ARTICLE{Paquola:905776,
      author       = {Paquola, Casey and Royer, Jessica and Lewis, Lindsay B and
                      Lepage, Claude and Glatard, Tristan and Wagstyl, Konrad and
                      DeKraker, Jordan and Toussaint, Paule-J and Valk, Sofie
                      Louise and Collins, D Louis and Khan, Ali and Amunts, Katrin
                      and Evans, Alan C and Dickscheid, Timo and Bernhardt, Boris},
      title        = {{B}ig{B}rain{W}arp: {T}oolbox for integration of
                      {B}ig{B}rain 3{D} histology with mutlimodal neuroimaging},
      reportid     = {FZJ-2022-00999},
      year         = {2021},
      abstract     = {Neuroimaging stands to benefit from emerging
                      ultrahigh-resolution histological atlases of the human
                      brain; the first of which is "BigBrain". Ongoing research
                      aims to characterise regional differentiation of
                      cytoarchitecture with BigBrain and to optimise registration
                      of BigBrain with standard neuroimaging templates. Together,
                      this work paves the way for multi-scale investigations of
                      brain organisation. However, working with BigBrain can
                      present new challenges for neuroimagers, including dealing
                      with cellular resolution neuroanatomy and complex
                      transformation procedures. To simplify workflows and support
                      adoption of best practices, we developed BigBrainWarp, a
                      toolbox for integration of BigBrain with multimodal
                      neuroimaging. The primary BigBrainWarp function wraps
                      multiple state-of-the-art deformation matrices into one line
                      of code, allowing users to easily map data between BigBrain
                      and standard MRI spaces. Additionally, the toolbox contains
                      ready-to-use cytoarchitectural features to improve
                      accessibility of histological information. The present
                      article discusses recent contributions to BigBrain-MRI
                      integration and demonstrates the utility of BigBrainWarp for
                      further investigations.},
      cin          = {INM-7 / INM-1},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)25},
      url          = {https://juser.fz-juelich.de/record/905776},
}