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@INPROCEEDINGS{Lothmann:1048784,
      author       = {Lothmann, Kimberley and Schiffer, Christian and Amunts,
                      Katrin and Dickscheid, Timo},
      title        = {{G}oing 3{D} with {AI}: {F}ull 3{D} {R}econstructions of
                      {C}ytoarchitectonic {M}aps in {B}ig{B}rain},
      reportid     = {FZJ-2025-04899},
      year         = {2025},
      abstract     = {The BigBrain dataset represents the first
                      ultrahigh-resolution 3D model of the human brain at 20 µm
                      isotropic resolution, reconstructed from 7,404 histological
                      sections of a human post-mortem brain. This unique dataset
                      provides the basis for cytoarchitectonic mapping at a level
                      of anatomical detail that bridges microscopic cellular
                      organization with macroscale brain imaging. Traditionally,
                      cytoarchitectonic areas have been delineated on individual
                      histological sections, resulting in 2D maps that are
                      difficult to integrate into 3D brain reference spaces.To
                      address this, we applied the AtLaSUi tool to reconstruct
                      delineated BigBrain areas in full 3D. This workflow
                      transforms manual 2D annotations into volumetric,
                      topologically consistent maps that preserve the fine-grained
                      borders of cortical regions. The resulting 3D maps enable
                      spatially continuous visualization of cortical areas and
                      facilitate direct comparison with structural and functional
                      neuroimaging data.The reconstructed areas are part of the
                      Julich-Brain Atlas, a continuously expanding
                      cytoarchitectonic atlas of the human brain. All maps are
                      openly available through the EBRAINS research infrastructure
                      and can be explored, accessed, and programmatically queried
                      via the siibra tool suite. By making these maps accessible
                      in a standardized 3D reference space, we contribute to the
                      integration of microstructural data with multimodal
                      neuroimaging and to the advancement of open, reproducible
                      neuroscience.},
      month         = {Oct},
      date          = {2025-10-27},
      organization  = {9th BigBrain Workshop - HIBALL Closing
                       Symposium, Berlin (Germany), 27 Oct
                       2025 - 29 Oct 2025},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to
                      Advance Neuroscience and Brain Health (101147319) /
                      Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5251 /
                      G:(DE-HGF)InterLabs-0015 / G:(EU-Grant)101147319 /
                      G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1048784},
}