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@INPROCEEDINGS{Schiffer:1043518,
      author       = {Schiffer, Christian and Boztoprak, Zeynep and Kropp,
                      Jan-Oliver and Thönnißen, Julia and Behr, Katja and
                      Spitzer, Hannah and Amunts, Katrin and Dickscheid, Timo},
      title        = {{C}yto{N}et: {A} {F}oundation {M}odel for
                      {C}ytoarchitecture in the {H}uman {C}erebral {C}ortex},
      reportid     = {FZJ-2025-02894},
      year         = {2025},
      abstract     = {Microscopic analysis of cytoarchitecture in the human
                      cerebral cortex plays a central role for developing
                      high-resolution microstructural human brain atlases.
                      Cytoarchitecture is defined by the spatial organization of
                      cells, including their shape, density, size, type, and their
                      columnar and laminar arrangement, which varies between brain
                      regions. Cytoarchitecture provides an important
                      microstructural reference for brain connectivity and
                      function and is therefore of great interest in brain
                      research. Microscopic scans of histological human brain
                      sections allow detailed analysis of cytoarchitecture.
                      High-throughput microscopy scanners can digitize sections of
                      an entire brain at 1 micrometer isotropic resolution in
                      about a year, resulting in petabyte-scale image datasets
                      that capture the brain’s complexity and variability across
                      multiple subjects. These large imaging datasets offer great
                      opportunities for brain research, but also pose novel
                      methodological and technical challenges that require
                      developing new analytical methods.Addressing these needs, we
                      present CytoNet, a foundation model for cytoarchitecture in
                      the human cerebral cortex. CytoNet is trained with a novel
                      self-supervised learning task that promotes the extraction
                      of cytoarchitectonic features from microscopic image patches
                      extracted along the cortical ribbon. We demonstrate that
                      CytoNet learns to extract powerful and anatomically
                      plausible representations of cytoarchitecture that capture
                      intra-subject variance and inter-subject variability.
                      CytoNet is able to compute features that are 1) completely
                      data-driven, 2) globally comparable across brain regions and
                      subjects, 3) encode a wide range of cytoarchitectonic
                      properties that facilitate relevant downstream analysis and
                      whole-brain correlative analysis, and 4) can be computed at
                      arbitrarily dense sampling intervals at any location within
                      the cerebral cortex of any subject. CytoNet achieves
                      state-of-the-art performance for brain area classification,
                      cortical layer segmentation, morphology estimation, and
                      data-driven discovery of cytoarchitectonic structures. Using
                      embedding analysis, we show that CytoNet learns to map
                      microscopic images into a semantically highly structured and
                      anatomically plausible latent space that facilitates the
                      aforementioned downstream tasks. Our work has broad
                      implications for changing the way we analyze microscopic
                      brain organization and will improve our ability to make
                      discoveries from large datasets.},
      month         = {Jun},
      date          = {2025-06-03},
      organization  = {Helmholtz AI Conference 2025,
                       Karlsruhe (Germany), 3 Jun 2025 - 5 Jun
                       2025},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      X-BRAIN (ZT-I-PF-4-061) / Helmholtz AI - Helmholtz
                      Artificial Intelligence Coordination Unit – Local Unit FZJ
                      (E.40401.62) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027) / EBRAINS
                      2.0 - EBRAINS 2.0: A Research Infrastructure to Advance
                      Neuroscience and Brain Health (101147319) / HIBALL -
                      Helmholtz International BigBrain Analytics and Learning
                      Laboratory (HIBALL) (InterLabs-0015)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)ZT-I-PF-4-061 /
                      G:(DE-Juel-1)E.40401.62 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(EU-Grant)101147319 / G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1043518},
}