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@INPROCEEDINGS{Schiffer:1043530,
      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 {M}icroscopic
                      {A}nalysis of {C}ytoarchitecture in the {H}uman {B}rain},
      reportid     = {FZJ-2025-02906},
      year         = {2025},
      abstract     = {Studying the structure of biological networks in the human
                      brain is key to decoding the mechanisms underlying brain
                      function, dysfunction, and behavior. Imaging and mapping
                      distributions of cells and nerve fibers at the micrometer
                      scale across entire human brains can bridge the gap between
                      nanoscale imaging of small fields of view (e.g., by EM) and
                      in vivo imaging of the whole brain (e.g., MRI, fMRI, DWI),
                      the latter capturing structure and function at the
                      millimeter scale across large numbers of subjects. A
                      fundamental organizational principle in the cerebral cortex
                      is cytoarchitecture; defined by the columnar and laminar
                      arrangement of cells as well as their shape, density, size,
                      and type. High-throughput microscopic imaging of whole human
                      brain sections allows to map cytoarchitecture at whole-brain
                      level, but implies to process petabyte-scale image datasets
                      to capture inter-individual microstructures variability of
                      different brains. To leverage the rich information of such
                      large datasets for brain research, we propose CytoNet, a
                      foundation model for cytoarchitecture in the human cerebral
                      cortex. CytoNet is trained with a specifically designed
                      selfsupervised learning task that exploits the relationship
                      between spatial proximity and architectural similarity in
                      the brain to promote the extraction of cytoarchitectonic
                      features from microscopic image patches extracted along the
                      cortical ribbon. The model learns to extract anatomically
                      plausible latent features in a fully data-driven fashion,
                      capturing fundamental properties of cytoarchitecture with
                      their regional variance and inter-subject variability. The
                      features are comparable across brain regions and subjects,
                      can be computed at arbitrarily dense sampling locations in
                      the cerebral cortex of different brains, and facilitate a
                      broad range of neuroscientific analysis tasks. In
                      particular, we demonstrate state-of-the-art performance on
                      brain area classification, cortical layer segmentation,
                      estimation of morphological parameters, and unsupervised
                      parcellation. As a foundation model, CytoNet offers new
                      perspectives for characterizing microscopic architecture
                      across subjects, establishing the foundation for holistic
                      analyses of cytoarchitecture and its relationship to other
                      organizational and functional principles at the whole-brain
                      level.},
      month         = {Jun},
      date          = {2025-06-25},
      organization  = {Helmholtz Imaging Conference 2025,
                       Potsdam (Germany), 25 Jun 2025 - 27 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/1043530},
}