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@INPROCEEDINGS{Bludau:1048761,
      author       = {Bludau, Sebastian and Dickscheid, Timo and Schiffer,
                      Christian and Steffens, Anna and Upschulte, Eric and Amunts,
                      Katrin},
      title        = {{L}ayer-specific cell counts in {B}ig{B}rain –
                      decomposing cortex-wide numbers based on cytoarchitectonics},
      reportid     = {FZJ-2025-04876},
      year         = {2025},
      abstract     = {<b>Introduction:</b> Cell counts of the cerebral cortex
                      represent one of the most fundamental characteristics of
                      brain organization, and serve as the basis for studying
                      evolution, development and disease (e.g., [1, 2]). However,
                      the total number of cells in the brain or cerebral cortex
                      does not reflect the variation between layers and cortical
                      areas, which is related to the functional heterogeneity of
                      the human brain. Therefore, we investigated layer- specific
                      cell counts in 94 cytoarchitectonic cortical areas in the
                      anatomical BigBrain model [3].<br><br><b>Methods:</b> The
                      study is based on cell counts and cortical thickness
                      measurements in high- resolution 2D scans (1 μm in-plane
                      resolution), most of which are already publicly available
                      [4]. In total, 940 patches were analyzed across 94 areas of
                      the Julich Brain Atlas [5] (Fig. 1), with 10 patches sampled
                      per area, ensuring a $>65\%$ probability of region being
                      present at each location. Using siibra-python [6], patches
                      were assigned to Julich Brain cytoarchitectonic
                      probabilistic maps by transforming their locations to MNI
                      space [7]. These were chosen to be perpendicular to the
                      cortical surface. Manual layer annotations were performed by
                      anatomical experts and independently verified. Cells were
                      segmented using a novel deep learning approach [8, 9]
                      including correction for truncated cells (Fig1). Cell
                      numbers were corrected for histological shrinkage [10]. To
                      cross-validate data and capture intersubject variability
                      among brains, patches from frontal pole area Fp1, motor area
                      4a, and visual area hOc1of ten Julich- atlas brains were
                      analyzed using the same counting approach and compared to
                      the BigBrain data. <br><br><b>Results:</b> Comprehensive
                      data sets of 940 image patches were obtained including the 1
                      μm images with manual annotation of cortical layers, the
                      cell counts, cell sizes, and cortical as well as layer
                      thicknesses (Fig1, Fig 2). The analysis revealed a
                      considerable regional variations in cell counts across
                      layers and areas as illustrated in Fig. 2. E.g., areas of
                      the insula showed up to $15\%$ variance. After shrinkage
                      correction and adjusting for truncated cells, the corrected
                      total cell count of the human cortex has been estimated to
                      be approximately 33.9 billion. Based on a neuron-to-glia
                      ratio of 1:1.5 [1], this corresponds to 13.6 billion neurons
                      and 20.3 billion glial cells. The average cortical thickness
                      across all regions was 2667.15 μm. Quantitative measures of
                      areas Fp1 of the BigBrain (Fig. 2) and of the other two
                      areas were in the range of variation of the ten brains from
                      Julich Brain Atlas. The data will be shared as part of a
                      growing dataset collection [11] in accordance with the FAIR
                      principles via the EBRAINS
                      infrastructure.<br><br><b>Discussion:</b> This study has
                      introduced a new, comprehensive dataset with detailed area-
                      and layer specific cell counts of the human cerebral cortex,
                      supplementing previous data at whole- cortex level [1]. It
                      extended our knowledge on cytoarchitectonic differences,
                      e.g., etween granular, dysgranular and granular areas and
                      further quantified regional differences at laminar level.
                      The considerable differences between areas within the
                      insular cortex, as one example, confirms the hypothesis that
                      macroanatomically defined regions do not adequately reflect
                      the microstructural organization of the brain; they may lump
                      together structurally and functionally different areas. Data
                      on cortical thickness correspond to earlier histological and
                      MR-based findings [2, 12, 13]. We will continuously
                      supplement the data together with new releases of Julich
                      Brain Atlas areas. Cell counts based on cytoarchitectonics
                      may serve as reference for comparative and disease studies,
                      and inform modeling and simulation, and AI, highlighting the
                      value of high-resolution atlases for capturing details of
                      microscopical brain
                      organization.<br><br><b>References:</b><ol><li>von Bartheld,
                      C.S., et al., The search for true numbers of neurons and
                      glial cells in the human brain: A review of 150 years of
                      cell counting. J Comp Neurol, 2016. 524(18): p.
                      3865-3895.<li>von Economo, C.F., et al., Die
                      Cytoarchitektonik der Hirnrinde des erwachsenen Menschen.
                      1925: J. Springer.<li>Amunts, K., et al., BigBrain: an
                      ultrahigh-resolution 3D human brain model. Science,
                      340(6139): p. 1472-5.<li>Schiffer, C., Lepage, C.,
                      Omidyeganeh, M., Mohlberg, H., Brandstetter, A., Bludau, S.,
                      Heuer, K., Toussaint, P.-J., Wenzel, S., Dickscheid, T.,
                      Evans, A. C., $\&$ Amunts, K. , Selected 1 micron scans of
                      BigBrain histological sections (v1.0) 2022. </li><li>Amunts,
                      K., et al., Julich-Brain: A 3D probabilistic atlas of the
                      human brain's cytoarchitecture. Science, 2020. 369(6506): p.
                      988-992. </li><li>Dickscheid, T., Gui, X., Simsek, A. N.,
                      Koehnen, L., Marcenko, V., Schiffer, C., Bludau, S., $\&$
                      Amunts, K., siibra-python (Zenodo).
                      https://zenodo.org/records/14184565. </li><li>Lebenberg, J.,
                      et al., A framework based on sulcal constraints to align
                      preterm, infant and adult human brain images acquired in
                      vivo and post mortem. Brain Struct Funct, 223(9): p.
                      4153-4168. </li><li>Ma, J., et al., The multimodality cell
                      segmentation challenge: toward universal solutions. Nat
                      Methods, 2024. 21(6): p. 1103-1113. </li><li>Upschulte, E.,
                      et al., Contour proposal networks for biomedical instance
                      segmentation. Med Image Anal, 2022. 77: p. 102371.
                      </li><li>Amunts, K., et al., Gender-specific left-right
                      asymmetries in human visual cortex. J Neurosci, 2007. 27(6):
                      p. 1356-64. </li><li>Dickscheid, T., Bludau, S., Paquola,
                      C., Schiffer, C., Upschulte, E., $\&$ Amunts, K.,
                      Layerspecific distributions of segmented cells in different
                      cytoarchitectonic regions of BigBrain iso cortex. EBRAINS
                      project:
                      https://search.kg.ebrains.eu/instances/f06a2fd1-a9ca-42a3-b754-adaa025adb10.
                      </li><li>Frangou, S., et al., Cortical thickness across the
                      lifespan: Data from 17,075 healthy individuals aged 3-90
                      years. Hum Brain Mapp, 2022. 43(1): p. 431-451.
                      </li><li>Wagstyl, K., et al., BigBrain 3D atlas of cortical
                      layers: Cortical and laminar thickness gradients diverge in
                      sensory and motor cortices. PLoS Biol, 2020. 18(4): p.
                      e3000678. </li></ol>},
      month         = {Dec},
      date          = {2025-12-08},
      organization  = {EBRAINS summit 2025, Brüssel
                       (Belgium), 8 Dec 2025 - 11 Dec 2025},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5241 - Molecular Information Processing in Cellular Systems
                      (POF4-524) / 5254 - Neuroscientific Data Analytics and AI
                      (POF4-525) / 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)POF4-5241 / G:(DE-HGF)POF4-5254 /
                      G:(EU-Grant)101147319 / G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1048761},
}