% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Bludau:1018412,
      author       = {Bludau, Sebastian and Dickscheid, Timo and Schiffer,
                      Christian and Steffens, Anna and Amunts, Katrin},
      title        = {{B}ig{B}rain {A}nalysis: {C}ellular-{L}evel {P}recision at
                      1µm {R}esolution},
      reportid     = {FZJ-2023-04793},
      year         = {2023},
      abstract     = {IntroductionThe BigBrain model [1] is a cornerstone for
                      extracting quantitative measures of brain architecture at
                      20μm isotropic resolution. While this model has proven
                      instrumental in extracting 3D histological features, there's
                      a growing need for even higher spatial resolution to obtain
                      measures at the level of individual cells. Building on
                      previous work from 2022 [2], this project utilizes 2D 1μm
                      sections to provide a more detailed characterization of
                      cellular distributions in the human brain, and to further
                      enhance the BigBrain model with accurate estimates of
                      layer-wise cell densities across the entire
                      cortex.MethodsExpanding on our previous work [2], we
                      investigated 78 additional areas of the Julich-Brain
                      cytoarchitectonic atlas [3]. These patches were sampled by
                      registering each section to BigBrain space, and then
                      sampling cortical locations corresponding to a probability
                      $>60\%$ of being the specific area. In each patch, cortical
                      layer boundaries were annotated by experts and validated
                      using a four-eye procedure. Automatic cell body detection
                      state of the art Deep Learning model [4] was applied to all
                      patches, enabling the extraction of laminar cell numbers and
                      cell body sizes for all areas under investigation
                      (Fig.1).ResultsThe expanded dataset now encompasses 900
                      cortical patches, with a size of about 52GB, selected from
                      well-defined cytoarchitectonic areas of the Julich-Brain
                      Atlas. Each patch in the dataset includes the 1μm raw
                      image, manual annotations of isocortical layers, and
                      contours and spatial properties of the extracted cell body
                      segmentations (Fig.2). This enhancement in resolution from
                      the native 20μm BigBrain resolution to 1μm has unveiled
                      significant differences in cell packing density across
                      various laminae of the brain. A trend of decreasing cell
                      densities from posterior to anteriorly located areas was
                      observed across all lamina of the human cortex. This trend
                      was especially pronounced in granular layers II and IV.
                      Moreover, the new patches can be utilized to refine
                      previously generated cortical laminae [5], which were based
                      on a limited number of areas.ConclusionsThe shift from 20 to
                      a 1µm resolution image data has enabled quantitative
                      analysis of individual cell bodies. This approach gives
                      precise cell counts from specific brain areas and integrates
                      them with overall brain data, revealing both known and new
                      brain architectural insights. The resulting dataset, rooted
                      in the BigBrain framework, provides a well structured and
                      accurate spatial representation. This dataset can
                      potentially replace the century-old cell counts from von
                      Economo and Koskinas [6]. Its strengths lie in its
                      reproducibility, precise 3D anchoring in the BigBrain, and
                      the availability of original images for each patch, allowing
                      detailed verification down to individual cells.[1] Amunts K,
                      et al. (2013). BigBrain: An ultrahigh-resolution 3D human
                      brain model. Science[2] EBRAINS
                      https://search.kg.ebrains.eu/instances/f06a2fd1-a9ca-42a3-b754-adaa025adb10[3]
                      Amunts K, et al. (2020). Julich-Brain: A 3D probabilistic
                      atlas of the human brain’s cytoarchitecture. Science[4]
                      Upschulte E, et al. (2022). Contour Proposal Networks for
                      Biomedical Instance Segmentation. Medical Image Analysis.[5]
                      Wagstyl K, et al. (2020). BigBrain 3D atlas of cortical
                      layers. PLOS Biology[6] von Economo C, Koskinas GN. (1925).
                      Die Cytoarchitektonik der Hirnrinde des Erwachsenen
                      Menschen. Springer},
      month         = {Oct},
      date          = {2023-10-04},
      organization  = {7th BigBrain Workshop, Reykjavík
                       (Iceland), 4 Oct 2023 - 6 Oct 2023},
      subtyp        = {After Call},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5254 - Neuroscientific Data Analytics and AI
                      (POF4-525) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
                      G:(DE-HGF)InterLabs-0015 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1018412},
}