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@INPROCEEDINGS{Fedorchenko:1048795,
      author       = {Fedorchenko, Nataliia and Ruland, Sabine Helene and
                      Mohlberg, Hartmut and Bludau, Sebastian and Schiffer,
                      Christian and Benning, Kai and Trettenbrein, Patrick C. and
                      Friederici, Angela C. and Amunts, Katrin},
      title        = {{F}ine-{G}rained {C}ytoarchitectonic {P}arcellation of
                      {B}roca’s {R}egion {S}upports{F}unctional
                      {D}ifferentiation — {I}n {J}ulich-{B}rain {A}tlas
                      ({EBRAINS})},
      reportid     = {FZJ-2025-04910},
      year         = {2025},
      abstract     = {<b>Introduction</b><br>Broca’s region plays a key role in
                      language and action processing, yet its classical
                      subdivision intoareas 44 and 45 does not fully capture its
                      functional heterogeneity. Existing anatomical maps oftenlack
                      sufficient granularity and do not adequately reflect
                      interindividual variability. To address this, weperformed a
                      detailed cytoarchitectonic parcellation of Broca’s region,
                      complemented by 3Dreconstruction and layer-specific cell
                      segmentation approaches. The resulting maps will be
                      integratedinto the EBRAINS infrastructure to facilitate
                      neuroimaging and brain modelling
                      research.</b><br><br><b>Methods</b><br>Ten post-mortem human
                      brains (5 female, 5 male; age range 30–80 years) were
                      analyzed using anobserver-independent, quantitative
                      cytoarchitectonic mapping method (Bludau et al., 2014).
                      Thisapproach identified four subdivisions—44p, 44a, 45p,
                      and 45a—arranged along the anterior-posterioraxis of
                      Broca’s region. 3D probability maps (PMs), and maximum
                      probability maps (MPMs) weregenerated in MNI Colin27 and
                      MNI152 stereotaxic reference spaces to capture
                      interindividual spatialvariability. Ultra-high-resolution 3D
                      reconstructions were performed using the BigBrain (Amunts
                      etall., 2013) dataset at 1 μm isotropic resolution
                      following the methods of (Schiffer et al., 2021).
                      Layerspecific cell segmentation was conducted using Contour
                      Proposal Networks (CPN), a state-of-the-artobject instance
                      segmentation method for biomedical images (Upschulte et al.,
                      2022); quantitative cellcounts are pending. Lateralization
                      was quantified by calculating Euclidean distances between
                      left andright homologous subdivisions. Structural
                      relationships with adjacent cortical areas were explored
                      viahierarchical clustering and multidimensional scaling.
                      Functional relevance was assessed by mappingfMRI activation
                      peaks reported in key studies (Goucha $\&$ Friederici, 2015;
                      Zaccarella $\&$ Friederici,2015; Papitto et al., 2024).
                      Furthermore, a meta-analysis using MPMs of the four
                      subdivisions asspeed regions identified brain areas
                      consistently co-activated during various cognitive
                      andsensorimotor tasks.</b><br><br><b>Results</b><br>The four
                      subdivisions—44p, 44a, 45p, and 45a—were robustly
                      identified and exhibited distinctcytoarchitectonic features.
                      Probabilistic maps showed stable spatial distributions with
                      measurableinterindividual variability. BigBrain-based
                      reconstructions provided detailed visualization
                      ofmicrostructural anatomy. Layer-specific cell segmentation
                      delineated cortical layers, with quantitativeanalysis
                      ongoing. Lateralization analysis revealed left-right spatial
                      asymmetries, with medianEuclidean distances of approximately
                      1.5–2.5 for key subdivisions, supporting known
                      left-hemispheredominance in language processing. Functional
                      mapping linked 44p primarily with action and syntax, 44a
                      with syntax, and 45p/45a with semantic processing. The
                      meta-analysis further confirmeddifferential co- activation
                      patterns across the four subdivisions, highlighting their
                      functionalspecialization.</b><br><br><b>Conclusions</b><br>This
                      work offers a refined cytoarchitectonic parcellation of
                      Broca’s region with probabilistic 3D maps,supporting its
                      functional differentiation. Integration with high-resolution
                      3D reconstructions and layerspecific segmentation advances
                      the microstructural understanding of this critical language
                      areas.Lateralization results align with known hemispheric
                      specialization. All data and maps will be madepublicly
                      available via the Julich-Brain Atlas (Amunts et al., 2020)
                      on the EBRAINS platform,promoting FAIR data access and
                      supporting future neuroimaging, brain modeling, and
                      structurefunction
                      investigations.</b><br><br><b>Aknowledgements</b><br>This
                      work was in part funded by Max Planck School of Cognition,
                      Leipzig, Germany, as well receiveda funding from European
                      Union’s Horizon 2020 Research and Innovation Programme
                      under ´GrantAgreement No. 101147319 (EBRAINS 2.0 Project)
                      as well as from the Helmholtz Association’sInitiative and
                      Networking Fund through the Helmholtz International BigBrain
                      Analytics and Learninglaboratory (HIBALL) under the
                      Helmholtz International Lab grant agreement
                      InterLabs-0015</b><br><br><b>References</b><ol><li>Bludau,
                      S., Eickhoff, S. B., Mohlberg, H., Caspers, S., Laird, A.
                      R., Fox, P. T., Schleicher, A., Zilles, K., $\&$ Amunts, K.
                      (2014). Cytoarchitecture, probability maps and functions of
                      the human frontal pole. NeuroImage, 93 Pt 2(Pt 2),
                      260–275.
                      https://doi.org/10.1016/j.neuroimage.2013.05.052</li><li>Amunts,
                      K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T.,
                      Rousseau, M. É., Bludau, S., Bazin, P. L., Lewis, L. B.,
                      Oros-Peusquens, A. M., Shah, N. J., Lippert, T., Zilles, K.,
                      $\&$ Evans, A. C. (2013). BigBrain: an ultrahigh-resolution
                      3D human brain model. Science (New York, N.Y.),340(6139),
                      1472–1475.
                      https://doi.org/10.1126/science.1235381</li><li>Christian
                      Schiffer, Hannah Spitzer, Kai Kiwitz, Nina Unger, Konrad
                      Wagstyl, Alan C. Evans, Stefan Harmeling, Katrin Amunts,
                      Timo Dickscheid, Convolutional neural networks for
                      cytoarchitectonic brain mapping at large scale, NeuroImage,
                      Volume 240,
                      2021,https://doi.org/10.1016/j.neuroimage.2021.118327</li><li>Upschulte,
                      E., Harmeling, S., Amunts, K., $\&$ Dickscheid, T. (2022).
                      Contour proposal networks for biomedical instance
                      segmentation. Medical image analysis, 77, 102371.
                      https://doi.org/10.1016/j.media.2022.102371</li><li>Goucha,
                      T., $\&$ Friederici, A. D. (2015). The language skeleton
                      after dissecting meaning: A functional segregation within
                      Broca's Area. NeuroImage, 114, 294–302.
                      https://doi.org/10.1016/j.neuroimage.2015.04.011</li><li>Zaccarella,
                      E., $\&$ Friederici, A. D. (2015). Merge in the Human Brain:
                      A Sub-Region Based Functional Investigation in the Left Pars
                      Opercularis. Frontiers in psychology, 6, 1818.
                      https://doi.org/10.3389/fpsyg.2015.01818</li><li>Papitto G,
                      Friederici AD, Zaccarella E. Distinct neural mechanisms for
                      action access and execution in the human brain: insights
                      from an fMRI study. Cereb Cortex. 2024 Apr 1;34(4):bhae163.
                      doi: 10.1093/cercor/bhae163. PMID: 38629799; PMCID:
                      PMC11022341.</li><li>Amunts, K., Mohlberg, H., Bludau, S.,
                      $\&$ Zilles, K. (2020). Julich-Brain: A 3D probabilistic
                      atlas of the human brain's cytoarchitecture. Science (New
                      York, N.Y.), 369(6506), 988–992.
                      https://doi.org/10.1126/science.abb458Z</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          = {5251 - Multilevel Brain Organization and Variability
                      (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)POF4-5251 / G:(EU-Grant)101147319 /
                      G:(DE-HGF)InterLabs-0015},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1048795},
}