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@INPROCEEDINGS{Chervonnyy:1048797,
      author       = {Chervonnyy, Alexey and Schiffer, Christian and Upschulte,
                      Eric and Bludau, Sebastian and Mohlberg, Hartmut and Amunts,
                      Katrin},
      title        = {{H}igh-resolution 3{D} {M}apping of the {H}uman
                      {H}ypothalamus:{T}owards a {C}omprehensive
                      {C}ytoarchitectonic {A}tlas},
      reportid     = {FZJ-2025-04912},
      year         = {2025},
      abstract     = {<b>INTRODUCTION/MOTIVATION</b><br>The hypothalamus is
                      crucial for maintaining homeostasis, regulating sleep-wake
                      cycles, appetite, circadian rhythm, and thermal regulation
                      [1]. Despite its importance, its structural organization,
                      precise boundaries, and functional differentiation of nuclei
                      remain incompletely understood. Existinganatomical maps of
                      the hypothalamus do not reflect interindividual variability
                      in 3D space; they often lack the spatial resolution and
                      morphological detail to provide a comprehensive
                      understanding of this complex region and to inform
                      neuroimaging studies about the microstructure. Therefore, we
                      aimed to develop probabilistic cytoarchitectonic maps to
                      address intersubject variability and provide a
                      highresolution 3D map of the hypothalamus to neuroimaging
                      studies of the living human
                      brain.<br><br><b>METHODS</b><br>Using every 15th cell body
                      stained brain section (1 μm resolution) from 10 postmortem
                      brains (5 female), including the BigBrain dataset [2], we
                      delineated the hypothalamus and its nuclei. For the BigBrain
                      dataset, a deep learning–based tool [3] was employed to
                      delineate the remaining sections and create a continuous
                      high-resolution 3D model. Delineated nuclei were
                      3D-reconstructed and superimposed in standard reference
                      space [4], and corresponding probability maps were generated
                      to quantify intersubject variability in their size and
                      spatial location. To further characterise cytoarchitectonic
                      features of nuclei, we performed texture analysis [5] on
                      6,709 regions of interest derived from the initial
                      delineations, employing the Gray Level Co-occurrence Matrix
                      method [6] to quantify local spatial relationships and
                      intensity distributions in grayscale images. Differences
                      between hypothalamic subdivisions were assessed using the
                      independent-samples Kruskal-Wallis test. In parallel,
                      neurons were segmented using a Contour Proposal Network
                      based on Fourier Descriptors [7], enabling precise
                      measurements of neuron number, size, and
                      morphology.<br><br><b>RESULTS AND DISCUSSION</b><br>We
                      generated a high-resolution 3D map of 23 nuclei of the human
                      hypothalamus, that show their shapes and neighbourhood
                      relationships with high precision (Fig. 1). Intersubject
                      variability was reflected in the probabilistic maps, which
                      will be made openly available as part of the
                      Julich-BrainAtlas [4] and accessible via EBRAINS and other
                      platforms. Principal Component Analysis (PCA; SPSS v.29)
                      identified four main components explaining $87.27\%$ of the
                      total variance. Significant differences in at least one main
                      component were observed between all adjacent nuclei,
                      supporting their delineation. For visualisation, we
                      generated a heatmap (Fig.2) indicating levels of
                      cytoarchitectural difference: a score of 0 showed no
                      significant differences, while a score of 4 indicated
                      pronounced disparities across all components. In addition,
                      some more distant nuclei, such as the uncinate and
                      suprachiasmatic nuclei, showed no significant differences in
                      the PCA components. These cytoarchitectural similarities may
                      suggest functional connectivity between distant nuclei and
                      warrant further investigation of their interactions. The
                      contour proposal network enabled pixel-level labeling of
                      cells in microscopic images, facilitating the identification
                      of individual neurons. Using the extracted data, such as the
                      number of neurons and their size, we calculated the cell
                      packing density and observed the highest density in the
                      supraoptic nucleus and the lowest in the lateral tuberal
                      nucleus, which was three times less dense. In summary, the
                      new maps of the hypothalamus with its 23 nuclei provide
                      highly detailed reference data on its structure,
                      intersubjective variability and localization in the standard
                      reference space. This resource will support the
                      identification of microstructural correlates of functional
                      and connectivity data in both healthy individuals and
                      patients.<br><br><b>REFERENCES</b><ol><li>Nieuwenhuys, R.,
                      Voogd, J., van Huijzen, C., $\&$ Papa, M. (2010). The Human
                      Central Nervous System. Springer-Verlag Italia.
                      https://doi.org/10.1007/978-88-470-1140-3</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>Schiffer,
                      C., Spitzer, H., Kiwitz, K., Unger, N., Wagstyl, K., Evans,
                      A. C., Harmeling, S., Amunts, K., $\&$ Dickscheid, T.
                      (2021). Convolutional neural networks for cytoarchitectonic
                      brain mapping at large scale. NeuroImage, 240, 118327.
                      https://doi.org/10.1016/j.neuroimage.2021.118327</li><li>Amunts,
                      K., Mohlberg, H., Bludau, S., $\&$ Zilles, K. (2020).
                      Julich-Brain: A 3D probabilistic atlas of the human
                      brain’s cytoarchitecture. Science, 369(6506), 988–992.
                      https://doi.org/10.1126/SCIENCE.ABB4588</li><li>Kedo, O.,
                      Bludau, S., Schiffer, C., Mohlberg, H., Dickscheid, T., $\&$
                      Amunts, K. (2024). Cytoarchitectonic Analysis and 3D Maps of
                      the Mesial Piriform Region in the Human Brain. Anatomia,
                      3(2), 68-92.
                      https://doi.org/10.3390/anatomia3020007</li><li>Löfstedt T,
                      Brynolfsson P, Asklund T, Nyholm T, Garpebring A (2019)
                      Gray-level invariant Haralick texture features. PLoS ONE
                      14(2): e0212110.
                      https://doi.org/10.1371/journal.pone.0212110</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></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)
                      / Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(EU-Grant)101147319 /
                      G:(DE-HGF)InterLabs-0015 / G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1048797},
}