TY  - CONF
AU  - Bludau, Sebastian
AU  - Dickscheid, Timo
AU  - Schiffer, Christian
AU  - Steffens, Anna
AU  - Upschulte, Eric
AU  - Amunts, Katrin
TI  - Layer-specific cell counts in BigBrain – decomposing cortex-wide numbers based on cytoarchitectonics
M1  - FZJ-2025-04876
PY  - 2025
AB  - <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>
T2  - EBRAINS summit 2025
CY  - 8 Dec 2025 - 11 Dec 2025, Brüssel (Belgium)
Y2  - 8 Dec 2025 - 11 Dec 2025
M2  - Brüssel, Belgium
LB  - PUB:(DE-HGF)24
UR  - https://juser.fz-juelich.de/record/1048761
ER  -