Speaker
Description
Introduction
The 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.
Methods
Expanding 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).
Results
The 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.
Conclusions
The 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
