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| Hauptseite > Online First > High-resolution 3D Mapping of the Human Hypothalamus:Towards a Comprehensive Cytoarchitectonic Atlas |
| Hauptseite > Online First > High-resolution 3D Mapping of the Human Hypothalamus:Towards a Comprehensive Cytoarchitectonic Atlas |
| Poster (After Call) | FZJ-2025-04912 |
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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>
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