The ‘BigBrain’ is a high-resolution data set of the human brain that enables three-dimensional (3D) analyses with a 20 µm spatial resolution at nearly cellular level. We use this data set to explore pre-α (cell) islands of layer 2 in the entorhinal cortex (EC), which are early affected in Alzheimer’s disease and have therefore been the focus of research for many years. They appear mostly in a round and elongated shape as shown in microscopic studies. Some studies suggested that islands may be interconnected based on analyses of their shape and size in two-dimensional (2D) space. Here, we characterized morphological features (shape, size, and distribution) of pre-α islands in the ‘BigBrain’, based on 3D-reconstructions of gapless series of cell-body-stained sections. The EC was annotated manually, and a machine-learning tool was trained to identify and segment islands with subsequent visualization using high-performance computing (HPC). Islands were visualized as 3D surfaces and their geometry was analyzed. Their morphology was complex: they appeared to be composed of interconnected islands of different types found in 2D histological sections of EC, with various shapes in 3D. Differences in the rostral-to-caudal part of EC were identified by specific distribution and size of islands, with implications for connectivity and function of the EC. 3D compactness analysis found more round and complex islands than elongated ones. The present study represents a use case for studying large microscopic data sets. It provides reference data for studies, e.g. investigating neurodegenerative diseases, where specific alterations in layer 2 were previously reported.
The human lateral geniculate body (LGB) with its six sickle shaped layers (lam) represents the principal thalamic relay nucleus for the visual system. Cytoarchitectonic analysis serves as the groundtruth for multimodal approaches and studies exploring its function. This technique, however, requires experienced knowledge about human neuroanatomy and is costly in terms of time. Here we mapped the six layers of the LGB manually in serial, histological sections of the BigBrain, a high-resolution model of the human brain, whereby their extent was manually labeled in every 30th section in both hemispheres. These maps were then used to train a deep learning algorithm in order to predict the borders on sections in-between these sections. These delineations needed to be performed in 1 µm scans of the tissue sections, for which no exact cross-section alignment is available. Due to the size and number of analyzed sections, this requires to employ high-performance computing. Based on the serial section delineations, high-resolution 3D reconstruction was performed at 20 µm isotropic resolution of the BigBrain model. The 3D reconstruction shows the shape of the human LGB and its sublayers for the first time at cellular precision. It represents a use case to study other complex structures, to visualize their shape and relationship to neighboring structures. Finally, our results could provide reference data of the LGB for modeling and simulation to investigate the dynamics of signal transduction in the visual system.
The human metathalamus plays an important role in processing visual and auditory information. Understanding its layers and subdivisions is important to gain insights in its function as a subcortical relay station and involvement in various pathologies. Yet, detailed histological references of the microanatomy in 3D space are still missing. We therefore aim at providing cytoarchitectonic maps of the medial geniculate body (MGB) and its subdivisions in the BigBrain – a high-resolution 3D-reconstructed histological model of the human brain, as well as probabilistic cytoarchitectonic maps of the MGB and lateral geniculate body (LGB). Therefore, histological sections of ten postmortem brains were studied. Three MGB subdivisions (MGBv, MGBd, MGBm) were identified on every 5th BigBrain section, and a deep-learning based tool was applied to map them on every remaining section. The maps were 3D-reconstructed to show the shape and extent of the MGB and its subdivisions with cellular precision. The LGB and MGB were additionally identified in nine other postmortem brains. Probabilistic cytoarchitectonic maps in the MNI “Colin27” and MNI ICBM152 reference spaces were computed which reveal an overall low interindividual variability in topography and extent. The probabilistic maps were included into the Julich-Brain atlas, and are freely available. They can be linked to other 3D data of human brain organization and serve as an anatomical reference for diagnostic, prognostic and therapeutic neuroimaging studies of healthy brains and patients. Furthermore, the high-resolution MGB BigBrain maps provide a basis for data integration, brain modeling and simulation to bridge the larger scale involvement of thalamocortical and local subcortical circuits.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.