Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture
By Katrin Amunts, Hartmut Mohlberg, S. Bludau, Karl Zilles. Science, 2020
498 citations recorded by the source corpus.
A present-day atlas of the human brain Defining brain regions and demarking their spatial extent are important goals in neuroscience. A modern map of the brain's cellular structure, a cytoarchitectonic atlas, should provide maps of areas in three dimensions, integrate recent knowledge about brain parcellation, consider variations between individual brains, rely on reproducible workflows, and provide web-based links to other resources and databases. Amunts et al. created such an atlas based on serial histological sections of brain. They developed a computational framework and refined the current boundaries of the human brain based on cytoarchitectural patterns. This technique can easily be transferred to build brain atlases for other species or a spatial framework for other organs, other modalities, or multimodal maps for regions of interest at higher spatial scales. This research makes similar future attempts simultaneously reproducible and flexible. Science, this issue p. 988 A virtual human brain containing cytoarchitectonic maps of cortical areas and subcortical nuclei can help in the interpretation of neuroimaging. Cytoarchitecture is a basic principle of microstructural brain parcellation. We introduce Julich-Brain, a three-dimensional atlas containing cytoarchitectonic maps of cortical areas and subcortical nuclei. The atlas is probabilistic, which enables it to account for variations between individual brains. Building such an atlas was highly data- and labor-intensive and required the development of nested, interdependent workflows for detecting borders between brain areas, data processing, provenance tracking, and flexible execution of processing chains to handle large amounts of data at different spatial scales. Full cortical coverage was achieved by the inclusion of gap maps to complement cortical maps. The atlas is dynamic and will be adapted as mapping progresses; it is openly available to support neuroimaging studies as well as modeling and simulation; and it is interoperable, enabling connection to other atlases and resources.
Subfields: Molecular Biology, Pharmacology
View Paper: https://www.semanticscholar.org/paper/0f1d1510df4328df5dd4026b3a03b8b242c4062c
Citations in the corpus
- CAT – A Computational Anatomy Toolbox for the Analysis of Structural MRI Data (2023, unknown): “Amunts, K. et al. (2020) ‘Julich-Brain: A 3D probabilistic atlas of the human brain’s cytoarchitecture.’, Science. American Association for the Advancement of Science, pp. 988–992. doi:10.1126/SCIENCE.ABB4588. Ashburner, J. (2007) ‘A fast diffeomorphic image registration algorithm’, NeuroImage, 38, ”
- Intracranial Mapping of Response Latencies and Task Effects for Spoken Syllable Processing in the Human Brain (2024, unknown): “This MNI deformation field was then used to warp the Julich volumetric atlas into patient space (Amunts et al., 2020; Eickhoff et al., 2005; Evans et al., 2012), and each channel was localized to a brain region by finding the region label of the closest voxel. sEEG pre-processing In line with…”