Atlases

Brain Atlases

A new generation of 3D reference atlases of the human and rodent brain, defined at different scales and modalities


Multilevel Brain Atlases

Multilevel Brain Atlases integrate multiple reference spaces and maps of the human, rodent and mouse brain into a common framework

The interactive Atlas Viewer allows to explore reference atlases in 3D, even at microscopic detail

By specifying regions in an atlas, you can find related neuroscience data in the Knowledge Graph

EBRAINS offers interactive tools for connecting your own 2D and 3D data to the reference atlases, as well as a range of tools for analyzing and comparing information from different brain regions

Atlases

BigBrain Atlas

Microscopic 3D model with maps of cytoarchitectonic structures

Use BigBrain (Amunts et al., 2013) to explore human brain architecture at the microscopic level. It is the highest resolution atlas of the human brain, with a nearly cellular isotropic resolution of 20 micrometer, and based on a reconstruction from 7404 digitized histological brain sections. Stained for cell bodies using a modified Silver staining, BigBrain provides a highly detailed anatomical reference space which resolves individual cortical layers and large cell bodies. We provide detailed 3D maps of cortical layers (Wagstyl et al., 2020) that were automatically computed based on cortical intensity profiles, as well as maps of cytoarchitectonic areas created with support of machine learning algorithms (Spitzer et al., 2018). The border definitions match those of the Julich-Brain cytoarchitectonic atlas (Amunts & Zilles, 2015) and are linked to the corresponding probabilistic maps in MNI space.

Brain model with 3D maps

ATLASES

Julich-Brain Atlas

Probabilistic cytoarchitectonic maps in MNI space

Refer to the Julich-Brain probabilistic cytoarchitectonic atlas for a highly detailed micro-structural parcellation of the human brain with explicit information about their variability in different individuals. The Julich-Brain probabilistic cytoarchitectonic atlas defines cortical areas and subcortical nuclei by histological analysis of ten human post-mortem brains for each structure, based on reproducible border delineations that were performed at microscopic resolution in serial, cell-body stained histological sections (Amunts & Zilles, 2015). The individual maps are transferred to MNI standard space at 1mm isotropic resolution, and 3D probabilistic maps are calculated. These maps capture intersubject variability, and describe how likely a particular structure is found at each voxel. A maximum probability parcellation further summarizes all maps by assigning each voxel to the most likely histological area (Eickhoff et al., 2005).

ATLASES

Neurospin fiber bundle atlases

Probabilistic maps of hundreds of fiber bundles in MNI space

Explore Neurospin fiber bundle atlases to examine the structure and variability of deep and superficial white matter fibre bundles in the human brain. These atlases have been created from diffusion MRI using fiber tractography. The first two atlases (Guevara et al., 2012, Guevara et al., 2017) have been inferred from the ARCHI dataset distributed by the HBP. The most recent atlas (Labra Avila et al., 2019) has been inferred from the Human Connectome Project dataset and includes over 700 short bundles. These atlases have been aligned with the MNI space using a framework matching the main cortical sulci. For each bundle, a probabilistic map captures intersubject variability. Each short bundle is labelled by the two connected ROIs of the Desikan-Killiany atlas. Postmortem validations are in progress using dMRI, PLI and Klingler dissection. For each atlas, a maximum probability parcellation of white matter provides for each voxel the most likely bundle.

ATLASES

Waxholm Space (WHS) rat brain atlas

The Waxholm Space (WHS) rat brain atlas is an open access volumetric atlas offering comprehensive anatomical delineations of the rat brain based on structural contrast in isotropic magnetic resonance (39 μm) and diffusion tensor (78 μm) images acquired ex vivo from an 80 day old male Sprague Dawley rat at the Duke Center for In Vivo Microscopy. Spatial reference is provided by the Waxholm Space coordinate system.

Footnotes

K. Amunts et al., ‘BigBrain: An Ultrahigh-Resolution 3D Human Brain Model’, Science, 2013, doi: 10.1126/science.1235381
K. Wagstyl et al., ‘BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices’, PLOS Biology, 2020, doi: 10.1371/journal.pbio.3000678.1.
H. Spitzer et al., ‘Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks’, MICCAI 2018
K. Amunts and K. Zilles, ‘Architectonic Mapping of the Human Brain beyond Brodmann’. Neuron 88 (6): 1086–1107, 2015
S. Eickhoff et al., ‘A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data’. NeuroImage 25(4), 1325-1335, 2005
P. Guevara et al., Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. Neuroimage, 61(4):1083-1099, 2012, https://doi.org/10.1016/j.neuroimage.2012.02.071
M. Guevara et al., Reproducibility of superficial white matter tracts using diffusion weighted imaging tractography. NeuroImage, 147:703-725, 2017, https://doi.org/10.1016/j.neuroimage.2016.11.066
N. Labra Avila et al., Inference of an Extended Short Fiber Bundle Atlas Using Sulcus-Based Constraints for a Diffeomorphic Inter-subject Alignment. In Computational Diffusion MRI, MICCAI, pp 323-333, 2019, https://doi.org/10.1007/978-3-030-05831-9_25

Coming soon to EBRAINS Atlases

◦ Tools and workflows for registration of data to the atlases
◦ Tools and workflows for analysis of data registered to the atlases

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