Abstract
Human brain atlases [1] are indispensable tools to achieve a better understanding of the multilevel organization of the brain through integrating and analyzing data from different brains, sources, and modalities while considering the functionally relevant topography of the brain [4]. The spatial resolution of most of these electronic atlases is in the range of millimeters, which does not allow the integration of the information at the level of cortical layers, columns, microcircuits or cells. Therefore, we introduced in 2013 the first BigBrain data set with a resolution of 20 \(\upmu \)m isotropic. This data set allows to specify morphometric parameters of human brain organization, which serve as a “gold standard” for neuroimaging data obtained at a lower spatial resolution. It provides, in addition, an essential basis for realistic brain models concerning structural analysis and simulation [2]. For the generation of other, even higher-resolution data sets of the human brain, we developed an improved and more efficient data processing workflow employing high performance computing to 3D reconstruct histological data sets. To facilitate the analysis of intersubject variability on a microscopic level, the new processing framework was applied for reconstructing a second BigBrain data set with 7676 sections. Efficient data processing of a large amount of data sets with a complex nested reconstruction workflow using large number of compute nodes required optimized distributed processing workflows as well as parallel programming. A detailed documentation of the processing steps and the complex inter-dependencies of the data sets at each level of the multi-step reconstruction workflow was essential to enable transformations to images of the same histological sections obtained with even higher spatial resolution. We have addressed these challenges, and achieved efficient high throughput processing of thousands of images of histological sections in combination with sufficient flexibility, based on an effective, successive coarse-to-fine hierarchical processing.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zilles, K., Amunts, K.: Centenary of Brodmann’s map - conception and fate. Nat. Rev. Neurosci. 11(2), 139–145 (2010)
Amunts, K., Lepage, C., Borgeat, L., Mohlberg, H., Dickscheid, T., Rousseau, M., Bludau, S., Bazin, P., Lewis, L., Oros-Peusquens, A., Shah, N., Lippert, T., Zilles, K., Evans, A.C.: BigBrain: an ultrahigh-resolution 3D human brain model. Science 340, 1472–1475 (2013)
Amunts, K., Bücker, O., Axer, M.: Towards a multiscale, high-resolution model of the human brain. In: Grandinetti, L., Lippert, T., Petkov, N. (eds.) BrainComp 2013. LNCS, vol. 8603, pp. 3–14. Springer, Heidelberg (2014). doi:10.1007/978-3-319-12084-3_1
Amunts, K., Hawrylycz, M., Van Essen, D., Van Horn, J.D., Harel, N., Poline, J.B., De Martino, F., Bjaalie, J.G., Dehaene-Lambertz, G., Dehaene, S., Valdes-Sosa, P., Thirion, B., Zilles, K., Hill, S.L., Abrams, M.B., Tass, P.A., Vanduffel, W., Evans, A.C., Eickhoff, S.B.: Interoperable atlases of the human brain. Neuroimage 99, 525–532 (2014)
Brodmann, K.: Vergleichende Lokalisationslehre der Großhirnrinde in ihren Prinzipien dargestellt auf Grund des Zellbaues. Barth, Leipzig (1909)
Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T., Zilles, K.: Brodmanns area 17 and 18 brought into stereotaxic space where and how variable? Neuroimage 11(1), 66–84 (2000)
Eickhoff, S., Stephan, K.E., Mohlberg, H., Grefkes, C., Fink, G.R., Amunts, K., Zilles, K.: A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25(4), 1325–1335 (2005)
Evans, A.C., Janke, A.L., Collins, D.L., Bailllet, S.: Brain templates and atlases. Neuroimage 62(2), 911–922 (2012)
Delescluse, M., Franconville, R., Joucla, S., Lieurya, T., Pouzat, C.: Making neurophysiological data analysis reproducible. Why and how? J. Physiol. Paris 106, 159–170 (2011)
Hömke, L.: A multigrid method for anisotropic PDEs in elastic image registration. Numer. Linear Algebra Appl. 13, 215–229 (2006)
Lepage, C., Mohlberg, H., Pietrzyk, U., Amunts, K., Zilles, K., Evans, A.C.: Automatic repair of acquisition defects in reconstruction of histology slices of the human brain. In: 16th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Barcelona, 06. - 10.06.2010: available on CD-ROM (2010)
Zhu, C., Byrd, R.H., Nocedal, L.: L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization. ACM Trans. Math. Softw. 23(4), 550–560 (1997)
Thévenaz, P., Blu, T., Unser, M.: Interpolation revisited. IEEE Trans. Med. Imaging 19(7), 739–758 (2000)
Lewis, L., Lepage, C., Fournier, M., Zilles, K., Amunts, K., Evans, A.C.: BigBrain: Initial tissue classification and surface extraction. In: 20th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Hamburg (2014)
Liu, S., Azevedo, C., Pelletier, D.: The use of BigBrain in MS: An ultrahigh-resolution 3D template for grey matter MRI segmentation. Neurology 82(10), 6.135 (2014)
Wagstyl, K., Lepage, C., Zilles, K., Amunts, K., Fletcher, P., Evans, A.: BigBrain: Automated analysis of laminar structure in the cerebral cortex. In: 22nd Annual Meeting of the Organization for Human Brain Mapping (OHBM), Geneva (2016)
JuBrain Cytoviewer: https://www.jubrain.fz-juelich.de/apps/cytoviewer/cytoviewer-main.php
Python Software Foundation: Python Language Reference, Version 2.7. http://www.python.org
MPI for Python. http://pypi.python.org/pypi/mpi4py
Graph-tool: Efficient network analysis with Python. http://www.graph-tool.org
Insight Segmentation and Registration Toolkit (ITK). http://www.itk.org
The HDF Group: Hierarchical data format (HDF), Version 5. http://www.hdfgroup.org/HDF5
Minc Tool Kit. http://www.bic.mni.mcgill.ca/ServicesSoftware/MINC
The Open Provenance Model (OPM). http://www.openprovenance.org
The Human Brain Project (HBP). https://www.humanbrainproject.eu
Acknowledgments
The authors thank Claude Lepage and Alan C. Evans from the Montreal Neurological Institute, Montreal, Canada, for developing and providing the HPC workflow for the 3-D reconstruction of the first BigBrain, which served as a basis for the recent workflow and for many fruitful and inspiring discussions. We thank Ana Oros-Peusquens and Nadim J. Shah for their contribution in MR-imaging. Funding was provided for this study by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project), and the Portfolio Theme Supercomputing and Modeling for the Human Brain of the German Helmholtz Association.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Mohlberg, H., Tweddell, B., Lippert, T., Amunts, K. (2016). Workflows for Ultra-High Resolution 3D Models of the Human Brain on Massively Parallel Supercomputers. In: Amunts, K., Grandinetti, L., Lippert, T., Petkov, N. (eds) Brain-Inspired Computing. BrainComp 2015. Lecture Notes in Computer Science(), vol 10087. Springer, Cham. https://doi.org/10.1007/978-3-319-50862-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-50862-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50861-0
Online ISBN: 978-3-319-50862-7
eBook Packages: Computer ScienceComputer Science (R0)