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Workflows for Ultra-High Resolution 3D Models of the Human Brain on Massively Parallel Supercomputers

  • Hartmut MohlbergEmail author
  • Bastian Tweddell
  • Thomas Lippert
  • Katrin Amunts
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10087)

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.

Keywords

Ultra-high resolution brain models BigBrain Cytoarchitecture Microstructure High performance computing Workflows Human Brain Atlas 

Notes

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.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Hartmut Mohlberg
    • 1
    Email author
  • Bastian Tweddell
    • 2
  • Thomas Lippert
    • 2
  • Katrin Amunts
    • 1
    • 3
  1. 1.Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich and JARA-BrainJülichGermany
  2. 2.Juelich Supercomputing Centre (JSC), IAS, and JARA-HPC, Research Centre JuelichJülichGermany
  3. 3.C. and O. Vogt Institute of Brain ResearchHeinrich-Heine UniversityDüsseldorfGermany

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