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

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 20μm 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 higherresolution 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 multistep 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. c © Springer International Publishing AG 2016 K. Amunts et al. (Eds.): BrainComp 2015, LNCS 10087, pp. 15–27, 2016. DOI: 10.1007/978-3-319-50862-7 2 16 H. Mohlberg et al.