Event Abstract

Atlas Based Visualization of Fiber Orientations in the Rat Brain Derived from 3D Polarized Light Imaging

  • 1 Research Centre Jülich, Institute of Neuroscience and Medicine, Germany
  • 2 University of Wuppertal, Faculty of Mathematics and Natural Sciences, Germany
  • 3 Heinrich-Heine-University Düsseldorf, C. a. O. Vogt Institute of Brain Research, Germany

Introduction 3D Polarized Light Imaging (3D-PLI) is a unique technique to measure spatial orientation of nerve fibers at resolutions of a few microns [1, 2]. The orientations are derived from birefringence measurements of unstained histological brain sections that are interpreted by a voxel-based analysis, i.e. each tissue voxel is assigned a single 3D fiber orientation vector. The 3D reconstruction of images of serial brain section by means of image registration yields a virtual brain model reflecting local fiber orientations, which can be visualized as 3D vector field. To compare such fiber models with existing parcellations schemes and atlases, the model has to be transferred into a common atlas space. The superimposition of such an atlas with fiber models enables an in-depth, voxel-based anatomical analysis of fiber orientations in 3D space. Materials and Methods The fiber orientation model of the present study was derived from measurements of an entire Wistar rat brain. The brain was serially cut with a cryostat microtome into 446 sections with 60 μm thickness each. The sections were successively scanned with the in-house developed polarimetric setup (i.e., the large-area polarimeter) and subjected to a dedicated analysis workflow [1, 2]. After the 3D reconstruction of the sections by means of sequential application of affine and B-spline based image registration [3], the model was transferred into the common rodent reference space, the Waxholm Space atlas [4, 5] using 3D affine and elastic image registration methods [6]. The generated fiber orientation model of the rat consists of a 3D vector field with a size of 588 x 723 x 413 voxels and with a voxel size of 64 μm x 64 μm x 60 μm. The total size of the data set is 2.1 GB. In order to visualize the fiber orientation model, the 3D orientation vector for each voxel was extracted and represented as a scalable glyph, i.e. as lines, cuboids or cylinders [7]. To improve the visual impression of the fiber orientations, the vectors were color-coded with RGB, HSV or a special HSV black color space. The parcellations of the Waxholm Space atlas were used as masks to visualize the fiber orientations in anatomical regions of interest (ROI). Furthermore, the brain hull, derived from the reconstructed brain model, was visualized together with the vector field to preserve the anatomical context for the observer. Results Figure 1 demonstrates the use of the atlas parcellations to show fiber orientations in anatomical ROI. Our graphical user interface enables the user to choose between 96 atlas structures (fig. 1, center). As examples, we have visualized the corpus callosum (fig. 1(a)), the hippocampal formation (fig. 1(b)), the striatum (fig. 1(c)), and the subiculum (fig. 1(d)). After selecting an anatomical ROI, the complex fiber architecture can be investigated under different viewpoints and magnifications by using tools for rotation, translation, and zooming as demonstrated for the corpus callosum (fig. 2). Figure 3 illustrates the fiber architecture of the corpus callosum in more detail. The displayed fiber orientations unveil the complex network of fibers and fiber bundles in the corpus callosum. The visualization tool shows that the orientation of fibers in the corpus callosum is not restricted to bundles running in parallel in the midline region, and then fanning out, but rather shows an architecture with partly abrupt changes in orientation (fig. 3, arrows) and fibers crossing the corpus callosum orthogonally, including regions close to the midline (fig. 3, circles). In addition, the present method enabled overcoming the problem of visual clutter and tangle, which is a typical challenge for vector field visualization. By masking out the structures of interest, the amount of data to visualize has been reduced, thus allowing to study fiber orientations interactively. Conclusion We introduced a method to facilitate 3D studies of high-resolution fiber architecture, based on the fiber orientation models derived from 3D-PLI. The use of atlas-based parcellations turned represents a powerful approach to interpret the topography of fibers, but also to improve visualization in anatomical ROI. It enabled new insights into the complex fiber architecture of the rat brain, and unveiled the different orientations and interrelations of fibers and fiber bundles. PLI-based vector-type datasets are essential prerequisites for comprehensive fiber tractography at high spatial resolution, e.g. revealing the fanning out of fibers of the corpus callosum when moving from the midline to more sagittal sections. By means of our atlas-based visualization tool, fibers and fiber bundles have been detected, which were running in rostro-caudal direction, unexpectedly close to midsagital plane of the corpus callosum, which are dominated by fibers interconnecting the two hemispheres. Figure 1: The atlas based visualization enables anatomical studies of the fiber architecture of 96 structures (center), for instance, corpus callosum (a), the hippocampal formation (b), the striatum (c), and the subiculum (d). The colors (here in HSV color space) indicate the orientation of the fibers. Figure 2: A coronal view of the fiber architecture of the corpus callosum; lower left: coronal section of the Waxholm atlas (a). The orange arrows point to the corpus callosum in the 3D fiber orientation model (b). Interactions with the model facilitate visual analysis in diverse perspectives, for instance rotation to left (c) and right (d). Figure 3: Zooming into the fiber orientation model of the corpus callosum enables new insights in the fiber architecture. It unveils different orientations and interrelations, for instance fiber orientations from left to right (arrow 1), lower right to upper left (arrows 2 and 5 with different angles), lower left to upper right (arrows 3 and 4), and from bottom to top (arrow 6). The circles point to diverse sites, where fiber orientations are perpendicular to each other. This indicates regions with fibers running orthogonal to the image plane.

Figure 1
Figure 2
Figure 3


This study was partially supported by the National Institutes of Health under grant agreement no. R01MH092311, by the Helmholtz Association through the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain", and by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project).


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[2] Axer M, Gräßel D, Kleiner M, et al. High-resolution fiber tract reconstruction in the human brain by means of polarized light imaging (3D-PLI). Front Neuroinform. 2011; 5: 1-13.

[3] Schober M, Schlömer P, Cremer M, et al. Reference Volume Generation for Subsequent 3D Reconstruction of Histological Sections. Proc BVM. 2015, Lübeck, Germany; p. 143-148.

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[5] Kjonigsen L, Lillehaug, S, Bjaalie, JG, et al. Waxholm Space atlas of the rat brain hippocampal region: Three-dimensional delineations based on magnetic resonance and diffusion tensor imaging. Neuroimage. 2015; 108: 441-449.

[6] Schubert N, Axer M, Schober M, et al. 3D Reconstructed Cyto-, Muscarinic M2 Receptor, and Fiber Architecture of the Rat Brain Registered to the Waxholm Space Atlas. Front. Neuroanat. 2016, accepted

[7] Schubert N, Gräßel D, Pietrzyk U, et al. Visualization of Vector Fields Derived from 3D Polarized Light Imaging. Proc BVM. 2016, Berlin, Germany; p. 176-181

Keywords: 3D visualization, Fiber Orientation, digital atlas, Registration, 3D Polarized Light Imaging

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Visualization

Citation: Schubert N, Pietrzyk U, Amunts K and Axer M (2016). Atlas Based Visualization of Fiber Orientations in the Rat Brain Derived from 3D Polarized Light Imaging. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00057

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Received: 30 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: Mrs. Nicole Schubert, Research Centre Jülich, Institute of Neuroscience and Medicine, Jülich, Germany, n.schubert@fz-juelich.de