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BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10081)

Abstract

Most available 3D human brain atlases provide information only at a macroscopic level, while 2D atlases are often at a microscopic level but lack 3D integration. A 3D atlas defined upon fine-grain anatomical detail of cortical layers and cells is necessary to fully understand neurobiological processes. “BigBrain,” a high-resolution 3D model of a human brain at nearly cellular resolution, was released in 2013. This unique dataset enables the extraction of microscopic data for utilization in brain mapping, modeling and simulation. We propose an automated 3D cortical parcellation of the BigBrain volume into functionally-meaningful areas in order to create a modern high-resolution 3D cytoarchitectural atlas that will complement existing brain atlases. We use a distance metrics-based framework for BigBrain parcellation, and perform comparative analyses of our results with existing atlases (Brodmann and JuBrain atlases). This work has immediate application in teaching, neurosurgery, cognitive neuroscience, and imaging-based brain mapping.

Keywords

  • Brain atlases
  • Cortical cytoarchitecture
  • Segmentation and parcellation
  • Brain mapping
  • Image registration
  • Computational neuroscience

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Acknowledgements

We acknowledge funding support from the Canadian Institutes of Health Research (CIHR) and from Canada’s Advanced Research and Innovation Network (CANARIE). We thank Compute Canada for continued support accessing the Compute Canada HPC grid through the CBRAIN software portal. We also thank Svenja Caspers for helpful discussion and providing expertise in neuroanatomy as well as Katrin Amunts and Karl Zilles from the Jülich Research Centre in Germany.

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Correspondence to Marc Fournier .

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Fournier, M., Lewis, L.B., Evans, A.C. (2017). BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases. In: , et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-61188-4_2

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