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Visualization in Connectomics

Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

Connectomics is a branch of neuroscience that attempts to create a connectome, i.e., a complete map of the neuronal system and all connections between neuronal structures. This representation can be used to understand how functional brain states emerge from their underlying anatomical structures and how dysfunction and neuronal diseases arise. We review the current state-of-the-art of visualization and image processing techniques in the field of connectomics and describe a number of challenges. After a brief summary of the biological background and an overview of relevant imaging modalities, we review current techniques to extract connectivity information from image data at macro-, meso- and microscales. We also discuss data integration and neural network modeling, as well as the visualization, analysis and comparison of brain networks.

Keywords

  • Fractional Anisotropy
  • Diffusion Tensor Image
  • Functional Connectivity
  • Brain Network
  • Effective Connectivity

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Fig. 21.1
Fig. 21.2
Fig. 21.3
Fig. 21.4
Fig. 21.5
Fig. 21.6
Fig. 21.7

Notes

  1. 1.

    http://flybrain.neurobio.arizona.edu

  2. 2.

    Similar approaches have been used in genomics [62, 84] and other areas.

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Pfister, H. et al. (2014). Visualization in Connectomics. In: Hansen, C., Chen, M., Johnson, C., Kaufman, A., Hagen, H. (eds) Scientific Visualization. Mathematics and Visualization. Springer, London. https://doi.org/10.1007/978-1-4471-6497-5_21

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