• DocumentCode
    3498367
  • Title

    Knife-edge scanning microscopy for connectomics research

  • Author

    Choe, Yoonsuck ; Mayerich, David ; Kwon, Jaerock ; Miller, Daniel E. ; Chung, Ji Ryang ; Sung, Chul ; Keyser, John ; Abbott, Louise C.

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Texas A&M Univ., College Station, TX, USA
  • fYear
    2011
  • fDate
    July 31 2011-Aug. 5 2011
  • Firstpage
    2258
  • Lastpage
    2265
  • Abstract
    In this paper, we will review a novel microscopy modality called Knife-Edge Scanning Microscopy (KESM) that we have developed over the past twelve years (since 1999) and discuss its relevance to connectomics and neural networks research. The operational principle of KESM is to simultaneously section and image small animal brains embedded in hard polymer resin so that a near-isotropic, sub-micrometer voxel size of 0.6 μm × 0.7 μm × 1.0 μm can be achieved over ~1 cm3 volume of tissue which is enough to hold an entire mouse brain. At this resolution, morphological details such as dendrites, dendritic spines, and axons are visible (for sparse stains like Golgi). KESM has been successfully used to scan whole mouse brains stained in Golgi (neuronal morphology), Nissl (somata), and India ink (vasculature), providing unprecedented insights into the system-level architectural layout of microstructures within the mouse brain. In this paper, we will present whole-brain-scale data sets from KESM and discuss challenges and opportunities posed to connectomics and neural networks research by such detailed yet system-level data.
  • Keywords
    brain; neural nets; neurophysiology; animal brain; axon; connectomics; dendrites; dendritic spine; hard polymer resin; knife-edge scanning microscopy; microscopy modality; mouse brain; neural network; neuronal morphology; somata; system-level architectural layout; vasculature; Data visualization; Image reconstruction; Image resolution; Mice; Microscopy; Three dimensional displays;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2011 International Joint Conference on
  • Conference_Location
    San Jose, CA
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4244-9635-8
  • Type

    conf

  • DOI
    10.1109/IJCNN.2011.6033510
  • Filename
    6033510