• DocumentCode
    1927877
  • Title

    Quantitative synaptic vesicle imaging for evaluating neuron activities in neurodegenerative diseases

  • Author

    Fan, Jing ; Xia, Xiaofeng ; Dy, Jennifer ; Wong, Stephen

  • Author_Institution
    Syst. Med. & Bioeng. Dept., Methodist Hosp. Res. Inst., Houston, TX, USA
  • fYear
    2011
  • fDate
    6-9 Nov. 2011
  • Firstpage
    421
  • Lastpage
    425
  • Abstract
    Synaptic vesicle dynamics play an important role in studying neuronal and synaptic activities of neurodegenerative diseases ranging from epidemic Alzheimer´s disease to rare Rett syndrome. To obtain significant statistical power in such studies, we developed a high content analysis (HCA) pipeline to visualize the vesicle dynamics and characterize the neuronal synaptic activities in a large population of neurons. Our experiments on hippocampal neuron assays showed that the proposed HCA system can automatically detect vesicles and quantify their dynamics for evaluating neuron activities. The availability of such an automated system would open up a vista to investigate synaptic neuropathology and identify candidate therapeutics of neurodegeneration.
  • Keywords
    data visualisation; diseases; medical image processing; neurophysiology; Alzheimer disease; Rett syndrome; high content analysis pipeline; hippocampal neuron; neurodegeneration therapeutics; neurodegenerative diseases; neuron activity evaluation; quantitative synaptic vesicle imaging; synaptic neuropathology; vesicle automatic detection; vesicle dynamics visualization; Frequency modulation; Image segmentation; Manuals; Neurons; Noise; Nonhomogeneous media; Pipelines; detection and quantification; high throughput study; neurodegenerative disease; neuron activity; synaptic vesicle;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signals, Systems and Computers (ASILOMAR), 2011 Conference Record of the Forty Fifth Asilomar Conference on
  • Conference_Location
    Pacific Grove, CA
  • ISSN
    1058-6393
  • Print_ISBN
    978-1-4673-0321-7
  • Type

    conf

  • DOI
    10.1109/ACSSC.2011.6190033
  • Filename
    6190033