Charting out the complete brain microstructure of a mammalian species is a
grand challenge. Recent advances in serial sectioning microscopy such as the Knife-
Edge Scanning Microscopy (KESM), a high-throughput and high-resolution physical
sectioning technique, have the potential to finally address this challenge. Nevertheless,
there still are several obstacles remaining to be overcome. First, many of
these serial sectioning microscopy methods are still experimental and are not fully
automated. Second, even when the full raw data have been obtained, morphological
reconstruction, visualization/editing, statistics gathering, connectivity inference, and
network analysis remain tough problems due to the unprecedented amounts of data.
I designed a general data acquisition and analysis framework to overcome these
challenges with a focus on data from the C57BL/6 mouse brain. Since there has been
no such complete microstructure data from any mammalian species, the sheer amount of data can overwhelm researchers. To address the problems, I constructed a general
software framework for automated data acquisition and computational analysis of the
KESM data, and conducted two scientific case studies to discuss how the mouse brain
microstructure from the KESM can be utilized.
I expect the data, tools, and studies resulting from this dissertation research to
greatly contribute to computational neuroanatomy and computational neuroscience.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2009-08-7049 |
| Date | 2009 August 1900 |
| Creators | Kwon, Jae-Rock |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | application/pdf |
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