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In JoVE (1)
Other Publications (1)
Articles by Daniel E. Miller in JoVE
Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy
Yoonsuck Choe1, David Mayerich2, Jaerock Kwon3, Daniel E. Miller1, Chul Sung1, Ji Ryang Chung1, Todd Huffman4, John Keyser1, Louise C. Abbott5
1Department of Computer Science and Engineering, Texas A&M University, 2Beckman Institute for Advanced Science and Technology, University of Illinois, 3Department of Electrical and Computer Engineering, Kettering University, 43Scan, 5Department of Veterinary Integrative Biosciences, Texas A&M University
The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.
Other articles by Daniel E. Miller on PubMed
Multiscale Exploration of Mouse Brain Microstructures Using the Knife-edge Scanning Microscope Brain Atlas
Frontiers in Neuroinformatics. 2011 | Pubmed ID: 22275895
Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions.