Abstract
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.
Highlights
Connectomics aims to map the full connection matrix of the brain (Sporns et al, 2005; Sporns, 2011)
We introduced a horizontal menu bar on top of the map to include the functions that are necessary for the KESM brain atlas (KESMBA)
The first Golgi data set did not include the left frontal lobe, part of the left temporal lobe, and part of the right frontal lobe due to a misconfigured frame buffer that truncated the images, the entire brain was sectioned using the Knife-Edge Scanning Microscope (KESM)
Summary
Connectomics aims to map the full connection matrix of the brain (Sporns et al, 2005; Sporns, 2011). The fundamental assumption in connectomics is that structure defines function. To evaluate this assumption, we can consider the fact that the functional evolution of the brain has been mainly driven by that of the brain architecture and not by individual neurons (Swanson, 2003). Structure (connectivity) has been shown to greatly affect the dynamics of the network (Sporns and Tononi, 2002). Varying the delay distribution in a network was found to alter its dynamics (Thiel et al, 2003), where structural analogs of delay, e.g., connection length, could contribute to the same effect. These, taken together, indicate that obtaining the connectome can lead to a major breakthrough in understanding brain function
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