A Computational Framework for Ultrastructural Mapping of Neural Circuitry

We have just published a manuscript in PLoS Biology where we describe how to build a complete and accurate neural network. This of course is one of the long standing holy grails in neuroscience. So, this effort meets two goals: 1) It meets the goals of building a complete neural connectome (we’ll be finished collecting all of the data with cell identity, physiologic response and all synaptic connectivity in approximately six days) and 2) It defines a workflow whereby investigators from around the planet can download and use the tools we are providing to build their own connectome projects using existing infrastructure. We are making those tools available here to enable other groups to assemble, browse and annotate the terabyte sized datasets required of connectome level projects.

The breakthroughs in this effort have largely centered on building a framework that can deal with ultrastructural image capture using Transmission Electron Microscopy (TEM) but greatly accelerates the data capture and image processing aspects. We combine Computational Molecular Phenotyping (CMP) to both precisely identify neurons according to their metabolomic signature along with a physiologic measure of activity, AGB. These two methodologies are combined with new approaches to TEM, enabling ultrastructural identification and reconstruction with new software created specifically for this task. This effort has been the work of our lab, the University of Utah’s Scientific Computing Institute and the University of Colorado’s Boulder Laboratory for 3-D Electron Microscopy of Cells.

We’re pretty excited as it has been picked up by dozens of science news sites around the Internet including here on Wired.

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