New Tools for Extracting Neuronal Phenotypes from a Volumetric Set of Cerebral Cortex Images

A major limitation in connectomics is that there are few tools to transform connectomic images into a minable database. The research aim of this project is to develop a suite of tools that extract essential structural parameters from the brain's physical structure that was imaged at very high (nanometer scale) resolution. The PIs will determine, by using automated methods, the sizes and shapes of neurons, synapses and their connectivity patterns. Using their tools, the PIs will analyze this detailed and varied dataset to find the key patterns within it. It is their belief that such automated methods are a requirement to comprehend the regularities and rules that govern the formation of neural circuits in the cerebral cortex, which to date have only been studied on very small sample spaces. The cerebral cortex remains perhaps the least understood aspect of mammalian biology. No studyof this magnitude of the neuronal phenotype space has ever been conducted: the dataset will contain hundreds of thousands of somata and a billion synapses, allowing the PIs to search for patterns that could only be guessed at with the tools used in prior research. Knowing what overarching organizational principles exist in a cerebral cortical network is crucial for understanding how brains work normally and how they may go awry in disease. Moreover, connectomic studies are beginning in a large number of different laboratories throughout the world focused on a wide range of species and parts of the brain. These tools should have direct applicability to many of these endeavors.

The PIs are a consortium of four laboratories with complementary areas of expertise in computer science (Shavit), systems biology (Alon), image processing (Pfister) and neurobiology (Lichtman). Together they are building a stacked set of methods that extract important parameters from connectomic images. These methods include neuron geometry extraction, network structure, motif detection, and archetypical pattern analysis. These approaches are based on two software platforms:the MapRecurse platform for generating connectome graphs and the Pareto Inference Engine for mining patterns within such graphs. The PIs will test these techniques on an a volume of mammalian cerebral cortex containing tens of thousands of cells and a billion synapses, with the aim of extracting the properties of neural circuits that would be difficult or impossible to obtain any other way. The work in this proposal will have significant impact on neuroscience. It speaks directly to the central goals of the White House BRAIN Initiative. It will provide neuroscientists with anumber of powerful and novel tools to understand the cells and circuits that underlie brain function. It should also be influential in developing approaches in machine learning and neuromorphic computing.