Enabling Scalable Neurocartography: Images to Graphs for Discovery

William Gray Roncal, Johns Hopkins, APL

In recent years, advances in technology have enabled researchers to ask new questions predicated on the collection and analysis of big datasets that were previously too large to study. More specifically, many fundamental questions in neuroscience require studying brain tissue at a large scale to discover emergent properties of neural computation, consciousness, and etiologies of brain disorders. A major obstacle is to construct larger, more detailed maps (e.g., structural wiring diagrams) of the brain, known as connectomes.

Although raw data exist, challenges remain in both algorithm development and scalable image analysis to enable access to the knowledge inside. This dissertation develops, combines and tests state-of-the-art algorithms to estimate graphs and glean other knowledge across the six orders of magnitude from millimeter-scale magnetic resonance imaging to nanometer-scale electron microscopy.

This work enables scientific discovery across the community and contributes to the tools and services offered by NeuroData and the Open Connectome Project. Contributions include creating, optimizing and evaluating the first known fully-automated brain graphs in electron microscopy data and magnetic resonance imaging data; pioneering approaches to generate knowledge from X-ray tomography imaging; and identifying and solving a variety of image analysis challenges associated with building graphs suitable for discovery. These methods were applied across diverse datasets to answer questions at scales not previously explored.

Speaker Biography

William Gray Roncal is a Project Manager in the Research and Exploratory Development Department at the Johns Hopkins University Applied Physics Laboratory (APL). In 2005, Will received a Master of Electrical Engineering from the University of Southern California. He earned his Bachelor of Electrical Engineering Degree from Vanderbilt University in 2003. He is a member of the Society for Neuroscience, Eta Kappa Nu, and Tau Beta Pi.

Will applies algorithms to solve big data challenges at the intersection of multiple disciplines. Although he has experience in diverse environments ranging from undersea to outer space, he currently works in connectomics, an emerging discipline within neuroscience that seeks to create a high-resolution map of the brain.