BRAIN EAGER:The Virtual Neuroanatomist: Using Machine Intelligence to Study Intelligent Machines

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Cutting-edge light microscopy technology allows entire vertebrate brains to be digitized, resulting in data sets of unprecedented size and complexity. However there is a lack of adequate computational tools to visualize, manage, analyze, and disseminate these enormous data sets, and visual examination by expert human neuroanatomists remains the standard method to extract information from microscopic images. Software tools exist that can perform simple operations, but they are not able to adequately mimic the visual pattern recognition skills of an experienced neuroanatomist. This project aims to develop computational tools that mimic the analysis of an expert neuroanatomist, thus allowing for rich data analysis of whole-brain light microscopy data sets on a scale that has been previously intractable using human experts.Machine vision algorithms will be developed and integrated into an an open source software toolbox (with associated whole brain image data) that will be made widely accessible for further development and refinement. Pattern recognition methodology will be applied to combine information about brain location (e.g., "where are we in the brain") with information about correspondence of brain structures in different species (e.g., "which areas of the brain of these species correspond"). Incorporating comparative neuroanatomical knowledge into pattern-recognition methodology is radically different from the "atlas morphing" approach currently used, and has the potential to transform the study of whole-brain neuroanatomy. The tools will help fill a gap in knowledge and skills (as contemporary neuroanatomists are increasingly less frequently being trained to study whole-brain microscopic anatomy), and postdoc and PhD students will be trained in the project.