Brown CS News

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We’re happy to announce that Prof. David Laidlaw has received a grant of $100,000 in research seed funds from Brown’s Office of the Vice President of Research. The purpose of this seed funding, according to the OVPR web page, is “to help faculty develop competitive proposals for external support, principally for large-scale multi-investigator projects”; it is thus intended for research projects early in their gestation, well after they’re a gleam in the PI’s eye but before a fundable proposal about them could be written.

David’s project entails getting “pilot results in support of a multidisciplinary research project to create, validate, and apply image analysis software tools to understanding brain disorders.” This will involve the design and use of software tools that can “simultaneously segment volumetric medical imaging data to identify different types of neural tissues and to locate bundles of neural fibers in the brain. The tools will operate on combined structural and diffusion magnetic resonance imaging (MRI) datasets of the nervous system and will produce morphometric measures of each white matter structure, including its trajectory; cross section, which may vary along the trajectory; fiber density; and coherence. These metrics have the potential noninvasively to elucidate neural pathology in dozens of brain disorders as well as normal development. The tools will differ from current morphometric tools in several ways: they will be more automated; they will exploit all the complementary information available in the different magnetic resonance modalities; and they will lack many of the inaccuracies that are inherent in most current tractography methods, which operate on diffusion datasets locally.”

This project involves researchers from an impressive variety of institutions: CMU, Caltech, University of Colorado; Butler Hospital, Miriam Hospital, and Rhode Island Hospital. The work, David believes, has the potential to “position Brown as a world leader in the very young field of image-based brain metrics” and may well be applicable “not merely in basic research but also in diagnosis”, giving it very widespread impact indeed.

For more information, see <http://vis.cs.brown.edu/areas/projects/brain.html>;