For those who may be interested in learning about current mathematical techniques applied to brain images for measuring, mapping, and modeling brain structure and function, the Institute of Pure & Applied Mathematics at UCLA will be holding a two-week workshop July 14-25. Topics will include Bayesian methods in fMRI, random field methods, multivariate methods, and connectivity models.
This two-week intensive workshop will focus on mathematical techniques applied to brain images to measure, map and model brain structure and function. Topics will range from modeling anatomical structures in MRI scans, and mapping connectivity in diffusion tensor images, to statistical analysis of functional brain images from fMRI and other imaging modalities. Current applications in radiology and neuroscience will be highlighted, as will new directions in the mathematics of structural and functional image analysis. In the second week on Functional Brain Mapping, a series of lectures on diffusion tensor imaging will discuss mathematics and tools for registration, segmentation, fiber tracking and connectivity modeling in tensor and “beyond-tensor” (high-angular resolution) diffusion images, using metrics on Riemannian manifolds. Software implementing a wide range of algorithms will be demonstrated; tutorial notes will be provided. Talks will interest newcomers as well as experts in the field. Morning lectures on the principles behind the methods; afternoon lectures will go in-depth into applications.
Michael Miller (Johns Hopkins University, Center for Imaging Science)
Thomas Nichols (University of Oxford, GlaxoSmithKline Clinical Imaging Centre )
Russell Poldrack (University of California, Los Angeles (UCLA), Psychology)
Jonathan Taylor (Stanford University, Statistics)
Paul Thompson (University of California, Los Angeles (UCLA), Laboratory of NeuroImaging)
Keith Worsley (McGill University, Department of Mathematics and Statistics)