Graduate Summer School: Mathematics in Brain Imaging

July 12 - 23, 2004


This two-week intensive workshop will focus on mathematical techniques that can be applied to brain images to measure, map and model brain structure and function. Experts who are pioneers in medical image analysis will describe the mathematics used in brain imaging today. 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, EEG, and MEG. Current applications in radiology and neuroscience will be highlighted, as well as new directions in the mathematics of structural and functional image analysis. Mathematical topics covered will include computational anatomy, statistical analysis of functional images and time-series, ICA and random field theory, metric pattern theory, differential geometry, and computer vision approaches used in computational anatomy and functional imaging. Software implementing a wide range of algorithms will also be demonstrated, and tutorial notes will be provided. Talks will be of interest to newcomers and experts in the field.

Organizing Committee

Michael Miller (Johns Hopkins University)
Thomas Nichols (University of Michigan)
Stanley Osher (IPAM)
Russell Poldrack (UCLA)
Paul Thompson, Chair (UCLA)