American Statistical Association
We are interested in the general problem of detecting sparse connectivity, or high correlation, between pairs of pixels or voxels in two sets of images. To do this, we set a threshold on the correlations that controls the false positive rate, which we approximate by the expected Euler characteristic of the excursion set. The first example is a data set on 425 multiple sclerosis patients. Lesion density was measured at each voxel in white matter, and cortical thickness was measured at each point on the cortical surface. The hypothesis is that increased lesion density interrupts neuronal activity, provoking cortical thinning in those grey matter regions connected through the affected white matter regions. The second example is an fMRI experiment using the 'bubbles' task. In this experiment, the subject is asked to discriminate between images that are revealed only through a random set of small windows or 'bubbles'. We are interested in which parts of the image are used in successful discrimination, and which parts of the brain are involved in this task.
|Date:||Monday, December 10, 2007|
|Time:||12:00 to 1:30 P.M.|
Department of Statistics
1255 Amsterdam Avenue
New York, New York