Title: Multiple comparisons in functional magnetic resonance imaging (fMRI)

In fMRI experiments, neuroscientists are able to collect huge amounts of
data as a given subject performs some cognitive task in the form of a
time series of moderate-to-high resolution 3-dimensional images. This
time series is often input into a regression model fit on a voxel-by-voxel
basis across time. This results in an image of highly structured t or F statistics,
which presents the data analyst with a large multiple comparisons problem.

In this talk, we will compare a few approaches to this multiple
comparisons problem, one using some results about the maximum of smooth
Gaussian random fields, the other using the False Discovery Rate.