Models and Methods for Mapping Brain Activation in Functional MRI Datasets

• Activity Number: 36
• Type: Invited
• Date/Time: Sunday, August 3, 2003 : 4:00 PM to 5:50 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Jonathan Marchini*+
• Companies: University of Oxford
• Keywords: fMRI ; spatio-temporal data ; random fields ; False Discovery Rate ; mixture models ; image analysis

Abstract

fMRI experiments allow neuroscientists to "watch" the brain function of a subject as they perform some task or receive some stimulus. These experiments produce large spatio-temporal datasets consisting of repeated measurements of neuronal activation on a 3D grid of locations throughout a subjects brain. In the analysis of each individual experiment interest lies in identifying areas that exhibit significant activation in response to the given stimulus or task. The analysis of fMRI datasets usually proceeds in two stages. First, temporal models are fitted at each brain location to assess the level of activation at each point. The product of this stage is a 3D map of statistical values. The second stage is a spatial analysis of this 3D map. In short, we wish to identify those regions of the statistical map that have been activated by the experiment. There are three main approaches to this second stage that have been proposed and discussed in the literature. These are Random Field modeling, False Discovery Rate testing, and spatial mixture modeling. We have used real and simulated datasets to compare these approaches. The talk will discuss these three approaches and suggest alteratives.