Application of Enhanced FDR on fMRI Data

• Activity Number: 17
• Type: Contributed
• Date/Time: Sunday, August 11, 2002 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section*
• Author(s): Martina Pavlicova*+ and Noel Cressie and Thomas Santner
• Affiliation(s): Ohio State University and Ohio State University and Ohio State University
• Keywords: multiple-comparisons ; thresholding ; wavelets ; ROC curves

Abstract

Voxel-wise analyses are common methods for processing fMRI data. Because of the large number of hypotheses involved, finding an activation threshold is a multiple-comparisons problem. Bonferroni comparisons and cluster-based thresholding are frequently used for this purpose. We propose using a modification of a procedure that controls the proportion of false positives among the voxels declared to be activated, namely the False Discovery Rate (FDR). We transform the maps of dependent test statistics to the wavelet domain and test the activation hypotheses using a nonparametric modification of the original FDR, called Enhanced FDR (Shen et al., 2001). Transforming back via the discrete inverse wavelet transformation produces a final image that shows the location and magnitude of the signal. We use the FSL software to preprocess and form test-statistic maps, and Enhanced FDR is applied to these maps. This methodology is illustrated on an artificial fMRI data set and also on a real visual-activation data set. Finally, comparisons are made with known thresholding methods.