Title
Statistical Detection of Signals based on FMRI Data: A p-value adaptive thresholding (PAT) procedure
Speaker
Martina Pavlicova, Ohio State University
Abstract
The aim of many Functional Magnetic Resonance Imaging (FMRI) experiments is to locate regions of the brain that are activated by a specific visual, audial, or cognitive task. Voxel-wise determination of activation is a common method for locating active regions. Because the decision is made for each (of a large number of) voxels, finding an activation threshold is a multiple-comparisons problem. We propose using modifications of the Benjamini and Hochberg (1995) procedure (BH) that account for the fact that observed images are strongly spatially correlated; the proposed procedures control the expected proportion of false positives among the voxels declared to be activated. The methods, Enhanced FDR (EFDR) by Shen at al. (2002) and EFDR fused with PAT procedure, transform the map of dependent test statistics to the wavelet domain and test the activation hypotheses. EFDR enhances FDR by reducing the number of hypotheses being tested. EFDR represents spatial map of the test statistics sparsely in the wavelet domain and selects an optimal set of hypotheses to be tested using a criterion based on generalized degrees of freedom. The PAT method thresholds the wavelet coefficients using "pvalue-driven'' threshold. Transforming the non-zero wavelet coefficients back via the inverse discrete-wavelet transformation produces a final image that indicates presence of a signal and also gives an idea about its location and magnitude. To examine the effectiveness of the new procedures on FMRI data, we performed a simulation involving artificial-activation data sets, consisting of noise plus a signal componenent; the noise component was determined by statistical properties of real noise data sets from three subjects.
This is joint work with N. Cressie and T. J. Santner.
