Statistical methods for detecting activated regions in functional MRI of the brain

Two statistical tests for detecting activated pixels in functional MRI (fMR I) data are presented. The first test (t-test) is the optimal solution to t he problem of detecting a known activation signal in Gaussian white noise. The results of this test are shown to be equivalent to the cross-correlatio n method that is widely used for activation detection in fMRI. The second t est (F test) is the optimal solution when the measured data are modeled to consist of an unknown activation signal that lies in a known lower dimensio nal subspace of the measurement space with added Gaussian white noise. A mo del for the signal subspace based on a truncated trigonometric Fourier seri es is proposed for periodic activation-baseline imaging paradigms. The adva ntage of the second method is that it does not assume any information about the shape or delay of the activation signal, except that it is periodic wi th the same period as the activation-baseline pattern. The two models are a pplied to experimental echo-planar fMRI data sets and the results are compa red. (C) 1998 Elsevier Science Inc.