Estimation and detection of event-related fMRI signals with temporally correlated noise: A statistically efficient and unbiased approach

Recent developments in analysis methods for event-related functional magnet ic resonance imaging (fMRI) has enabled a wide range of novel experimental designs. As with selective averaging methods used in event-related potentia l (ERP) research, these methods allow for the estimation of the average tim e-locked response to particular event-types, even when these events occur i n rapid succession and in an arbitrary sequence. Here we present a flexible framework for obtaining efficient and unbiased estimates of event-related hemodynamic responses, in the presence of realistic temporally correlated ( nonwhite) noise. We further present statistical inference methods based upo n the estimated responses, using restriction matrices to formulate temporal hypothesis tests about the shape of the evoked responses. The accuracy of the methods is assessed using synthetic noise, actual fMRI noise, and synth etic activation in actual noise; Actual false-positive rates were compared to nominal false-positive rates assuming white noise, as well as local and global noise estimates in the estimation procedure (assuming white noise re sulted in inappropriate inference, while both global and local estimates co rrected false-positive rates). Furthermore, both local and global noise est imates were found to increase the statistical power of the hypothesis tests , as measured by the receiver operating characteristics (ROC). This approac h thus enables appropriate univariate statistical inference with improved s tatistical power, without requiring a priori assumptions about the shape or timing of the event-related hemodynamic response. Hum. Brain Mapping 11:24 9-260, 2000. (C) 2000 Wiley-Liss, Inc.