This paper compares the statistical power of BOLD and arterial spin labeling perfusion fMRI for a variety of experimental designs within and across subjects. Based on theory and simulations, we predict that perfusion data are composed of independent observations in time under the null hypothesis, in contrast to BOLD data, which possess marked autocorrelation. We also present a method (sinc subtraction) of generating perfusion data from its raw source signal that minimizes the presence of oxygen-sensitive signal changes and can be used with any experimental design. Empirically, we demonstrate the absence of autocorrelation in perfusion noise, examine the shape of the hemodynamic response function for BOLD and perfusion, and obtain a measure of signal to noise for each method. This information is then used to generate a model of relative sensitivity of the BOLD and perfusion methods for within-subject experimental designs of varying temporal frequency. It is determined that perfusion fMRI provides superior sensitivity for within-subject experimental designs that concentrate their power at or below approximately 0.009 Hz (corresponding to a "blocked" experimental design of 60-s epochs). Additionally, evidence is presented that across-subject hypothesis tests may be more sensitive when conducted using perfusion imaging, despite the better within-subject signal to noise obtained in some cases with BOLD.
©2002 Elsevier Science (USA).