Empirical analyses of null-hypothesis perfusion FMRI data at 1.5 and 4 T

Functional magnetic resonance imaging (fMRI) based on arterial spin labeling (ASL) perfusion contrast is an emergent methodology for visualizing brain function both at rest and during task performance. Because of the typical pairwise subtraction approach in generating perfusion images, ASL contrast manifests different noise properties and offers potential advantages for some experimental designs as compared with blood oxygenation-level-dependent (BOLD) contrast. We studied the noise properties and statistical power of ASL contrast, with a focus on temporal autocorrelation and spatial coherence, at both 1.5- and 4.0-T field strengths. Perfusion fMRI time series were found to be roughly independent in time, and voxelwise statistical analysis assuming independence of observations yielded false-positive rates compatible with theoretical values using appropriate analysis methods. Unlike BOLD fMRI data, perfusion data were not found to have spatial coherence that varied across temporal frequency. This finding has implications for the application of spatial smoothing to perfusion data. It was also found that the spatial coherence of the ASL data is greater at high magnetic field than low field, and including the global signal as a covariate in the general linear model improves the central tendency of test statistic as well as reduces the noise level in perfusion fMRI, especially at high magnetic field.