Analysis and Design of Perfusion-Based Event-Related fMRI Experiments

Abstract

Perfusion-based functional magnetic resonance imaging (fMRI) using arterial spin labeling (ASL) methods has the potential to provide better localization of the functional signal to the sites of neural activity compared to blood oxygenation level-dependent (BOLD) contrast fMRI. At present, experiments using ASL have been limited to simple block and periodic single-trial designs. We present here an adaptation of the general linear model to perfusion-based fMRI that enables the design and analysis of more complicated designs, such as random and semirandom event-related designs. Formulas for the least-squares estimate of the perfusion response and the F statistic for the detection of a response are derived. Exact expressions and useful approximations for detection power and estimation efficiency are presented, and it is shown that the trade-off between power and efficiency for perfusion experiments is similar to that previously observed for BOLD experiments. The least-squares estimate is compared with an estimate formed from the running subtraction of tag and control images. The running subtraction estimate is shown to be approximately equal to a temporally low-pass-filtered version of the least-squares estimate. Numerical simulations and results from ASL experiments are used to support the theoretical findings.