  Previous studies on event-related functional magnetic resonance imaging
experimental designs are primarily based on linear models, in which a known
shape of the hemodynamic response function (HRF) is assumed. However, the HRF
shape is usually uncertain at the design stage. To address this issue, we
consider a nonlinear model to accommodate a wide spectrum of feasible HRF
shapes, and propose efficient approaches for obtaining maximin and
maximin-efficient designs. Our approaches involve a reduction in the parameter
space and a search algorithm that helps to efficiently search over a restricted
class of designs for good designs. The obtained designs are compared with
traditional designs widely used in practice. We also demonstrate the usefulness
of our approaches via a motivating example.
